From 629805814b2074533a2126939aa4280887056b90 Mon Sep 17 00:00:00 2001 From: Meghna Thomas <74310847+meghnathomas@users.noreply.github.com> Date: Tue, 6 Feb 2024 22:42:49 -0600 Subject: [PATCH 1/4] preliminary PRV tests PRV control structure created --- pipedream_solver/nsuperlink.py | 281 ++++++++++++++++++++++--- pipedream_solver/superlink.py | 361 +++++++++++++++++++++++++++++---- 2 files changed, 577 insertions(+), 65 deletions(-) diff --git a/pipedream_solver/nsuperlink.py b/pipedream_solver/nsuperlink.py index 6dae001..35469f5 100644 --- a/pipedream_solver/nsuperlink.py +++ b/pipedream_solver/nsuperlink.py @@ -284,14 +284,14 @@ class nSuperLink(SuperLink): def __init__(self, superlinks, superjunctions, links=None, junctions=None, transects={}, storages={}, - orifices=None, weirs=None, pumps=None, + orifices=None, weirs=None, pumps=None, prvs = None, dt=60, sparse=False, min_depth=1e-5, method='b', inertial_damping=False, bc_method='z', exit_hydraulics=False, auto_permute=False, end_length=None, end_method='b', internal_links=4, mobile_elements=False): super().__init__(superlinks, superjunctions, links, junctions, transects, storages, - orifices, weirs, pumps, dt, sparse, + orifices, weirs, pumps, prvs, dt, sparse, min_depth, method, inertial_damping, bc_method, exit_hydraulics, auto_permute, end_length, end_method, internal_links, mobile_elements) @@ -566,8 +566,9 @@ def compute_storage_areas(self): _storage_codes = self._storage_codes # Compute storage areas _h_j = np.maximum(H_j - _z_inv_j, min_depth) - numba_compute_functional_storage_areas(_h_j, _A_sj, _storage_a, _storage_b, - _storage_c, _functional) +# print(_h_j, _A_sj, _storage_a, _storage_b, +# _storage_c, _functional) + numba_compute_functional_storage_areas(_h_j, _A_sj, _storage_a, _storage_b,_storage_c, _functional) if _tabular.any(): numba_compute_tabular_storage_areas(_h_j, _A_sj, _storage_hs, _storage_As, _storage_js, _storage_codes, @@ -932,13 +933,12 @@ def orifice_flow_coefficients(self, u=None): _alpha_o = self._alpha_o # Orifice flow coefficient alpha_o _beta_o = self._beta_o # Orifice flow coefficient beta_o _chi_o = self._chi_o # Orifice flow coefficient chi_o - _unidir_o = self._unidir_o # If no input signal, assume orifice is closed if u is None: u = np.zeros(self.n_o, dtype=np.float64) # Specify orifice heads at previous timestep numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_inv_j, - _z_o, _tau_o, _Co, _Ao, _y_max_o, _unidir_o, _J_uo, _J_do) + _z_o, _tau_o, _Co, _Ao, _y_max_o, _J_uo, _J_do) # Export instance variables self._alpha_o = _alpha_o self._beta_o = _beta_o @@ -984,8 +984,8 @@ def pump_flow_coefficients(self, u=None): # Import instance variables H_j = self.H_j # Head at superjunction j _z_inv_j = self._z_inv_j # Invert elevation at superjunction j - _J_up = self._J_up # Index of superjunction upstream of pump p - _J_dp = self._J_dp # Index of superjunction downstream of pump p + _J_up = self._J_up # Index of superjunction upstream of prv + _J_dp = self._J_dp # Index of superjunction downstream of prv _z_p = self._z_p # Offset of pump inlet above upstream invert elevation _dHp_max = self._dHp_max # Maximum pump head difference _dHp_min = self._dHp_min # Minimum pump head difference @@ -998,16 +998,56 @@ def pump_flow_coefficients(self, u=None): _chi_p = self._chi_p # Pump flow coefficient chi_p # If no input signal, assume pump is closed if u is None: - u = np.zeros(self.n_p, dtype=np.float64) + u = np.ones(self.n_p, dtype=np.float64) # changed this from zeros # Check max/min head differences assert (_dHp_min <= _dHp_max).all() - # Compute pump flow coefficients - numba_pump_flow_coefficients(_alpha_p, _beta_p, _chi_p, H_j, _z_inv_j, _Qp, u, - _z_p, _dHp_max, _dHp_min, _a_p, _b_p, _c_p, _J_up, _J_dp) + # Compute prv flow coefficients + numba_pump_flow_coefficients(_alpha_p, _beta_p, _chi_p, H_j, + _z_inv_j, _Qp, u,_z_p, + _dHp_max, _dHp_min, + _a_p, _b_p, _c_p, + _J_up, + _J_dp) # Export instance variables self._alpha_p = _alpha_p self._beta_p = _beta_p self._chi_p = _chi_p + + def prv_flow_coefficients(self, u=None): + """ + Compute orifice flow coefficients: alpha_uo, beta_uo, chi_uo, + alpha_do, beta_do, chi_do. + """ + # Import instance variables + H_j = self.H_j # Head at superjunction j + _z_inv_j = self._z_inv_j # Invert elevation at superjunction j + _H_set = self._H_set + _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o + _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o + _z_prv = self._z_prv # Elevation offset of bottom of orifice o + _tau_prv = self._tau_prv # Orientation of orifice o (side/bottom) + _y_max_prv = self._y_max_prv # Maximum height of orifice o + _Qprv = self._Qprv # Current flow rate of orifice o + _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o + _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o + _Aprv = self._Aprv # Maximum flow area of orifice o + _alpha_prv = self._alpha_prv # Orifice flow coefficient alpha_o + _beta_prv = self._beta_prv # Orifice flow coefficient beta_o + _chi_prv = self._chi_prv # Orifice flow coefficient chi_o + bc = self.bc # Boundary conditions + # If no input signal, assume orifice is closed + if u is None: + u = np.zeros(self.n_o, dtype=np.float64) + # Specify orifice heads at previous timestep + numba_prv_flow_coefficients(_alpha_prv, _beta_prv, _chi_prv, H_j, _Qprv, u, _z_inv_j, _H_set, + _z_prv, _tau_prv, _Cprv_active, _Cprv_open, _Aprv, _y_max_prv, _J_uprv, _J_dprv, bc) + # Export instance variables + self._alpha_prv = _alpha_prv + self._beta_prv = _beta_prv + self._chi_prv = _chi_prv + self.bc = bc # Boundary conditions + #H_j = self.H_j # Head at superjunction j + def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, implicit=True, first_time=False): @@ -1040,6 +1080,7 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli n_o = self.n_o # Number of orifices in system n_w = self.n_w # Number of weirs in system n_p = self.n_p # Number of pumps in system + n_prv = self.n_prv # Number of pumps in system A = self.A if n_o: O = self.O @@ -1077,6 +1118,20 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli _chi_upl = self._chi_upl # Summation of pump flow coefficients _chi_dpm = self._chi_dpm # Summation of pump flow coefficients _P_diag = self._P_diag # Diagonal elements of matrix P + if n_prv: + PRV = self.PRV + _J_uprv = self._J_uprv # Index of superjunction upstream of pump p + _J_dprv = self._J_dprv # Index of superjunction downstream of pump p + _alpha_prv = self._alpha_prv # Pump flow coefficient + #print('alpha in sparse', _alpha_prv) + _beta_prv = self._beta_prv # Pump flow coefficient + _chi_prv = self._chi_prv # Pump flow coefficient + _alpha_uprvm = self._alpha_uprvm # Summation of pump flow coefficients + _beta_dprvl = self._beta_dprvl # Summation of pump flow coefficients + _chi_uprvl = self._chi_uprvl # Summation of pump flow coefficients + _chi_dprvm = self._chi_dprvm # Summation of pump flow coefficients + _PRV_diag = self._PRV_diag # Diagonal elements of matrix P + bc = self.bc # added this _sparse = self._sparse # Use sparse matrix data structures (y/n) M = self.M # Number of superjunctions in system H_j_next = self.H_j # Head at superjunction j @@ -1158,6 +1213,21 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli # Set right-hand side numba_add_at(D, _J_up, -_chi_up) numba_add_at(D, _J_dp, _chi_dp) + if n_prv: + _alpha_uprv = _alpha_prv + _alpha_dprv = _alpha_prv + _beta_uprv = _beta_prv + _beta_dprv = _beta_prv + _chi_uprv = _chi_prv + _chi_dprv = _chi_prv + _PRV_diag.fill(0) + numba_clear_off_diagonals(PRV, bc, _J_uprv, _J_dprv, n_prv) + # Set diagonal + numba_create_OWP_matrix(PRV, _PRV_diag, bc, _J_uprv, _J_dprv, _alpha_uprv, + _alpha_dprv, _beta_uprv, _beta_dprv, M, n_prv) + # Set right-hand side + numba_add_at(D, _J_uprv, -_chi_uprv) + numba_add_at(D, _J_dprv, _chi_dprv) b.fill(0) # TODO: Which A_sj? Might need to apply product rule here. b = (_A_sj * H_j_prev / _dt) + _Q_0j + D @@ -1184,19 +1254,21 @@ def solve_sparse_matrix(self, u=None, implicit=True): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps _z_inv_j = self._z_inv_j # Invert elevation of superjunction j _sparse = self._sparse # Use sparse data structures (y/n) min_depth = self.min_depth # Minimum depth at superjunctions max_depth = self.max_depth # Maximum depth at superjunctions # Does the system have control assets? - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get right-hand size if has_control: if implicit: - l = A + O + W + P + l = A + O + W + P + PRV r = b else: # TODO: Broken @@ -1227,9 +1299,11 @@ def solve_banded_matrix(self, u=None, implicit=True): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps _z_inv_j = self._z_inv_j # Invert elevation of superjunction j _sparse = self._sparse # Use sparse data structures (y/n) min_depth = self.min_depth # Minimum depth at superjunctions @@ -1237,11 +1311,11 @@ def solve_banded_matrix(self, u=None, implicit=True): bandwidth = self.bandwidth M = self.M # Does the system have control assets? - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get right-hand size if has_control: if implicit: - l = A + O + W + P + l = A + O + W + P + PRV r = b else: raise NotImplementedError @@ -1425,14 +1499,13 @@ def solve_orifice_flows(self, dt, u=None): _Co = self._Co # Discharge coefficient of orifice o _Ao = self._Ao # Maximum flow area of orifice o _V_sj = self._V_sj - _unidir_o = self._unidir_o g = 9.81 # If no input signal, assume orifice is closed if u is None: u = np.zeros(self.n_o, dtype=np.float64) # Compute orifice flows _Qo_next = numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, _tau_o, _y_max_o, _Co, _Ao, - _unidir_o, _J_uo, _J_do, g) + _J_uo, _J_do, g) # TODO: Move this inside numba function upstream_ctrl = (H_j[_J_uo] > H_j[_J_do]) _Qo_max = np.where(upstream_ctrl, _V_sj[_J_uo], _V_sj[_J_do]) / dt @@ -1490,6 +1563,41 @@ def solve_pump_flows(self, u=None): _Qp_next = numba_solve_pump_flows(H_j, u, _z_inv_j, _z_p, _dHp_max, _dHp_min, _a_p, _b_p, _c_p, _J_up, _J_dp) self._Qp = _Qp_next + + def solve_prv_flows(self, dt, u=None): + """ + Solve for pump discharges given superjunction heads at time t + dt. + """ + # Import instance variables + H_j = self.H_j # Head at superjunction j + bc = self.bc # Boundary conditions + _z_inv_j = self._z_inv_j # Invert elevation at superjunction j + _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o + _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o + _z_prv = self._z_prv # Offset of orifice above upstream invert elevation + _tau_prv = self._tau_prv # Orientation of orifice o (bottom/side) + _y_max_prv = self._y_max_prv # Maximum height of orifice o + _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o + _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o + _Aprv = self._Aprv # Maximum flow area of orifice o + _V_sj = self._V_sj + _H_set = self._H_set + g = 9.81 + # If no input signal, assume orifice is closed + if u is None: + u = np.zeros(self.n_o, dtype=np.float64) + # Compute orifice flows + # Compute pump flows + _Qprv_next = numba_solve_prv_flows(H_j, _H_set, u, _z_inv_j, _z_prv, + _tau_prv, _y_max_prv, _Cprv_open, _Cprv_active, _Aprv, _J_uprv, _J_dprv, bc, g=9.81) +# what's this!! # TODO: Move this inside numba function + upstream_ctrl = (H_j[_J_uprv] > H_j[_J_dprv]) + #_Qprv_max = np.where(upstream_ctrl, _V_sj[_J_uprv], _V_sj[_J_dprv]) / dt + #_Qprv_next = np.sign(_Qprv_next) * np.minimum(np.abs(_Qprv_next), _Qprv_max) + # Export instance variables + self._Qprv = _Qprv_next + #print(_Qprv_next) + self.bc = bc # Boundary conditions def compute_storage_volumes(self): """ @@ -2482,6 +2590,17 @@ def gamma_p(Q_p_t, b_p, c_p, u): result = safe_divide_vec(num, den) return result +@njit(float64[:](float64[:], float64[:], float64[:], float64), + cache=True) +def gamma_prv(Q_prv_t, Aprv, Cprv, g=9.81): + """ + Compute flow coefficient 'gamma' for orifice o. + """ + num = 2 * g * Cprv**2 * Aprv**2 + den = np.abs(Q_prv_t) + result = safe_divide_vec(num, den) + return result + @njit(float64[:](float64[:], float64[:], float64[:], float64), cache=True) def gamma_uk(Q_uk_t, C_uk, A_uk, g=9.81): @@ -2521,12 +2640,10 @@ def xi_dk(dx_dk, B_dk, theta_dk, dt): return result @njit(int64(float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], - float64[:], float64[:], float64[:], float64[:], float64[:], boolean[:], - int64[:], int64[:]), + float64[:], float64[:], float64[:], float64[:], float64[:], int64[:], int64[:]), cache=True) def numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_inv_j, - _z_o, _tau_o, _Co, _Ao, _y_max_o, _unidir_o, - _J_uo, _J_do): + _z_o, _tau_o, _Co, _Ao, _y_max_o, _J_uo, _J_do): g = 9.81 _H_uo = H_j[_J_uo] _H_do = H_j[_J_do] @@ -2543,7 +2660,6 @@ def numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_i _z_o + _z_inv_uo + (_tau_o * _y_max_o * u / 2)) cond_2 = (_omega_o * _H_uo + (1 - _omega_o) * _H_do > _z_o + _z_inv_uo) - cond_3 = (_H_do >= _H_uo) & _unidir_o # Fill coefficient arrays # Submerged on both sides a = (cond_0 & cond_1) @@ -2564,18 +2680,17 @@ def numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_i _chi_o[c] = (_gamma_o[c] * (-1)**(1 - _omega_o[c]) * (- _z_inv_uo[c] - _z_o[c])) # No flow - d = (~cond_0 & ~cond_2) | cond_3 + d = (~cond_0 & ~cond_2) _alpha_o[d] = 0.0 _beta_o[d] = 0.0 _chi_o[d] = 0.0 return 1 @njit(float64[:](float64[:], float64[:], float64[:], float64[:], - float64[:], float64[:], float64[:], float64[:], boolean[:], - int64[:], int64[:], float64), + float64[:], float64[:], float64[:], float64[:], int64[:], int64[:], float64), cache=True) def numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, - _tau_o, _y_max_o, _Co, _Ao, _unidir_o, _J_uo, _J_do, g=9.81): + _tau_o, _y_max_o, _Co, _Ao, _J_uo, _J_do, g=9.81): # Specify orifice heads at previous timestep _H_uo = H_j[_J_uo] _H_do = H_j[_J_do] @@ -2596,7 +2711,6 @@ def numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, _z_o + _z_inv_uo + (_tau_o * _y_max_o * u / 2)) cond_2 = (_omega_o * _H_uo + (1 - _omega_o) * _H_do > _z_o + _z_inv_uo) - cond_3 = (_H_do >= _H_uo) & _unidir_o # Fill coefficient arrays # Submerged on both sides a = (cond_0 & cond_1) @@ -2617,7 +2731,7 @@ def numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, _chi_o[c] = (_gamma_o[c] * (-1)**(1 - _omega_o[c]) * (- _z_inv_uo[c] - _z_o[c])) # No flow - d = (~cond_0 & ~cond_2) | cond_3 + d = (~cond_0 & ~cond_2) _alpha_o[d] = 0.0 _beta_o[d] = 0.0 _chi_o[d] = 0.0 @@ -2763,6 +2877,115 @@ def numba_solve_pump_flows(H_j, u, _z_inv_j, _z_p, _dHp_max, _dHp_min, _a_p, _b_ _Qp_next[~cond_0] = 0.0 return _Qp_next +@njit(int64(float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], + float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], int64[:], int64[:], boolean[:]), + cache=True) +def numba_prv_flow_coefficients(_alpha_prv, _beta_prv, _chi_prv, H_j, _Qprv, u, _z_inv_j, _H_set, + _z_prv, _tau_prv, _Cprv_active, _Cprv_open, _Aprv, _y_max_prv, _J_uprv, _J_dprv, bc): + g = 9.81 + _H_uprv = H_j[_J_uprv] + _H_dprv = H_j[_J_dprv] + _z_inv_uprv = _z_inv_j[_J_uprv] + div_term = _H_uprv / _H_set + # Create indicator functions + _omega_prv = np.zeros_like(_H_uprv) + _omega_prv[_H_uprv >= _H_dprv] = 1.0 + # Compute universal coefficients + _gamma_prv_active = gamma_o(_Qprv, _Aprv, _Cprv_active, g) # use orifice gamma + _gamma_prv_open = gamma_prv(_Qprv, _Aprv, _Cprv_open, g) + + # Create conditionals + cond_0 = (_H_uprv > _H_dprv) + cond_1 = (_H_uprv > _H_set) + + # Fill coefficient arrays + # Active + a = (cond_0 & cond_1) + _alpha_prv[a] = _gamma_prv_active[a] + _beta_prv[a] = -div_term* _gamma_prv_active[a] + _chi_prv[a] = 0.0 + #bc[_J_dprv] = True + + # Open + b = (cond_0 & ~cond_1) + _alpha_prv[b] = _gamma_prv_open[b] + _beta_prv[b] = -_gamma_prv_open[b] + _chi_prv[b] = 0.0 + #bc[_J_dprv] = False + + # No flow + c = (~cond_0) + _alpha_prv[c] = 0.0 + _beta_prv[c] = 0.0 + _chi_prv[c] = 0.0 + + #print('gamma',a,b,c, _alpha_prv, _beta_prv, _chi_prv, _H_set) + return 1 + +@njit(float64[:](float64[:], float64[:], float64[:], float64[:], float64[:], + float64[:], float64[:], float64[:], float64[:], float64[:], int64[:], int64[:], boolean[:], float64), + cache=True) +def numba_solve_prv_flows(H_j, _H_set, u, _z_inv_j, _z_prv, + _tau_prv, _y_max_prv, _Cprv_open, _Cprv_active, _Aprv, _J_uprv, _J_dprv, bc, g=9.81): + # Specify orifice heads at previous timestep + _H_uprv = H_j[_J_uprv] + _H_dprv = H_j[_J_dprv] + _z_inv_uprv = _z_inv_j[_J_uprv] + # Create indicator functions + _omega_prv = np.zeros_like(_H_uprv) + _omega_prv[_H_uprv >= _H_dprv] = 1.0 + # Create arrays to store flow coefficients for current time step + _alpha_prv = np.zeros_like(_H_uprv) + _beta_prv = np.zeros_like(_H_uprv) + _chi_prv = np.zeros_like(_H_uprv) + # Compute universal coefficients + #print('C', _Cprv_active) + _gamma_prv_active = 2 * g * _Cprv_active**2 * _Aprv**2 + _gamma_prv_open = 2 * g * _Cprv_open**2 * _Aprv**2 + # Create conditionals + cond_0 = (_H_uprv > _H_dprv) + cond_1 = (_H_uprv > _H_set) + + # Fill coefficient arrays + # Active + a = (cond_0 & cond_1) + _alpha_prv[a] = _gamma_prv_active[a] + _beta_prv[a] = -_gamma_prv_active[a] + _chi_prv[a] = 0.0 + if a: + # print('a true') + bc[_J_dprv] = False #True + + # Open + b = (cond_0 & ~cond_1) + _alpha_prv[b] = _gamma_prv_open[b] + _beta_prv[b] = -_gamma_prv_open[b] + _chi_prv[b] = 0.0 + if b: + # print('b true') + bc[_J_dprv] = False + + # No flow + c = (~cond_0) + _alpha_prv[c] = 0.0 + _beta_prv[c] = 0.0 + _chi_prv[c] = 0.0 + if c: + # print('c true') + bc[_J_dprv] = False + + # Compute flow + + #print('alpha', _alpha_prv) + #print(_gamma_prv_active, _gamma_prv_open) + _Qprv_next = np.sqrt(np.abs( + _alpha_prv * _H_uprv + _beta_prv * _H_dprv + _chi_prv)) + #print('Q', 3600*_Qprv_next) + # Export instance variables + return _Qprv_next # removed bc from the things we export + + + @njit(int64(float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], int64, int64[:], int64[:], int64[:]), cache=True) diff --git a/pipedream_solver/superlink.py b/pipedream_solver/superlink.py index 2e405a7..96f7012 100644 --- a/pipedream_solver/superlink.py +++ b/pipedream_solver/superlink.py @@ -173,7 +173,6 @@ class SuperLink(): | A | float | m^2 | Full area of orifice | | y_max | float | m | Full height of orifice | | z_o | float | m | Offset of bottom above upstream superjunction invert | - | oneway | bool | | Is the flow one-way only? (upstream to downstream) | |-------------+-------+------+------------------------------------------------------| weirs: pd.DataFrame (optional) @@ -282,7 +281,7 @@ class SuperLink(): def __init__(self, superlinks, superjunctions, links=None, junctions=None, transects={}, storages={}, - orifices=None, weirs=None, pumps=None, + orifices=None, weirs=None, pumps=None, prvs = None, dt=60, sparse=False, min_depth=1e-5, method='b', inertial_damping=False, bc_method='z', exit_hydraulics=False, auto_permute=False, @@ -315,6 +314,7 @@ def __init__(self, superlinks, superjunctions, self.orifices = orifices self.weirs = weirs self.pumps = pumps + self.prvs = prvs # Dimensions of supersystem self.M = len(superjunctions) if superlinks is not None: @@ -331,6 +331,7 @@ def __init__(self, superlinks, superjunctions, orifices = self.orifices weirs = self.weirs pumps = self.pumps + prvs = self.prvs else: self.permutations = np.arange(len(superjunctions)) self.banded = False @@ -410,8 +411,8 @@ def __init__(self, superlinks, superjunctions, self._ki = links['k'].values.astype(np.int64) self.start_nodes = self.superlinks['j_0'].values.astype(np.int64) self.end_nodes = self.superlinks['j_1'].values.astype(np.int64) - self._is_start = np.zeros(self._I.size, dtype=np.bool_) - self._is_end = np.zeros(self._I.size, dtype=np.bool_) + self._is_start = np.zeros(self._I.size, dtype=np.bool8) + self._is_end = np.zeros(self._I.size, dtype=np.bool8) self._is_start[self.start_nodes] = True self._is_end[self.end_nodes] = True self.middle_nodes = self._I[(~self._is_start) & (~self._is_end)] @@ -461,7 +462,7 @@ def __init__(self, superlinks, superjunctions, self._n_ik = links['roughness'].values.astype(np.float64) else: self._n_ik = links['n'].values.astype(np.float64) - self._ctrl = links['ctrl'].values.astype(np.bool_) + self._ctrl = links['ctrl'].values.astype(np.bool8) self._A_c_ik = links['A_c'].values.astype(np.float64) self._C_ik = links['C'].values.astype(np.float64) self._storage_type = superjunctions['storage'] @@ -522,10 +523,6 @@ def __init__(self, superlinks, superjunctions, self._g1_o = np.sqrt(self._Ao_max) self._g2_o = np.sqrt(self._Ao_max) self._g3_o = np.zeros(self.n_o) - if 'oneway' in self.orifices.columns: - self._unidir_o = self.orifices['oneway'].values.astype(np.bool_) - else: - self._unidir_o = np.zeros(self.n_o, dtype=np.bool_) self._Qo = np.zeros(self.n_o, dtype=np.float64) self._alpha_o = np.zeros(self.n_o, dtype=np.float64) self._beta_o = np.zeros(self.n_o, dtype=np.float64) @@ -631,6 +628,63 @@ def __init__(self, superlinks, superjunctions, self._beta_dpl = np.array([], dtype=np.float64) self._chi_upl = np.array([], dtype=np.float64) self._chi_dpm = np.array([], dtype=np.float64) + # Handle PRVs + if prvs is not None: + self.n_prv = self.prvs.shape[0] + self._J_uprv = self.prvs['sj_0'].values.astype(np.int64) + self._J_dprv = self.prvs['sj_1'].values.astype(np.int64) + self._Aprv_max = self.prvs['A'].values.astype(np.float64) + self._Aprv = np.copy(self._Aprv_max) + self._Cprv_open = self.prvs['C_open'].values.astype(np.float64) + self._Cprv_active = self.prvs['C_active'].values.astype(np.float64) + self._H_set = self.prvs['Hset'].values.astype(np.float64) + self._z_prv = self.prvs['z_o'].values.astype(np.float64) + self._y_max_prv = self.prvs['y_max'].values.astype(np.float64) + self._orient_prv = self.prvs['orientation'].values + self._tau_prv = (self._orient_prv == 'side').astype(np.float64) + if 'shape' in self.prvs.columns: + self._shape_prv = self.prvs['shape'] + self._g1_prv = self.prvs['g1'].values.astype(np.float64) + self._g2_prv = self.prvs['g2'].values.astype(np.float64) + self._g3_prv = self.prvs['g3'].values.astype(np.float64) + else: + self._shape_prv = pd.Series(['rect_open'] * self.n_prv) + self._g1_prv = np.sqrt(self._Aprv_max) + self._g2_prv = np.sqrt(self._Aprv_max) + self._g3_prv = np.zeros(self.n_prv) + self._Qprv = np.zeros(self.n_prv, dtype=np.float64) + self._alpha_prv = np.zeros(self.n_prv, dtype=np.float64) + self._beta_prv = np.zeros(self.n_prv, dtype=np.float64) + self._chi_prv = np.zeros(self.n_prv, dtype=np.float64) + self._alpha_uprvm = np.zeros(self.M, dtype=np.float64) + self._beta_dprvl = np.zeros(self.M, dtype=np.float64) + self._chi_uprvl = np.zeros(self.M, dtype=np.float64) + self._chi_dprvm = np.zeros(self.M, dtype=np.float64) + else: + self._J_uprv = np.array([], dtype=np.int64) + self._J_dprv = np.array([], dtype=np.int64) + self._Aprv_max = np.array([], dtype=np.float64) + self._Aprv = np.array([], dtype=np.float64) + self._Cprv_open = np.array([], dtype=np.float64) + self._Cprv_active = np.array([], dtype=np.float64) + self._H_set = np.array([], dtype=np.float64) + self._z_prv = np.array([], dtype=np.float64) + self._y_max_prv = np.array([], dtype=np.float64) + self._orient_prv = np.array([]) + self._shape_prv = pd.Series(np.array([])) + self._g1_prv = np.array([], dtype=np.float64) + self._g2_prv = np.array([], dtype=np.float64) + self._g3_prv = np.array([], dtype=np.float64) + self.n_prv = 0 + self._Qprv = np.array([], dtype=np.float64) + self._alpha_prv = np.array([], dtype=np.float64) + self._beta_prv = np.array([], dtype=np.float64) + self._chi_prv = np.array([], dtype=np.float64) + self._alpha_uprvm = np.array([], dtype=np.float64) + self._beta_dprvl = np.array([], dtype=np.float64) + self._chi_uprvl = np.array([], dtype=np.float64) + self._chi_dprvm = np.array([], dtype=np.float64) + # Enforce minimum depth self._h_Ik = np.maximum(self._h_Ik, self.min_depth) # Computational arrays @@ -647,7 +701,7 @@ def __init__(self, superlinks, superjunctions, self._E_Ik = np.zeros(self._I.size) self._D_Ik = np.zeros(self._I.size) # Forward recurrence relations - self._I_end = np.zeros(self._I.size, dtype=np.bool_) + self._I_end = np.zeros(self._I.size, dtype=np.bool8) self._I_end[self.end_nodes] = True self._I_1k = self.start_nodes self._I_2k = self.forward_I_I[self._I_1k] @@ -658,7 +712,7 @@ def __init__(self, superlinks, superjunctions, self._V_Ik = np.zeros(self._I.size) self._W_Ik = np.zeros(self._I.size) # Backward recurrence relations - self._I_start = np.zeros(self._I.size, dtype=np.bool_) + self._I_start = np.zeros(self._I.size, dtype=np.bool8) self._I_start[self.start_nodes] = True self._I_Np1k = self.end_nodes self._I_Nk = self.backward_I_I[self._I_Np1k] @@ -685,17 +739,19 @@ def __init__(self, superlinks, superjunctions, self.A = np.zeros((self.M, self.M)) self.b = np.zeros(self.M) self.D = np.zeros(self.M) - self.bc = self.superjunctions['bc'].values.astype(np.bool_) + self.bc = self.superjunctions['bc'].values.astype(np.bool8) if sparse: self.B = scipy.sparse.lil_matrix((self.M, self.n_o)) self.O = scipy.sparse.lil_matrix((self.M, self.M)) self.W = scipy.sparse.lil_matrix((self.M, self.M)) self.P = scipy.sparse.lil_matrix((self.M, self.M)) + self.PRV = scipy.sparse.lil_matrix((self.M, self.M)) else: self.B = np.zeros((self.M, self.n_o)) self.O = np.zeros((self.M, self.M)) self.W = np.zeros((self.M, self.M)) self.P = np.zeros((self.M, self.M)) + self.PRV = np.zeros((self.M, self.M)) # TODO: Should these be size NK? self._theta_uk = np.ones(self.NK) self._theta_dk = np.ones(self.NK) @@ -718,6 +774,7 @@ def __init__(self, superlinks, superjunctions, self._O_diag = np.zeros(self.M) self._W_diag = np.zeros(self.M) self._P_diag = np.zeros(self.M) + self._PRV_diag = np.zeros(self.M) # Superlink end hydraulic geometries self._A_uk = np.copy(self._A_ik[self._i_1k]) self._A_dk = np.copy(self._A_ik[self._i_nk]) @@ -737,8 +794,8 @@ def __init__(self, superlinks, superjunctions, self._S_o_uk = _S_o_uk self._S_o_dk = _S_o_dk # Boundary indexers - self._link_start = np.zeros(self._ik.size, dtype=np.bool_) - self._link_end = np.zeros(self._ik.size, dtype=np.bool_) + self._link_start = np.zeros(self._ik.size, dtype=np.bool8) + self._link_end = np.zeros(self._ik.size, dtype=np.bool8) self._link_start[self._i_1k] = True self._link_end[self._i_nk] = True # End roughness @@ -826,6 +883,14 @@ def Q_p(self): @Q_p.setter def Q_p(self, value): self._Qp = np.asarray(value) + + @property + def Q_prv(self): + return self._Qprv + + @Q_prv.setter + def Q_prv(self, value): + self._Qprv = np.asarray(value) @property def A_ik(self): @@ -932,17 +997,18 @@ def adjacency_matrix(self, J_u=None, J_d=None, symmetric=True): orifices = self.orifices weirs = self.weirs pumps = self.pumps + prvs = self.prvs # Create array of upstream and downstream indices J_u = np.concatenate([elem['sj_0'].values for elem in - (superlinks, orifices, weirs, pumps) + (superlinks, orifices, weirs, pumps, prvs) if elem is not None]) J_d = np.concatenate([elem['sj_1'].values for elem in - (superlinks, orifices, weirs, pumps) + (superlinks, orifices, weirs, pumps, prvs) if elem is not None]) At = np.zeros((M, M)) - At[J_u, J_d] = 1 + At[J_u.astype(int), J_d.astype(int)] = 1 if symmetric: - At[J_d, J_u] = 1 + At[J_d.astype(int), J_u.astype(int)] = 1 return At def _compute_bandwidth(self): @@ -969,6 +1035,7 @@ def _initialize_with_permuted_columns(self): orifices = self.orifices weirs = self.weirs pumps = self.pumps + prvs = self.prvs # Find permutation array permutations = self._to_banded() perm_inv = np.argsort(permutations) @@ -989,6 +1056,9 @@ def _initialize_with_permuted_columns(self): if pumps is not None: pumps['sj_0'] = perm_inv[pumps['sj_0'].values] pumps['sj_1'] = perm_inv[pumps['sj_1'].values] + if prvs is not None: + prvs['sj_0'] = perm_inv[prvs['sj_0'].values] + prvs['sj_1'] = perm_inv[prvs['sj_1'].values] # TODO: Remember to permute weirs/orifices/pumps too # Export instance variables self.superjunctions = superjunctions @@ -996,6 +1066,7 @@ def _initialize_with_permuted_columns(self): self.orifices = orifices self.weirs = weirs self.pumps = pumps + self.prvs = prvs self.permutations = permutations def _configure_internals_variable(self, internal_links=4, mobile_elements=True): @@ -1099,7 +1170,7 @@ def _configure_internals_variable(self, internal_links=4, mobile_elements=True): # xx[:, :] = np.vstack([np.linspace(j, i + j + k, njunctions) # for i, j, k in zip(dx_j, _dx_uk, _dx_dk)]) zz[:] = xx * _m.reshape(-1, 1) + _b0.reshape(-1, 1) - _fixed = np.ones(xx.shape, dtype=np.bool_) + _fixed = np.ones(xx.shape, dtype=np.bool8) _xc = None _zc = None c = None @@ -1589,6 +1660,15 @@ def gamma_p(self, Q_p_t, dH_p_t, a_q=1.0, a_h=1.0): den = a_h**2 * np.abs(Q_p_t) return num, den + @safe_divide + def gamma_prv(self, Q_o_t, Ao, Co=0.67, g=9.81): + """ + Compute flow coefficient 'gamma' for orifice o. + """ + num = 2 * g * Co**2 * Ao**2 + den = np.abs(Q_o_t) + return num, den + def configure_hydraulic_geometry(self): """ Prepare data structures for hydraulic geometry computations. @@ -2670,6 +2750,74 @@ def pump_flow_coefficients(self, u=None): self._alpha_p = _alpha_p self._beta_p = _beta_p self._chi_p = _chi_p + + def prv_flow_coefficients(self, u=None): + """ + Compute orifice flow coefficients: alpha_uo, beta_uo, chi_uo, + alpha_do, beta_do, chi_do. + """ + # Import instance variables + H_j = self.H_j # Head at superjunction j + _z_inv_j = self._z_inv_j # Invert elevation at superjunction j + _H_set = self._H_set + _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o + _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o + _z_prv = self._z_prv # Elevation offset of bottom of orifice o + _tau_prv = self._tau_prv # Orientation of orifice o (side/bottom) + _y_max_prv = self._y_max_prv # Maximum height of orifice o + _Qprv = self._Qprv # Current flow rate of orifice o + _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o + _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o + _Aprv = self._Aprv # Maximum flow area of orifice o + _alpha_prv = self._alpha_prv # Orifice flow coefficient alpha_o + _beta_prv = self._beta_prv # Orifice flow coefficient beta_o + _chi_prv = self._chi_prv # Orifice flow coefficient chi_o + bc = self.bc # Boundary conditions + + # If no input signal, assume orifice is closed + g = 9.81 + _H_uprv = H_j[_J_uprv] + _H_dprv = H_j[_J_dprv] + _z_inv_uprv = _z_inv_j[_J_uprv] + # Create indicator functions + _omega_prv = np.zeros_like(_H_uprv) + _omega_prv[_H_uprv >= _H_dprv] = 1.0 + # Compute universal coefficients + _gamma_prv_active = gamma_o(_Qprv, _Aprv, _Cprv_active, g) # use orifice gamma + _gamma_prv_open = gamma_prv(_Qprv, _Aprv, _Cprv_open, g) + + # Create conditionals + cond_0 = (_H_uprv > _H_dprv) + cond_1 = (_H_uprv > _H_set) + + # Fill coefficient arrays + # Active + a = (cond_0 & cond_1) + _alpha_prv[a] = _gamma_prv_active[a] + _beta_prv[a] = -_gamma_prv_active[a] + _chi_prv[a] = 0.0 + bc[_J_dprv] = True + + # Open + b = (cond_0 & ~cond_1) + _alpha_prv[b] = _gamma_prv_open[b] + _beta_prv[b] = -_gamma_prv_open[b] + _chi_prv[b] = 0.0 + bc[_J_dprv] = False + + # No flow + c = (~cond_0) + _alpha_prv[c] = 0.0 + _beta_prv[c] = 0.0 + _chi_prv[c] = 0.0 + + # Export instance variables + self._alpha_prv = _alpha_prv + self._beta_prv = _beta_prv + self._chi_prv = _chi_prv + self.bc = bc # Boundary conditions + #H_j = self.H_j # Head at superjunction j + def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, implicit=True, first_time=False): @@ -2721,10 +2869,21 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli _chi_upl = self._chi_upl # Summation of pump flow coefficients _chi_dpm = self._chi_dpm # Summation of pump flow coefficients _P_diag = self._P_diag # Diagonal elements of matrix P + _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o + _J_dprv = self._J_dprv # Index of superjunction upstream of orifice o + _alpha_prv = self._alpha_prv # Orifice flow coefficient + _beta_prv = self._beta_prv # Orifice flow coefficient + _chi_prv = self._chi_prv # Orifice flow coefficient + _alpha_uprvm = self._alpha_uprvm # Summation of orifice flow coefficients + _beta_dprvl = self._beta_dprvl # Summation of orifice flow coefficients + _chi_uprvl = self._chi_uprvl # Summation of orifice flow coefficients + _chi_dprvm = self._chi_dprvm # Summation of orifice flow coefficients + _PRV_diag = self._PRV_diag # Diagonal elements of matrix O _sparse = self._sparse # Use sparse matrix data structures (y/n) n_o = self.n_o # Number of orifices in system n_w = self.n_w # Number of weirs in system n_p = self.n_p # Number of pumps in system + n_prv = self.n_prv # Number of pumps in system M = self.M # Number of superjunctions in system H_j = self.H_j # Head at superjunction j bc = self.bc # Superjunction j has a fixed boundary condition (y/n) @@ -2865,6 +3024,41 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli np.add.at(D, i[~bc], -_chi_upl[~bc] + _chi_dpm[~bc]) else: pass + if n_prv: + bc_uprv = bc[_J_uprv] + bc_dprv = bc[_J_dprv] + if implicit: + _alpha_uprv = _alpha_prv + _alpha_dprv = _alpha_prv + _beta_uprv = _beta_prv + _beta_dprv = _beta_prv + _chi_uprv = _chi_prv + _chi_dprv = _chi_prv + _alpha_uprvm.fill(0) + _beta_dprvl.fill(0) + _chi_uprvl.fill(0) + _chi_dprvm.fill(0) + _PRV_diag.fill(0) + # Set diagonal + np.add.at(_alpha_uprvm, _J_uprv, _alpha_uprv) + np.add.at(_beta_dprvl, _J_dprv, _beta_dprv) + _PRV_diag = -_beta_dprvl + _alpha_uprvm + # Set off-diagonal + self.PRV[i[~bc], i[~bc]] = _PRV_diag[i[~bc]] + self.PRV[_J_uprv[~bc_uprv], _J_dprv[~bc_uprv]] = 0.0 + self.PRV[_J_do[~bc_dprv], _J_uprv[~bc_dprv]] = 0.0 + np.add.at(self.PRV, (_J_uprv[~bc_uprv], _J_dprv[~bc_uprv]), _beta_uprv[~bc_uprv]) + np.add.at(self.PRV, (_J_dprv[~bc_dprv], _J_uprv[~bc_dprv]), -_alpha_dprv[~bc_dprv]) + # Set right-hand side + np.add.at(_chi_uprvl, _J_uprv, _chi_uprv) + np.add.at(_chi_dprvm, _J_dprv, _chi_dprv) + np.add.at(D, i[~bc], -_chi_uprvl[~bc] + _chi_dprvm[~bc]) + else: + # TODO: Broken + # _Qo_u, _Qo_d = self.B_j(_J_uo, _J_do, _Ao, H_j) + # self.B[_J_uo[~bc_uo]] = _Qo_u[~bc_uo] + # self.B[_J_do[~bc_do]] = _Qo_d[~bc_do] + pass b.fill(0) b = (_A_sj * H_j / _dt) + _Q_0j + D # Ensure boundary condition is specified @@ -2890,19 +3084,21 @@ def solve_sparse_matrix(self, u=None, implicit=True): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps _z_inv_j = self._z_inv_j # Invert elevation of superjunction j _sparse = self._sparse # Use sparse data structures (y/n) min_depth = self.min_depth # Minimum depth at superjunctions max_depth = self.max_depth # Maximum depth at superjunctions # Does the system have control assets? - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get right-hand size if has_control: if implicit: - l = A + O + W + P + l = A + O + W + P + PRV r = b else: raise NotImplementedError @@ -2930,9 +3126,11 @@ def solve_banded_matrix(self, u=None, implicit=True): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps _z_inv_j = self._z_inv_j # Invert elevation of superjunction j _sparse = self._sparse # Use sparse data structures (y/n) min_depth = self.min_depth # Minimum depth at superjunctions @@ -2940,11 +3138,11 @@ def solve_banded_matrix(self, u=None, implicit=True): bandwidth = self.bandwidth M = self.M # Does the system have control assets? - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get right-hand size if has_control: if implicit: - l = A + O + W + P + l = A + O + W + P + PRV r = b else: raise NotImplementedError @@ -3137,6 +3335,85 @@ def solve_pump_flows(self, u=None): _Qp_next[~cond_0] = 0.0 self._Qp = _Qp_next + def solve_prv_flows(self, dt, u=None): + """ + Solve for pump discharges given superjunction heads at time t + dt. + """ + + # Import instance variables + H_j = self.H_j # Head at superjunction j + bc = self.bc # Boundary conditions + _z_inv_j = self._z_inv_j # Invert elevation at superjunction j + _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o + _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o + _z_prv = self._z_prv # Offset of orifice above upstream invert elevation + _tau_prv = self._tau_prv # Orientation of orifice o (bottom/side) + _y_max_prv = self._y_max_prv # Maximum height of orifice o + _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o + _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o + _Aprv = self._Aprv # Maximum flow area of orifice o + _V_sj = self._V_sj + _H_set = self._H_set + g = 9.81 + _Qprv = self._Qprv # Current flow rate through pump p + # If no input signal, assume orifice is closed + + # Specify orifice heads at previous timestep + _H_uprv = H_j[_J_uprv] + _H_dprv = H_j[_J_dprv] + _z_inv_uprv = _z_inv_j[_J_uprv] + if u is None: + u = np.zeros(self.n_o, dtype=np.float64) + # Compute orifice flows + + # Create indicator functions + _omega_prv = np.zeros_like(_H_uprv) + _omega_prv[_H_uprv >= _H_dprv] = 1.0 + # Create arrays to store flow coefficients for current time step + _alpha_prv = np.zeros_like(_H_uprv) + _beta_prv = np.zeros_like(_H_uprv) + _chi_prv = np.zeros_like(_H_uprv) + # Compute universal coefficients + _gamma_prv_active = 2 * g * _Cprv_active**2 * _Aprv**2 + _gamma_prv_open = 2 * g * _Cprv_open**2 * _Aprv**2 + # Create conditionals + cond_0 = (_H_uprv > _H_dprv) + cond_1 = (_H_uprv > _H_set) + + # Fill coefficient arrays + # Active + a = (cond_0 & cond_1) + _alpha_prv[a] = _gamma_prv_active[a] + _beta_prv[a] = -_gamma_prv_active[a] + _chi_prv[a] = 0.0 + bc[_J_dprv] = True + + # Open + b = (cond_0 & ~cond_1) + _alpha_prv[b] = _gamma_prv_open[b] + _beta_prv[b] = -_gamma_prv_open[b] + _chi_prv[b] = 0.0 + bc[_J_dprv] = False + + # No flow + c = (~cond_0) + _alpha_prv[c] = 0.0 + _beta_prv[c] = 0.0 + _chi_prv[c] = 0.0 + + # Compute pump flows + _Qprv_next = np.sqrt(np.abs( + _alpha_prv * _H_uprv + _beta_prv * _H_dprv + _chi_prv)) + +# what's this!! # TODO: Move this inside numba function + upstream_ctrl = (H_j[_J_uprv] > H_j[_J_dprv]) + _Qprv_max = np.where(upstream_ctrl, _V_sj[_J_uprv], _V_sj[_J_dprv]) / dt + _Qprv_next = np.sign(_Qprv_next) * np.minimum(np.abs(_Qprv_next), _Qprv_max) + # Export instance variables + self._Qprv = _Qprv_next + #print(_Qprv_next) + self.bc = bc # Boundary conditions + def solve_superlink_depths(self): """ Solve for depths at superlink ends given discharges and @@ -3815,6 +4092,7 @@ def _augmented_system(self, _Q_0j=None, _dt=None): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix D = self.D # Vector for storing chi coefficients M = self.M # Number of superjunctions in system H_j = self.H_j # Head at superjunction j @@ -3823,6 +4101,7 @@ def _augmented_system(self, _Q_0j=None, _dt=None): n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps # If no time step specified, use instance time step if _dt is None: _dt = self._dt @@ -3832,10 +4111,10 @@ def _augmented_system(self, _Q_0j=None, _dt=None): A_1 = np.zeros((M + 1, M + 1)) A_2 = np.zeros((M + 1, M + 1)) Q = np.zeros(M + 1) - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get right-hand size if has_control: - L = A + O + W + P + L = A + O + W + P + PRV else: L = A # Fill in A_1 matrix @@ -3856,6 +4135,7 @@ def _semi_implicit_system(self, _dt=None): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix b = self.b M = self.M # Number of superjunctions in system H_j = self.H_j # Head at superjunction j @@ -3864,13 +4144,14 @@ def _semi_implicit_system(self, _dt=None): n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps # If no time step specified, use instance time step if _dt is None: _dt = self._dt - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get A_1 if has_control: - A_1 = A + O + W + P + A_1 = A + O + W + P + PRV else: A_1 = A # Get A_2 @@ -3882,6 +4163,7 @@ def state_space_system(self, _dt=None): O = self.O # Orifice matrix W = self.W # Weir matrix P = self.P # Pump matrix + PRV = self.PRV # Pump matrix D = self.D M = self.M # Number of superjunctions in system H_j_next = self.H_j # Head at superjunction j @@ -3891,14 +4173,15 @@ def state_space_system(self, _dt=None): n_o = self.n_o # Number of orifices n_w = self.n_w # Number of weirs n_p = self.n_p # Number of pumps + n_prv = self.n_prv # Number of pumps Q_in = self._Q_in # If no time step specified, use instance time step if _dt is None: _dt = self._dt - has_control = n_o + n_w + n_p + has_control = n_o + n_w + n_p + n_prv # Get A_1 if has_control: - A_1 = A + O + W + P + A_1 = A + O + W + P + PRV else: A_1 = A # Get A_2 @@ -3923,6 +4206,8 @@ def save_state(self): self.states['Q_w'] = np.copy(self.Q_w) if self.n_p: self.states['Q_p'] = np.copy(self.Q_p) + if self.n_prv: + self.states['Q_prv'] = np.copy(self.Q_prv) self.states['A_ik'] = np.copy(self.A_ik) self.states['A_uk'] = np.copy(self.A_uk) self.states['A_dk'] = np.copy(self.A_dk) @@ -4014,7 +4299,7 @@ def plot_network_3d(self, ax=None, superjunction_signal=None, junction_signal=No weir_kwargs=weir_kwargs, pump_kwargs=pump_kwargs)) - def _setup_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, dt=None, + def _setup_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, u_prv = None, dt=None, first_time=False, implicit=True, banded=False, first_iter=True): if first_iter: self.save_state() @@ -4048,11 +4333,13 @@ def _setup_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p= self.weir_flow_coefficients(u=u_w) if self.pumps is not None: self.pump_flow_coefficients(u=u_p) + if self.prvs is not None: + self.prv_flow_coefficients(u=u_prv) self.sparse_matrix_equations(H_bc=H_bc, _Q_0j=Q_in, first_time=first_time, _dt=dt, implicit=implicit) - def _solve_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, dt=None, + def _solve_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, u_prv = None, dt=None, first_time=False, implicit=True, banded=False, first_iter=True): _method = self._method _exit_hydraulics = self._exit_hydraulics @@ -4067,6 +4354,8 @@ def _solve_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p= self.solve_weir_flows(u=u_w) if self.pumps is not None: self.solve_pump_flows(u=u_p) + if self.prvs is not None: + self.solve_prv_flows(dt=dt, u=u_prv) self.solve_superlink_depths() if _exit_hydraulics: self.exit_conditions() @@ -4084,7 +4373,7 @@ def _solve_step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p= self.iter_count += 1 self.t += dt - def step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, dt=None, + def step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, u_prv = None, dt=None, first_time=False, implicit=True, banded=None, first_iter=True, num_iter=1, head_tol=0.0015): """ @@ -4121,10 +4410,10 @@ def step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, d """ if banded is None: banded = self.banded - self._setup_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, dt=dt, + self._setup_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, u_prv=u_prv, dt=dt, first_time=first_time, implicit=implicit, banded=banded, first_iter=first_iter) - self._solve_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, dt=dt, + self._solve_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, u_prv=u_prv, dt=dt, first_time=first_time, implicit=implicit, banded=banded, first_iter=first_iter) # Perform fixed-point iteration until convergence @@ -4138,10 +4427,10 @@ def step(self, H_bc=None, Q_in=None, Q_0Ik=None, u_o=None, u_w=None, u_p=None, d for _ in range(num_iter): self.iter_count -= 1 self.t -= dt - self._setup_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, dt=dt, + self._setup_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, u_prv=u_prv, dt=dt, first_time=first_time, implicit=implicit, banded=banded, first_iter=False) - self._solve_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, dt=dt, + self._solve_step(H_bc=H_bc, Q_in=Q_in, Q_0Ik=Q_0Ik, u_o=u_o, u_w=u_w, u_p=u_p, u_prv=u_prv, dt=dt, first_time=first_time, implicit=implicit, banded=banded, first_iter=False) iter_elapsed += 1 From 820c32c49b251e3633e193454f1897c914c68d43 Mon Sep 17 00:00:00 2001 From: Meghna Thomas <74310847+meghnathomas@users.noreply.github.com> Date: Mon, 19 Feb 2024 13:37:50 -0600 Subject: [PATCH 2/4] final solver updates to include prvs --- pipedream_solver/nsuperlink.py | 248 ++++++++++++++++----------------- pipedream_solver/superlink.py | 139 +++++++++--------- 2 files changed, 186 insertions(+), 201 deletions(-) diff --git a/pipedream_solver/nsuperlink.py b/pipedream_solver/nsuperlink.py index 35469f5..5ecb1b8 100644 --- a/pipedream_solver/nsuperlink.py +++ b/pipedream_solver/nsuperlink.py @@ -215,6 +215,23 @@ class nSuperLink(SuperLink): | dH_max | float | m | Maximum pump head | |--------+-------+------+------------------------------------------------------| + prvs: pd.DataFrame (optional) + Table containing PRV control structures and their attributes. + The following fields are required: + + |-----------+-------+------+------------------------------------------------------| + | Field | Type | Unit | Description | + |-----------+-------+------+------------------------------------------------------| + | id | int | | Integer id for the PRV | + | name | str | | Name of the PRV | + | sj_0 | int | | Index of the upstream superjunction | + | sj_1 | int | | Index of the downstream superjunction | + | Hset | float | m | Head setting of PRV | + | C_open | float | | Discharge coefficient when PRV is open | + | C_active | float | | Discharge coefficient when PRV is active | + | A | float | m^2 | Full area of PRV opening | + |-----------+-------+------+------------------------------------------------------| + dt: float Default timestep of model (in seconds). @@ -271,6 +288,7 @@ class nSuperLink(SuperLink): Q_o : Orifice flows (m^3/s) Q_w : Weir flows (m^3/s) Q_p : Pump flows (m^3/s) + Q_prv : PRV flows (m^3/s) A_ik : Cross-sectional area of flow in links (m^2) Pe_ik : Wetted perimeter in links (m) R_ik : Hydraulic radius in links (m) @@ -566,8 +584,6 @@ def compute_storage_areas(self): _storage_codes = self._storage_codes # Compute storage areas _h_j = np.maximum(H_j - _z_inv_j, min_depth) -# print(_h_j, _A_sj, _storage_a, _storage_b, -# _storage_c, _functional) numba_compute_functional_storage_areas(_h_j, _A_sj, _storage_a, _storage_b,_storage_c, _functional) if _tabular.any(): numba_compute_tabular_storage_areas(_h_j, _A_sj, _storage_hs, _storage_As, @@ -933,12 +949,14 @@ def orifice_flow_coefficients(self, u=None): _alpha_o = self._alpha_o # Orifice flow coefficient alpha_o _beta_o = self._beta_o # Orifice flow coefficient beta_o _chi_o = self._chi_o # Orifice flow coefficient chi_o + _unidir_o = self._unidir_o # If no input signal, assume orifice is closed if u is None: u = np.zeros(self.n_o, dtype=np.float64) + # Specify orifice heads at previous timestep numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_inv_j, - _z_o, _tau_o, _Co, _Ao, _y_max_o, _J_uo, _J_do) + _z_o, _tau_o, _Co, _Ao, _y_max_o, _unidir_o, _J_uo, _J_do) # Export instance variables self._alpha_o = _alpha_o self._beta_o = _beta_o @@ -984,8 +1002,8 @@ def pump_flow_coefficients(self, u=None): # Import instance variables H_j = self.H_j # Head at superjunction j _z_inv_j = self._z_inv_j # Invert elevation at superjunction j - _J_up = self._J_up # Index of superjunction upstream of prv - _J_dp = self._J_dp # Index of superjunction downstream of prv + _J_up = self._J_up # Index of superjunction upstream of pump p + _J_dp = self._J_dp # Index of superjunction downstream of pump p _z_p = self._z_p # Offset of pump inlet above upstream invert elevation _dHp_max = self._dHp_max # Maximum pump head difference _dHp_min = self._dHp_min # Minimum pump head difference @@ -998,9 +1016,11 @@ def pump_flow_coefficients(self, u=None): _chi_p = self._chi_p # Pump flow coefficient chi_p # If no input signal, assume pump is closed if u is None: - u = np.ones(self.n_p, dtype=np.float64) # changed this from zeros + u = np.zeros(self.n_p, dtype=np.float64) + # Check max/min head differences assert (_dHp_min <= _dHp_max).all() + # Compute prv flow coefficients numba_pump_flow_coefficients(_alpha_p, _beta_p, _chi_p, H_j, _z_inv_j, _Qp, u,_z_p, @@ -1014,40 +1034,31 @@ def pump_flow_coefficients(self, u=None): self._chi_p = _chi_p def prv_flow_coefficients(self, u=None): - """ - Compute orifice flow coefficients: alpha_uo, beta_uo, chi_uo, - alpha_do, beta_do, chi_do. - """ + # """ + # Compute orifice flow coefficients: alpha_uo, beta_uo, chi_uo, + # alpha_do, beta_do, chi_do. + # """ # Import instance variables - H_j = self.H_j # Head at superjunction j - _z_inv_j = self._z_inv_j # Invert elevation at superjunction j - _H_set = self._H_set - _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o - _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o - _z_prv = self._z_prv # Elevation offset of bottom of orifice o - _tau_prv = self._tau_prv # Orientation of orifice o (side/bottom) - _y_max_prv = self._y_max_prv # Maximum height of orifice o - _Qprv = self._Qprv # Current flow rate of orifice o - _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o - _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o - _Aprv = self._Aprv # Maximum flow area of orifice o - _alpha_prv = self._alpha_prv # Orifice flow coefficient alpha_o - _beta_prv = self._beta_prv # Orifice flow coefficient beta_o - _chi_prv = self._chi_prv # Orifice flow coefficient chi_o - bc = self.bc # Boundary conditions - # If no input signal, assume orifice is closed + H_j = self.H_j # Head at superjunction j + _H_set = self._H_set # Head setting of PRV prv + _J_uprv = self._J_uprv # Index of superjunction upstream of PRV prv + _J_dprv = self._J_dprv # Index of superjunction downstream of PRV prv + _Qprv = self._Qprv # Current flow rate of PRV prv + _Cprv_open = self._Cprv_open # Discharge coefficient of open PRV prv + _Cprv_active = self._Cprv_active # Discharge coefficient of active PRV prv + _Aprv = self._Aprv # Maximum flow area of PRV prv + _alpha_prv = self._alpha_prv # PRV flow coefficient alpha_prv + _beta_prv = self._beta_prv # PRV flow coefficient beta_prv + _chi_prv = self._chi_prv # PRV flow coefficient chi_prv + # If no input signal, assume PRV is closed if u is None: - u = np.zeros(self.n_o, dtype=np.float64) - # Specify orifice heads at previous timestep - numba_prv_flow_coefficients(_alpha_prv, _beta_prv, _chi_prv, H_j, _Qprv, u, _z_inv_j, _H_set, - _z_prv, _tau_prv, _Cprv_active, _Cprv_open, _Aprv, _y_max_prv, _J_uprv, _J_dprv, bc) + u = np.ones(self.n_prv, dtype=np.float64) + numba_prv_flow_coefficients(_alpha_prv, _beta_prv, _chi_prv, H_j, _Qprv, u, _H_set, + _Cprv_active, _Cprv_open, _Aprv, _J_uprv, _J_dprv) # Export instance variables self._alpha_prv = _alpha_prv self._beta_prv = _beta_prv self._chi_prv = _chi_prv - self.bc = bc # Boundary conditions - #H_j = self.H_j # Head at superjunction j - def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, implicit=True, first_time=False): @@ -1080,7 +1091,7 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli n_o = self.n_o # Number of orifices in system n_w = self.n_w # Number of weirs in system n_p = self.n_p # Number of pumps in system - n_prv = self.n_prv # Number of pumps in system + n_prv = self.n_prv # Number of PRVs in system A = self.A if n_o: O = self.O @@ -1120,18 +1131,16 @@ def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, impli _P_diag = self._P_diag # Diagonal elements of matrix P if n_prv: PRV = self.PRV - _J_uprv = self._J_uprv # Index of superjunction upstream of pump p - _J_dprv = self._J_dprv # Index of superjunction downstream of pump p - _alpha_prv = self._alpha_prv # Pump flow coefficient - #print('alpha in sparse', _alpha_prv) - _beta_prv = self._beta_prv # Pump flow coefficient - _chi_prv = self._chi_prv # Pump flow coefficient + _J_uprv = self._J_uprv # Index of superjunction upstream of PRV prv + _J_dprv = self._J_dprv # Index of superjunction downstream of PRV prv + _alpha_prv = self._alpha_prv # PRV flow coefficient + _beta_prv = self._beta_prv # PRV flow coefficient + _chi_prv = self._chi_prv # PRV flow coefficient _alpha_uprvm = self._alpha_uprvm # Summation of pump flow coefficients - _beta_dprvl = self._beta_dprvl # Summation of pump flow coefficients - _chi_uprvl = self._chi_uprvl # Summation of pump flow coefficients - _chi_dprvm = self._chi_dprvm # Summation of pump flow coefficients - _PRV_diag = self._PRV_diag # Diagonal elements of matrix P - bc = self.bc # added this + _beta_dprvl = self._beta_dprvl # Summation of PRV flow coefficients + _chi_uprvl = self._chi_uprvl # Summation of PRV flow coefficients + _chi_dprvm = self._chi_dprvm # Summation of PRV flow coefficients + _PRV_diag = self._PRV_diag # Diagonal elements of matrix PRV _sparse = self._sparse # Use sparse matrix data structures (y/n) M = self.M # Number of superjunctions in system H_j_next = self.H_j # Head at superjunction j @@ -1499,13 +1508,14 @@ def solve_orifice_flows(self, dt, u=None): _Co = self._Co # Discharge coefficient of orifice o _Ao = self._Ao # Maximum flow area of orifice o _V_sj = self._V_sj + _unidir_o = self._unidir_o g = 9.81 # If no input signal, assume orifice is closed if u is None: u = np.zeros(self.n_o, dtype=np.float64) # Compute orifice flows _Qo_next = numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, _tau_o, _y_max_o, _Co, _Ao, - _J_uo, _J_do, g) + _unidir_o, _J_uo, _J_do, g) # TODO: Move this inside numba function upstream_ctrl = (H_j[_J_uo] > H_j[_J_do]) _Qo_max = np.where(upstream_ctrl, _V_sj[_J_uo], _V_sj[_J_do]) / dt @@ -1566,38 +1576,32 @@ def solve_pump_flows(self, u=None): def solve_prv_flows(self, dt, u=None): """ - Solve for pump discharges given superjunction heads at time t + dt. + Solve for PRV discharges given superjunction heads at time t + dt. """ # Import instance variables - H_j = self.H_j # Head at superjunction j - bc = self.bc # Boundary conditions - _z_inv_j = self._z_inv_j # Invert elevation at superjunction j - _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o - _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o - _z_prv = self._z_prv # Offset of orifice above upstream invert elevation - _tau_prv = self._tau_prv # Orientation of orifice o (bottom/side) - _y_max_prv = self._y_max_prv # Maximum height of orifice o - _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o - _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o - _Aprv = self._Aprv # Maximum flow area of orifice o - _V_sj = self._V_sj - _H_set = self._H_set + H_j = self.H_j # Head at superjunction j + _J_uprv = self._J_uprv # Index of superjunction upstream of PRV prv + _J_dprv = self._J_dprv # Index of superjunction downstream of PRV prv + _Cprv_open = self._Cprv_open # Discharge coefficient of open PRV prv + _Cprv_active = self._Cprv_active # Discharge coefficient of active PRV prv + _Aprv = self._Aprv # Maximum flow area of PRV prv + # _V_sj = self._V_sj + _H_set = self._H_set # Head setting of PRV prv g = 9.81 - # If no input signal, assume orifice is closed + _Qprv = self._Qprv # Current flow rate through PRV prv if u is None: - u = np.zeros(self.n_o, dtype=np.float64) - # Compute orifice flows - # Compute pump flows - _Qprv_next = numba_solve_prv_flows(H_j, _H_set, u, _z_inv_j, _z_prv, - _tau_prv, _y_max_prv, _Cprv_open, _Cprv_active, _Aprv, _J_uprv, _J_dprv, bc, g=9.81) -# what's this!! # TODO: Move this inside numba function + u = np.zeros(self.n_prv, dtype=np.float64) + # Compute PRV flows + # ################################################ + _Qprv_next = numba_solve_prv_flows(H_j, _H_set, u, + _Cprv_open, _Cprv_active, _Aprv, _J_uprv, _J_dprv, g=9.81) + # TODO: Move this inside numba function upstream_ctrl = (H_j[_J_uprv] > H_j[_J_dprv]) #_Qprv_max = np.where(upstream_ctrl, _V_sj[_J_uprv], _V_sj[_J_dprv]) / dt #_Qprv_next = np.sign(_Qprv_next) * np.minimum(np.abs(_Qprv_next), _Qprv_max) # Export instance variables self._Qprv = _Qprv_next - #print(_Qprv_next) - self.bc = bc # Boundary conditions + def compute_storage_volumes(self): """ @@ -2592,15 +2596,26 @@ def gamma_p(Q_p_t, b_p, c_p, u): @njit(float64[:](float64[:], float64[:], float64[:], float64), cache=True) -def gamma_prv(Q_prv_t, Aprv, Cprv, g=9.81): +def gamma_prv_us(Q_prv_t, Aprv, Cprv, g=9.81): """ - Compute flow coefficient 'gamma' for orifice o. + Compute upstream flow coefficient 'gamma' for of PRV prv. """ num = 2 * g * Cprv**2 * Aprv**2 den = np.abs(Q_prv_t) result = safe_divide_vec(num, den) return result +@njit(float64[:](float64[:], float64[:], float64[:], float64[:], float64), + cache=True) +def gamma_prv_ds(Q_prv_t, Aprv, Cprv, div_term, g=9.81): + """ + Compute downstream flow coefficient 'gamma' for of PRV prv. + """ + num = 2 * g * div_term * Cprv**2 * Aprv**2 + den = np.abs(Q_prv_t) + result = safe_divide_vec(num, den) + return result + @njit(float64[:](float64[:], float64[:], float64[:], float64), cache=True) def gamma_uk(Q_uk_t, C_uk, A_uk, g=9.81): @@ -2640,10 +2655,10 @@ def xi_dk(dx_dk, B_dk, theta_dk, dt): return result @njit(int64(float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], - float64[:], float64[:], float64[:], float64[:], float64[:], int64[:], int64[:]), + float64[:], float64[:], float64[:], float64[:], float64[:], boolean[:], int64[:], int64[:]), cache=True) def numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_inv_j, - _z_o, _tau_o, _Co, _Ao, _y_max_o, _J_uo, _J_do): + _z_o, _tau_o, _Co, _Ao, _y_max_o, _unidir_o, _J_uo, _J_do): g = 9.81 _H_uo = H_j[_J_uo] _H_do = H_j[_J_do] @@ -2660,6 +2675,7 @@ def numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_i _z_o + _z_inv_uo + (_tau_o * _y_max_o * u / 2)) cond_2 = (_omega_o * _H_uo + (1 - _omega_o) * _H_do > _z_o + _z_inv_uo) + cond_3 = (_H_do >= _H_uo) & _unidir_o # Fill coefficient arrays # Submerged on both sides a = (cond_0 & cond_1) @@ -2680,17 +2696,17 @@ def numba_orifice_flow_coefficients(_alpha_o, _beta_o, _chi_o, H_j, _Qo, u, _z_i _chi_o[c] = (_gamma_o[c] * (-1)**(1 - _omega_o[c]) * (- _z_inv_uo[c] - _z_o[c])) # No flow - d = (~cond_0 & ~cond_2) + d = (~cond_0 & ~cond_2) | cond_3 _alpha_o[d] = 0.0 _beta_o[d] = 0.0 _chi_o[d] = 0.0 return 1 @njit(float64[:](float64[:], float64[:], float64[:], float64[:], - float64[:], float64[:], float64[:], float64[:], int64[:], int64[:], float64), + float64[:], float64[:], float64[:], float64[:], boolean[:], int64[:], int64[:], float64), cache=True) def numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, - _tau_o, _y_max_o, _Co, _Ao, _J_uo, _J_do, g=9.81): + _tau_o, _y_max_o, _Co, _Ao, _unidir_o, _J_uo, _J_do, g=9.81): # Specify orifice heads at previous timestep _H_uo = H_j[_J_uo] _H_do = H_j[_J_do] @@ -2711,6 +2727,7 @@ def numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, _z_o + _z_inv_uo + (_tau_o * _y_max_o * u / 2)) cond_2 = (_omega_o * _H_uo + (1 - _omega_o) * _H_do > _z_o + _z_inv_uo) + cond_3 = (_H_do >= _H_uo) & _unidir_o # Fill coefficient arrays # Submerged on both sides a = (cond_0 & cond_1) @@ -2731,7 +2748,7 @@ def numba_solve_orifice_flows(H_j, u, _z_inv_j, _z_o, _chi_o[c] = (_gamma_o[c] * (-1)**(1 - _omega_o[c]) * (- _z_inv_uo[c] - _z_o[c])) # No flow - d = (~cond_0 & ~cond_2) + d = (~cond_0 & ~cond_2) | cond_3 _alpha_o[d] = 0.0 _beta_o[d] = 0.0 _chi_o[d] = 0.0 @@ -2877,110 +2894,79 @@ def numba_solve_pump_flows(H_j, u, _z_inv_j, _z_p, _dHp_max, _dHp_min, _a_p, _b_ _Qp_next[~cond_0] = 0.0 return _Qp_next -@njit(int64(float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], - float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], int64[:], int64[:], boolean[:]), +@njit(int64(float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], float64[:], + float64[:], float64[:], float64[:], int64[:], int64[:]) , cache=True) -def numba_prv_flow_coefficients(_alpha_prv, _beta_prv, _chi_prv, H_j, _Qprv, u, _z_inv_j, _H_set, - _z_prv, _tau_prv, _Cprv_active, _Cprv_open, _Aprv, _y_max_prv, _J_uprv, _J_dprv, bc): +def numba_prv_flow_coefficients(_alpha_prv, _beta_prv, _chi_prv, H_j, _Qprv, u, _H_set, + _Cprv_active, _Cprv_open, _Aprv, _J_uprv, _J_dprv): g = 9.81 _H_uprv = H_j[_J_uprv] _H_dprv = H_j[_J_dprv] - _z_inv_uprv = _z_inv_j[_J_uprv] div_term = _H_uprv / _H_set - # Create indicator functions - _omega_prv = np.zeros_like(_H_uprv) - _omega_prv[_H_uprv >= _H_dprv] = 1.0 # Compute universal coefficients - _gamma_prv_active = gamma_o(_Qprv, _Aprv, _Cprv_active, g) # use orifice gamma - _gamma_prv_open = gamma_prv(_Qprv, _Aprv, _Cprv_open, g) - + _gamma_prv_active_us = gamma_prv_us(_Qprv, _Aprv, _Cprv_active, g) + _gamma_prv_active_ds = gamma_prv_ds(_Qprv, _Aprv, _Cprv_active, div_term, g) + _gamma_prv_open = gamma_o(_Qprv, _Aprv, _Cprv_open, g) # Create conditionals cond_0 = (_H_uprv > _H_dprv) cond_1 = (_H_uprv > _H_set) - # Fill coefficient arrays # Active a = (cond_0 & cond_1) - _alpha_prv[a] = _gamma_prv_active[a] - _beta_prv[a] = -div_term* _gamma_prv_active[a] + _alpha_prv[a] = _gamma_prv_active_us[a] + _beta_prv[a] = - _gamma_prv_active_ds[a] _chi_prv[a] = 0.0 - #bc[_J_dprv] = True - # Open b = (cond_0 & ~cond_1) _alpha_prv[b] = _gamma_prv_open[b] _beta_prv[b] = -_gamma_prv_open[b] _chi_prv[b] = 0.0 - #bc[_J_dprv] = False - # No flow c = (~cond_0) - _alpha_prv[c] = 0.0 + _alpha_prv[c] = 0.0 _beta_prv[c] = 0.0 - _chi_prv[c] = 0.0 - - #print('gamma',a,b,c, _alpha_prv, _beta_prv, _chi_prv, _H_set) + _chi_prv[c] = 0.0 return 1 -@njit(float64[:](float64[:], float64[:], float64[:], float64[:], float64[:], - float64[:], float64[:], float64[:], float64[:], float64[:], int64[:], int64[:], boolean[:], float64), +@njit(float64[:](float64[:], float64[:], float64[:], + float64[:], float64[:], float64[:], int64[:], int64[:], float64), cache=True) -def numba_solve_prv_flows(H_j, _H_set, u, _z_inv_j, _z_prv, - _tau_prv, _y_max_prv, _Cprv_open, _Cprv_active, _Aprv, _J_uprv, _J_dprv, bc, g=9.81): +def numba_solve_prv_flows(H_j, _H_set, u, + _Cprv_open, _Cprv_active, _Aprv, _J_uprv, _J_dprv, g=9.81): # Specify orifice heads at previous timestep _H_uprv = H_j[_J_uprv] _H_dprv = H_j[_J_dprv] - _z_inv_uprv = _z_inv_j[_J_uprv] - # Create indicator functions - _omega_prv = np.zeros_like(_H_uprv) - _omega_prv[_H_uprv >= _H_dprv] = 1.0 + div_term = _H_uprv / _H_set # Create arrays to store flow coefficients for current time step _alpha_prv = np.zeros_like(_H_uprv) _beta_prv = np.zeros_like(_H_uprv) _chi_prv = np.zeros_like(_H_uprv) # Compute universal coefficients - #print('C', _Cprv_active) - _gamma_prv_active = 2 * g * _Cprv_active**2 * _Aprv**2 + _gamma_prv_active_us = 2 * g * _Cprv_active**2 * _Aprv**2 + _gamma_prv_active_ds = 2 * div_term * g * _Cprv_active**2 * _Aprv**2 _gamma_prv_open = 2 * g * _Cprv_open**2 * _Aprv**2 # Create conditionals cond_0 = (_H_uprv > _H_dprv) cond_1 = (_H_uprv > _H_set) - # Fill coefficient arrays # Active a = (cond_0 & cond_1) - _alpha_prv[a] = _gamma_prv_active[a] - _beta_prv[a] = -_gamma_prv_active[a] + _alpha_prv[a] = _gamma_prv_active_us[a] + _beta_prv[a] = - _gamma_prv_active_ds[a] _chi_prv[a] = 0.0 - if a: - # print('a true') - bc[_J_dprv] = False #True - # Open b = (cond_0 & ~cond_1) _alpha_prv[b] = _gamma_prv_open[b] _beta_prv[b] = -_gamma_prv_open[b] _chi_prv[b] = 0.0 - if b: - # print('b true') - bc[_J_dprv] = False - # No flow c = (~cond_0) _alpha_prv[c] = 0.0 _beta_prv[c] = 0.0 - _chi_prv[c] = 0.0 - if c: - # print('c true') - bc[_J_dprv] = False - + _chi_prv[c] = 0.0 # Compute flow - - #print('alpha', _alpha_prv) - #print(_gamma_prv_active, _gamma_prv_open) _Qprv_next = np.sqrt(np.abs( - _alpha_prv * _H_uprv + _beta_prv * _H_dprv + _chi_prv)) - #print('Q', 3600*_Qprv_next) + _alpha_prv * _H_uprv + _beta_prv * _H_dprv + _chi_prv)) # Export instance variables return _Qprv_next # removed bc from the things we export diff --git a/pipedream_solver/superlink.py b/pipedream_solver/superlink.py index 96f7012..4124e97 100644 --- a/pipedream_solver/superlink.py +++ b/pipedream_solver/superlink.py @@ -173,6 +173,7 @@ class SuperLink(): | A | float | m^2 | Full area of orifice | | y_max | float | m | Full height of orifice | | z_o | float | m | Offset of bottom above upstream superjunction invert | + | oneway | bool | | Is the flow one-way only? (upstream to downstream) | |-------------+-------+------+------------------------------------------------------| weirs: pd.DataFrame (optional) @@ -212,6 +213,23 @@ class SuperLink(): | dH_max | float | m | Maximum pump head | |--------+-------+------+------------------------------------------------------| + prvs: pd.DataFrame (optional) + Table containing PRV control structures and their attributes. + The following fields are required: + + |-----------+-------+------+------------------------------------------------------| + | Field | Type | Unit | Description | + |-----------+-------+------+------------------------------------------------------| + | id | int | | Integer id for the PRV | + | name | str | | Name of the PRV | + | sj_0 | int | | Index of the upstream superjunction | + | sj_1 | int | | Index of the downstream superjunction | + | Hset | float | m | Head setting of PRV | + | C_open | float | | Discharge coefficient when PRV is open | + | C_active | float | | Discharge coefficient when PRV is active | + | A | float | m^2 | Full area of PRV opening | + |-----------+-------+------+------------------------------------------------------| + dt: float Default timestep of model (in seconds). @@ -411,8 +429,8 @@ def __init__(self, superlinks, superjunctions, self._ki = links['k'].values.astype(np.int64) self.start_nodes = self.superlinks['j_0'].values.astype(np.int64) self.end_nodes = self.superlinks['j_1'].values.astype(np.int64) - self._is_start = np.zeros(self._I.size, dtype=np.bool8) - self._is_end = np.zeros(self._I.size, dtype=np.bool8) + self._is_start = np.zeros(self._I.size, dtype=np.bool_) + self._is_end = np.zeros(self._I.size, dtype=np.bool_) self._is_start[self.start_nodes] = True self._is_end[self.end_nodes] = True self.middle_nodes = self._I[(~self._is_start) & (~self._is_end)] @@ -462,7 +480,7 @@ def __init__(self, superlinks, superjunctions, self._n_ik = links['roughness'].values.astype(np.float64) else: self._n_ik = links['n'].values.astype(np.float64) - self._ctrl = links['ctrl'].values.astype(np.bool8) + self._ctrl = links['ctrl'].values.astype(np.bool_) self._A_c_ik = links['A_c'].values.astype(np.float64) self._C_ik = links['C'].values.astype(np.float64) self._storage_type = superjunctions['storage'] @@ -523,6 +541,10 @@ def __init__(self, superlinks, superjunctions, self._g1_o = np.sqrt(self._Ao_max) self._g2_o = np.sqrt(self._Ao_max) self._g3_o = np.zeros(self.n_o) + if 'oneway' in self.orifices.columns: + self._unidir_o = self.orifices['oneway'].values.astype(np.bool_) + else: + self._unidir_o = np.zeros(self.n_o, dtype=np.bool_) self._Qo = np.zeros(self.n_o, dtype=np.float64) self._alpha_o = np.zeros(self.n_o, dtype=np.float64) self._beta_o = np.zeros(self.n_o, dtype=np.float64) @@ -633,28 +655,13 @@ def __init__(self, superlinks, superjunctions, self.n_prv = self.prvs.shape[0] self._J_uprv = self.prvs['sj_0'].values.astype(np.int64) self._J_dprv = self.prvs['sj_1'].values.astype(np.int64) - self._Aprv_max = self.prvs['A'].values.astype(np.float64) - self._Aprv = np.copy(self._Aprv_max) + self._Aprv = self.prvs['A'].values.astype(np.float64) self._Cprv_open = self.prvs['C_open'].values.astype(np.float64) self._Cprv_active = self.prvs['C_active'].values.astype(np.float64) self._H_set = self.prvs['Hset'].values.astype(np.float64) - self._z_prv = self.prvs['z_o'].values.astype(np.float64) - self._y_max_prv = self.prvs['y_max'].values.astype(np.float64) - self._orient_prv = self.prvs['orientation'].values - self._tau_prv = (self._orient_prv == 'side').astype(np.float64) - if 'shape' in self.prvs.columns: - self._shape_prv = self.prvs['shape'] - self._g1_prv = self.prvs['g1'].values.astype(np.float64) - self._g2_prv = self.prvs['g2'].values.astype(np.float64) - self._g3_prv = self.prvs['g3'].values.astype(np.float64) - else: - self._shape_prv = pd.Series(['rect_open'] * self.n_prv) - self._g1_prv = np.sqrt(self._Aprv_max) - self._g2_prv = np.sqrt(self._Aprv_max) - self._g3_prv = np.zeros(self.n_prv) - self._Qprv = np.zeros(self.n_prv, dtype=np.float64) - self._alpha_prv = np.zeros(self.n_prv, dtype=np.float64) - self._beta_prv = np.zeros(self.n_prv, dtype=np.float64) + self._Qprv = np.ones(self.n_prv, dtype=np.float64) + self._alpha_prv = np.ones(self.n_prv, dtype=np.float64) + self._beta_prv = np.ones(self.n_prv, dtype=np.float64) self._chi_prv = np.zeros(self.n_prv, dtype=np.float64) self._alpha_uprvm = np.zeros(self.M, dtype=np.float64) self._beta_dprvl = np.zeros(self.M, dtype=np.float64) @@ -663,18 +670,10 @@ def __init__(self, superlinks, superjunctions, else: self._J_uprv = np.array([], dtype=np.int64) self._J_dprv = np.array([], dtype=np.int64) - self._Aprv_max = np.array([], dtype=np.float64) self._Aprv = np.array([], dtype=np.float64) self._Cprv_open = np.array([], dtype=np.float64) self._Cprv_active = np.array([], dtype=np.float64) self._H_set = np.array([], dtype=np.float64) - self._z_prv = np.array([], dtype=np.float64) - self._y_max_prv = np.array([], dtype=np.float64) - self._orient_prv = np.array([]) - self._shape_prv = pd.Series(np.array([])) - self._g1_prv = np.array([], dtype=np.float64) - self._g2_prv = np.array([], dtype=np.float64) - self._g3_prv = np.array([], dtype=np.float64) self.n_prv = 0 self._Qprv = np.array([], dtype=np.float64) self._alpha_prv = np.array([], dtype=np.float64) @@ -701,7 +700,7 @@ def __init__(self, superlinks, superjunctions, self._E_Ik = np.zeros(self._I.size) self._D_Ik = np.zeros(self._I.size) # Forward recurrence relations - self._I_end = np.zeros(self._I.size, dtype=np.bool8) + self._I_end = np.zeros(self._I.size, dtype=np.bool_) self._I_end[self.end_nodes] = True self._I_1k = self.start_nodes self._I_2k = self.forward_I_I[self._I_1k] @@ -712,7 +711,7 @@ def __init__(self, superlinks, superjunctions, self._V_Ik = np.zeros(self._I.size) self._W_Ik = np.zeros(self._I.size) # Backward recurrence relations - self._I_start = np.zeros(self._I.size, dtype=np.bool8) + self._I_start = np.zeros(self._I.size, dtype=np.bool_) self._I_start[self.start_nodes] = True self._I_Np1k = self.end_nodes self._I_Nk = self.backward_I_I[self._I_Np1k] @@ -739,7 +738,7 @@ def __init__(self, superlinks, superjunctions, self.A = np.zeros((self.M, self.M)) self.b = np.zeros(self.M) self.D = np.zeros(self.M) - self.bc = self.superjunctions['bc'].values.astype(np.bool8) + self.bc = self.superjunctions['bc'].values.astype(np.bool_) if sparse: self.B = scipy.sparse.lil_matrix((self.M, self.n_o)) self.O = scipy.sparse.lil_matrix((self.M, self.M)) @@ -794,8 +793,8 @@ def __init__(self, superlinks, superjunctions, self._S_o_uk = _S_o_uk self._S_o_dk = _S_o_dk # Boundary indexers - self._link_start = np.zeros(self._ik.size, dtype=np.bool8) - self._link_end = np.zeros(self._ik.size, dtype=np.bool8) + self._link_start = np.zeros(self._ik.size, dtype=np.bool_) + self._link_end = np.zeros(self._ik.size, dtype=np.bool_) self._link_start[self._i_1k] = True self._link_end[self._i_nk] = True # End roughness @@ -1011,6 +1010,19 @@ def adjacency_matrix(self, J_u=None, J_d=None, symmetric=True): At[J_d.astype(int), J_u.astype(int)] = 1 return At + + def safe_divide(function): + """ + Allow for division by zero. Division by zero will return zero. + """ + def inner(*args, **kwargs): + num, den = function(*args, **kwargs) + cond = (den != 0) + result = np.zeros(num.size) + result[cond] = num[cond] / den[cond] + return result + return inner + def _compute_bandwidth(self): At = self.adjacency_matrix bandwidth = 0 @@ -1170,7 +1182,7 @@ def _configure_internals_variable(self, internal_links=4, mobile_elements=True): # xx[:, :] = np.vstack([np.linspace(j, i + j + k, njunctions) # for i, j, k in zip(dx_j, _dx_uk, _dx_dk)]) zz[:] = xx * _m.reshape(-1, 1) + _b0.reshape(-1, 1) - _fixed = np.ones(xx.shape, dtype=np.bool8) + _fixed = np.ones(xx.shape, dtype=np.bool_) _xc = None _zc = None c = None @@ -1194,17 +1206,17 @@ def _configure_internals_variable(self, internal_links=4, mobile_elements=True): self._xc = _xc self._zc = _zc - def safe_divide(function): - """ - Allow for division by zero. Division by zero will return zero. - """ - def inner(*args, **kwargs): - num, den = function(*args, **kwargs) - cond = (den != 0) - result = np.zeros(num.size) - result[cond] = num[cond] / den[cond] - return result - return inner + # def safe_divide(function): + # """ + # Allow for division by zero. Division by zero will return zero. + # """ + # def inner(*args, **kwargs): + # num, den = function(*args, **kwargs) + # cond = (den != 0) + # result = np.zeros(num.size) + # result[cond] = num[cond] / den[cond] + # return result + # return inner @safe_divide def u_ik(self, Q_ik, A_ik): @@ -2758,13 +2770,9 @@ def prv_flow_coefficients(self, u=None): """ # Import instance variables H_j = self.H_j # Head at superjunction j - _z_inv_j = self._z_inv_j # Invert elevation at superjunction j _H_set = self._H_set _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o - _z_prv = self._z_prv # Elevation offset of bottom of orifice o - _tau_prv = self._tau_prv # Orientation of orifice o (side/bottom) - _y_max_prv = self._y_max_prv # Maximum height of orifice o _Qprv = self._Qprv # Current flow rate of orifice o _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o @@ -2772,18 +2780,17 @@ def prv_flow_coefficients(self, u=None): _alpha_prv = self._alpha_prv # Orifice flow coefficient alpha_o _beta_prv = self._beta_prv # Orifice flow coefficient beta_o _chi_prv = self._chi_prv # Orifice flow coefficient chi_o - bc = self.bc # Boundary conditions # If no input signal, assume orifice is closed g = 9.81 _H_uprv = H_j[_J_uprv] _H_dprv = H_j[_J_dprv] - _z_inv_uprv = _z_inv_j[_J_uprv] + div_term = _H_uprv / _H_set # Create indicator functions _omega_prv = np.zeros_like(_H_uprv) _omega_prv[_H_uprv >= _H_dprv] = 1.0 # Compute universal coefficients - _gamma_prv_active = gamma_o(_Qprv, _Aprv, _Cprv_active, g) # use orifice gamma + _gamma_prv_active = 2 * g * _Cprv_active**2 * _Aprv**2 _gamma_prv_open = gamma_prv(_Qprv, _Aprv, _Cprv_open, g) # Create conditionals @@ -2794,16 +2801,14 @@ def prv_flow_coefficients(self, u=None): # Active a = (cond_0 & cond_1) _alpha_prv[a] = _gamma_prv_active[a] - _beta_prv[a] = -_gamma_prv_active[a] + _beta_prv[a] = -div_term*_gamma_prv_active[a] _chi_prv[a] = 0.0 - bc[_J_dprv] = True # Open b = (cond_0 & ~cond_1) _alpha_prv[b] = _gamma_prv_open[b] _beta_prv[b] = -_gamma_prv_open[b] _chi_prv[b] = 0.0 - bc[_J_dprv] = False # No flow c = (~cond_0) @@ -2815,8 +2820,6 @@ def prv_flow_coefficients(self, u=None): self._alpha_prv = _alpha_prv self._beta_prv = _beta_prv self._chi_prv = _chi_prv - self.bc = bc # Boundary conditions - #H_j = self.H_j # Head at superjunction j def sparse_matrix_equations(self, H_bc=None, _Q_0j=None, u=None, _dt=None, implicit=True, @@ -3335,6 +3338,7 @@ def solve_pump_flows(self, u=None): _Qp_next[~cond_0] = 0.0 self._Qp = _Qp_next + @safe_divide def solve_prv_flows(self, dt, u=None): """ Solve for pump discharges given superjunction heads at time t + dt. @@ -3343,16 +3347,12 @@ def solve_prv_flows(self, dt, u=None): # Import instance variables H_j = self.H_j # Head at superjunction j bc = self.bc # Boundary conditions - _z_inv_j = self._z_inv_j # Invert elevation at superjunction j _J_uprv = self._J_uprv # Index of superjunction upstream of orifice o _J_dprv = self._J_dprv # Index of superjunction downstream of orifice o - _z_prv = self._z_prv # Offset of orifice above upstream invert elevation - _tau_prv = self._tau_prv # Orientation of orifice o (bottom/side) - _y_max_prv = self._y_max_prv # Maximum height of orifice o _Cprv_open = self._Cprv_open # Discharge coefficient of orifice o _Cprv_active = self._Cprv_active # Discharge coefficient of orifice o _Aprv = self._Aprv # Maximum flow area of orifice o - _V_sj = self._V_sj + # _V_sj = self._V_sj _H_set = self._H_set g = 9.81 _Qprv = self._Qprv # Current flow rate through pump p @@ -3361,9 +3361,8 @@ def solve_prv_flows(self, dt, u=None): # Specify orifice heads at previous timestep _H_uprv = H_j[_J_uprv] _H_dprv = H_j[_J_dprv] - _z_inv_uprv = _z_inv_j[_J_uprv] if u is None: - u = np.zeros(self.n_o, dtype=np.float64) + u = np.zeros(self.n_prv, dtype=np.float64) # Compute orifice flows # Create indicator functions @@ -3374,8 +3373,8 @@ def solve_prv_flows(self, dt, u=None): _beta_prv = np.zeros_like(_H_uprv) _chi_prv = np.zeros_like(_H_uprv) # Compute universal coefficients - _gamma_prv_active = 2 * g * _Cprv_active**2 * _Aprv**2 - _gamma_prv_open = 2 * g * _Cprv_open**2 * _Aprv**2 + _gamma_prv_active = safe_divide(2 * g * _Cprv_active**2 * _Aprv**2, np.abs(_Qprv)) + _gamma_prv_open = safe_divide(2 * g * _Cprv_open**2 * _Aprv**2, np.abs(_Qprv)) # Create conditionals cond_0 = (_H_uprv > _H_dprv) cond_1 = (_H_uprv > _H_set) @@ -3407,8 +3406,8 @@ def solve_prv_flows(self, dt, u=None): # what's this!! # TODO: Move this inside numba function upstream_ctrl = (H_j[_J_uprv] > H_j[_J_dprv]) - _Qprv_max = np.where(upstream_ctrl, _V_sj[_J_uprv], _V_sj[_J_dprv]) / dt - _Qprv_next = np.sign(_Qprv_next) * np.minimum(np.abs(_Qprv_next), _Qprv_max) + # _Qprv_max = np.where(upstream_ctrl, _V_sj[_J_uprv], _V_sj[_J_dprv]) / dt + # _Qprv_next = np.sign(_Qprv_next) * np.minimum(np.abs(_Qprv_next), _Qprv_max) # Export instance variables self._Qprv = _Qprv_next #print(_Qprv_next) From 24e8718acd2ae7853cbd3ec862c3cbd006f09542 Mon Sep 17 00:00:00 2001 From: Meghna Thomas <74310847+meghnathomas@users.noreply.github.com> Date: Tue, 20 Feb 2024 10:54:34 -0600 Subject: [PATCH 3/4] added Q_prv to state variables --- pipedream_solver/simulation.py | 23 ++++++----------------- 1 file changed, 6 insertions(+), 17 deletions(-) diff --git a/pipedream_solver/simulation.py b/pipedream_solver/simulation.py index a0af8b1..3780078 100644 --- a/pipedream_solver/simulation.py +++ b/pipedream_solver/simulation.py @@ -11,9 +11,9 @@ except: _HAS_NUMBA = False if _HAS_NUMBA: - from pipedream_solver.nutils import interpolate_sample, _kalman_semi_implicit, _square_root_kalman_semi_implicit + from pipedream_solver.nutils import interpolate_sample, _kalman_semi_implicit else: - from pipedream_solver.utils import interpolate_sample, _kalman_semi_implicit, _square_root_kalman_semi_implicit + from pipedream_solver.utils import interpolate_sample, _kalman_semi_implicit eps = np.finfo(float).eps @@ -86,7 +86,7 @@ class Simulation(): def __init__(self, model, Q_in=None, H_bc=None, Q_Ik=None, t_start=None, t_end=None, dt=None, max_iter=None, min_dt=1, max_dt=200, tol=0.01, min_rel_change=1e-10, max_rel_change=1e10, safety_factor=0.9, - Pxx = None, Qcov=None, Rcov=None, C=None, H=None, interpolation_method='linear'): + Qcov=None, Rcov=None, C=None, H=None, interpolation_method='linear'): self.model = model if Q_in is not None: self.Q_in = Q_in.copy(deep=True) @@ -204,12 +204,7 @@ def __init__(self, model, Q_in=None, H_bc=None, Q_Ik=None, t_start=None, else: assert isinstance(H, np.ndarray) self.H = H - if Pxx is None: - self.P_x_k_k = self.C @ self.Qcov @ self.C.T - else: - self.P_x_k_k = Pxx.copy() - self.A_1 = None - self.P_zz = None + self.P_x_k_k = self.C @ self.Qcov @ self.C.T # Progress bar checkpoints if np.isfinite(self.t_end): self._checkpoints = np.linspace(self.t_start, self.t_end) @@ -452,7 +447,7 @@ def filter_step_size(self, tol=0.5, dts=None, errs=None, coeffs=[0.5, 0.5, 0, 0. return dt_np1 def kalman_filter(self, Z, H=None, C=None, Qcov=None, Rcov=None, P_x_k_k=None, - dt=None, SR=False, **kwargs): + dt=None, **kwargs): """ Apply Kalman Filter to fuse observed data into model. @@ -486,15 +481,9 @@ def kalman_filter(self, Z, H=None, C=None, Qcov=None, Rcov=None, P_x_k_k=None, if Rcov is None: Rcov = self.Rcov A_1, A_2, b = self.model._semi_implicit_system(_dt=dt) - if SR == False: - b_hat, P_x_k_k, P_zz = _kalman_semi_implicit(Z, P_x_k_k, A_1, A_2, b, H, C, - Qcov, Rcov) - else: - b_hat, P_x_k_k, P_zz = _square_root_kalman_semi_implicit(Z, P_x_k_k, A_1, A_2, b, H, C, + b_hat, P_x_k_k = _kalman_semi_implicit(Z, P_x_k_k, A_1, A_2, b, H, C, Qcov, Rcov) self.P_x_k_k = P_x_k_k - self.P_zz = P_zz - self.A_1 = A_1 self.model.b = b_hat self.model.iter_count -= 1 self.model.t -= dt From 179a644794bfb62adfb71f0f49aed791fb64ae16 Mon Sep 17 00:00:00 2001 From: Meghna Thomas <74310847+meghnathomas@users.noreply.github.com> Date: Tue, 18 Jun 2024 13:52:40 -0500 Subject: [PATCH 4/4] tests for PRV control structure --- PRV tests/Networks/Net2 prv open.inp | 313 +++ PRV tests/Networks/PRV_active.inp | 147 ++ PRV tests/Networks/PRV_closed.inp | 147 ++ PRV tests/Networks/PRV_open.inp | 147 ++ PRV tests/Networks/headpump ky6 new.inp | 3229 +++++++++++++++++++++++ PRV tests/PRV_testing_demo.ipynb | 912 +++++++ PRV tests/pipedream_simulation.py | 198 ++ PRV tests/pipedream_utility.py | 614 +++++ 8 files changed, 5707 insertions(+) create mode 100644 PRV tests/Networks/Net2 prv open.inp create mode 100644 PRV tests/Networks/PRV_active.inp create mode 100644 PRV tests/Networks/PRV_closed.inp create mode 100644 PRV tests/Networks/PRV_open.inp create mode 100644 PRV tests/Networks/headpump ky6 new.inp create mode 100644 PRV tests/PRV_testing_demo.ipynb create mode 100644 PRV tests/pipedream_simulation.py create mode 100644 PRV tests/pipedream_utility.py diff --git a/PRV tests/Networks/Net2 prv open.inp b/PRV tests/Networks/Net2 prv open.inp new file mode 100644 index 0000000..f132135 --- /dev/null +++ b/PRV tests/Networks/Net2 prv open.inp @@ -0,0 +1,313 @@ +[TITLE] +EPANET Example Network 2 +Example of modeling a 55-hour fluoride tracer study. +Measured fluoride data is contained in the file Net2-FL.dat +and should be registered with the project to produce a +Calibration Report (select Calibration Data from the Project +menu). + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 100 8 1 ; + 3 60 14 1 ; + 4 60 8 1 ; + 5 100 8 1 ; + 6 125 5 1 ; + 7 192.80839895 4 1 ; + 8 110 9 1 ; + 9 180 14 1 ; + 10 130 5 1 ; + 11 185 34.78 1 ; + 12 210 16 1 ; + 13 210 2 1 ; + 14 200 2 1 ; + 15 190 2 1 ; + 16 150 20 1 ; + 17 180 20 1 ; + 18 100 20 1 ; + 19 150 5 1 ; + 20 170 19 1 ; + 21 150 16 1 ; + 22 200 10 1 ; + 23 230 8 1 ; + 24 190 11 1 ; + 25 230 6 1 ; + 27 130 8 1 ; + 28 110 0 1 ; + 29 110 7 1 ; + 30 130 3 1 ; + 31 190 17 1 ; + 32 110 17 1 ; + 33 180 1.5 1 ; + 34 190 1.5 1 ; + 35 110 0 1 ; + 36 110 1 1 ; + 37 192.80839895 0 1 ; + +[RESERVOIRS] +;ID Head Pattern + 1 300 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + 26 235 56.7 50 70 50 0 ; + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 2 2 5 800 12 100 0 Open ; + 3 2 3 1300 8 100 0 Open ; + 4 3 4 1200 8 100 0 Open ; + 5 4 5 1000 12 100 0 Open ; + 6 5 6 1200 12 100 0 Open ; + 7 6 37 2700 12 100 0 Open ; + 8 7 8 1200 12 140 0 Open ; + 9 7 9 400 12 100 0 Open ; + 10 8 10 1000 8 140 0 Open ; + 11 9 11 700 12 100 0 Open ; + 12 11 12 1900 12 100 0 Open ; + 13 12 13 600 12 100 0 Open ; + 14 13 14 400 12 100 0 Open ; + 15 14 15 300 12 100 0 Open ; + 16 13 16 1500 8 100 0 Open ; + 17 15 17 1500 8 100 0 Open ; + 18 16 17 600 8 100 0 Open ; + 19 17 18 700 12 100 0 Open ; + 20 18 32 350 12 100 0 Open ; + 21 16 19 1400 8 100 0 Open ; + 22 14 20 1100 12 100 0 Open ; + 23 20 21 1300 8 100 0 Open ; + 24 21 22 1300 8 100 0 Open ; + 25 20 22 1300 8 100 0 Open ; + 26 24 23 600 12 100 0 Open ; + 27 15 24 250 12 100 0 Open ; + 28 23 25 300 12 100 0 Open ; + 29 25 26 200 12 100 0 Open ; + 30 25 31 600 12 100 0 Open ; + 31 31 27 400 8 100 0 Open ; + 32 27 29 400 8 100 0 Open ; + 34 29 28 700 8 100 0 Open ; + 35 22 33 1000 8 100 0 Open ; + 36 33 34 400 8 100 0 Open ; + 37 32 19 500 8 100 0 Open ; + 38 29 35 500 8 100 0 Open ; + 39 35 30 1000 8 100 0 Open ; + 40 28 35 700 8 100 0 Open ; + 41 28 36 300 8 100 0 Open ; + 1 1 2 2400 12 100 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + 33 37 7 12 PRV 50 0 ; + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers +; + 1 1.260000 1.040000 0.970000 0.970000 0.890000 1.190000 + 1 1.280000 0.670000 0.670000 1.340000 2.460000 0.970000 + 1 0.920000 0.680000 1.430000 0.610000 0.310000 0.780000 + 1 0.370000 0.670000 1.260000 1.560000 1.190000 1.260000 + 1 0.600000 1.100000 1.030000 0.730000 0.880000 1.060000 + 1 0.990000 1.720000 1.120000 1.340000 1.120000 0.970000 + 1 1.040000 1.150000 0.910000 0.610000 0.680000 0.460000 + 1 0.510000 0.740000 1.120000 1.340000 1.260000 0.970000 + 1 0.820000 1.370000 1.030000 0.810000 0.880000 0.810000 + 1 0.810000 +; + 2 0.960000 0.960000 0.960000 0.960000 0.960000 0.960000 + 2 0.620000 0.000000 0.000000 0.000000 0.000000 0.000000 + 2 0.800000 1.000000 1.000000 1.000000 1.000000 0.150000 + 2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 + 2 0.550000 0.920000 0.920000 0.920000 0.920000 0.900000 + 2 0.900000 0.450000 0.000000 0.000000 0.000000 0.000000 + 2 0.000000 0.700000 1.000000 1.000000 1.000000 1.000000 + 2 0.200000 0.000000 0.000000 0.000000 0.000000 0.000000 + 2 0.000000 0.740000 0.920000 0.920000 0.920000 0.920000 + 2 0.920000 +; + 3 0.980000 1.020000 1.050000 0.990000 0.640000 0.460000 + 3 0.350000 0.350000 0.350000 0.350000 0.350000 0.350000 + 3 0.170000 0.170000 0.130000 0.130000 0.130000 0.150000 + 3 0.150000 0.150000 0.150000 0.150000 0.150000 0.150000 + 3 0.150000 0.120000 0.100000 0.080000 0.110000 0.090000 + 3 0.090000 0.080000 0.080000 0.080000 0.080000 0.080000 + 3 0.080000 0.090000 0.070000 0.070000 0.090000 0.090000 + 3 0.090000 0.090000 0.090000 0.090000 0.090000 0.090000 + 3 0.090000 0.080000 0.350000 0.720000 0.820000 0.920000 + 3 1.000000 + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + +[ENERGY] + Global Efficiency 75.0000 + Global Price 0.0000 + Demand Charge 0.0000 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + 2 1.0 + 3 1.0 + 4 1.0 + 5 1.0 + 6 1.0 + 7 1.0 + 8 1.0 + 9 1.0 + 10 1.0 + 11 1.0 + 12 1.0 + 13 1.0 + 14 1.0 + 15 1.0 + 16 1.0 + 17 1.0 + 18 1.0 + 19 1.0 + 20 1.0 + 21 1.0 + 22 1.0 + 23 1.0 + 24 1.0 + 25 1.0 + 27 1.0 + 28 1.0 + 29 1.0 + 30 1.0 + 31 1.0 + 32 1.0 + 33 1.0 + 34 1.0 + 35 1.0 + 36 1.0 + 26 1.0 + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0.0000 + Global Wall 0.0000 + Limiting Potential 0.0000 + Roughness Correlation 0.0000 + +[MIXING] +;Tank Model + +[TIMES] + Duration 55:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 08:00:00 AM + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units GPM + Headloss H-W + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced STOP + Pattern 1 + Demand Multiplier 1 + Emitter Exponent 0.5 + Quality Fluoride mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 19.000 20.000 +3 11.000 21.000 +4 14.000 28.000 +5 19.000 25.000 +6 28.000 23.000 +7 36.000 39.000 +8 38.000 30.000 +9 36.000 42.000 +10 37.000 23.000 +11 37.000 49.000 +12 39.000 60.000 +13 38.000 64.000 +14 38.000 66.000 +15 37.000 69.000 +16 27.000 65.000 +17 27.000 69.000 +18 23.000 68.000 +19 21.000 59.000 +20 45.000 68.000 +21 51.000 62.000 +22 54.000 69.000 +23 35.000 74.000 +24 37.000 71.000 +25 35.000 76.000 +27 39.000 87.000 +28 49.000 85.000 +29 42.000 86.000 +30 47.000 80.000 +31 37.000 80.000 +32 23.000 64.000 +33 56.000 73.000 +34 56.000 77.000 +35 43.000 81.000 +36 53.000 87.000 +37 33.653 37.612 +1 20.319 4.328 +26 33.000 76.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node +24.000 7.000 "Pump" +24.000 4.000 "Station" +26.760 77.420 "Tank" + +[BACKDROP] + DIMENSIONS 8.750 -0.150 58.250 91.150 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/PRV tests/Networks/PRV_active.inp b/PRV tests/Networks/PRV_active.inp new file mode 100644 index 0000000..063aae6 --- /dev/null +++ b/PRV tests/Networks/PRV_active.inp @@ -0,0 +1,147 @@ +[TITLE] + + +[JUNCTIONS] +;ID Elev Demand Pattern + 1 70 0 ; + 2 65 0 ; + 3 70 55 1 ; + +[RESERVOIRS] +;ID Head Pattern + Res 100 ; + 5 90 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + A Res 1 1000 12 100 0 Open ; + B 2 3 1000 12 100 0 Open ; + 4 5 3 1000 12 100 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + Valve1 1 2 12 PRV 12 0 ; + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers +; + 1 1 1.2 3 2.5 1.5 1 + 1 0.5 0.5 0.5 1.2 1.1 1 +; + 2 1 1 0.8 0.8 0.8 1 + 2 1 1 1 1 1.5 1 + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + + + +[RULES] + + + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 12:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units GPM + Headloss H-W + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +1 2870.201 5356.490 +2 4625.229 5374.771 +3 6435.101 5411.335 +Res 1297.989 5393.053 +5 7165.420 5976.725 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/PRV tests/Networks/PRV_closed.inp b/PRV tests/Networks/PRV_closed.inp new file mode 100644 index 0000000..6b91db5 --- /dev/null +++ b/PRV tests/Networks/PRV_closed.inp @@ -0,0 +1,147 @@ +[TITLE] + + +[JUNCTIONS] +;ID Elev Demand Pattern + 1 70 0 ; + 2 70 0 ; + 3 70 55 1 ; + +[RESERVOIRS] +;ID Head Pattern + Res 100 ; + 5 110 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + A Res 1 1000 12 100 0 Open ; + B 2 3 1000 12 100 0 Open ; + 4 5 3 1000 12 100 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + Valve1 1 2 12 PRV 12 0 ; + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers +; + 1 1 1.2 3 2.5 1.5 1 + 1 0.5 0.5 0.5 1.2 1.1 1 +; + 2 1 1 0.8 0.8 0.8 1 + 2 1 1 1 1 1.5 1 + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + + + +[RULES] + + + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 12:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units GPM + Headloss H-W + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +1 2870.201 5356.490 +2 4625.229 5374.771 +3 6435.101 5411.335 +Res 1297.989 5393.053 +5 7165.420 5976.725 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/PRV tests/Networks/PRV_open.inp b/PRV tests/Networks/PRV_open.inp new file mode 100644 index 0000000..763b54e --- /dev/null +++ b/PRV tests/Networks/PRV_open.inp @@ -0,0 +1,147 @@ +[TITLE] + + +[JUNCTIONS] +;ID Elev Demand Pattern + 1 20 0 ; + 2 70 0 ; + 3 70 55 1 ; + +[RESERVOIRS] +;ID Head Pattern + Res 100 ; + 5 90 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + A Res 1 1000 12 100 0 Open ; + B 2 3 1000 12 100 0 Open ; + 4 5 3 1000 12 100 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + Valve1 1 2 12 PRV 12 0 ; + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers +; + 1 1 1.2 3 2.5 1.5 1 + 1 0.5 0.5 0.5 1.2 1.1 1 +; + 2 1 1 0.8 0.8 0.8 1 + 2 1 1 1 1 1.5 1 + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + + + +[RULES] + + + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 12:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units GPM + Headloss H-W + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +1 2870.201 5356.490 +2 4625.229 5374.771 +3 6435.101 5411.335 +Res 1297.989 5393.053 +5 7165.420 5976.725 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/PRV tests/Networks/headpump ky6 new.inp b/PRV tests/Networks/headpump ky6 new.inp new file mode 100644 index 0000000..f759e55 --- /dev/null +++ b/PRV tests/Networks/headpump ky6 new.inp @@ -0,0 +1,3229 @@ +[TITLE] + + +[JUNCTIONS] +;ID Elev Demand Pattern + J-1 643.6125 2 1 ; + J-10 699.2517 0.5 1 ; + J-100 706.6241 0.9 1 ; + J-101 721.9749 1.9 1 ; + J-102 699 2.3 1 ; + J-103 700.1707 0.4 1 ; + J-104 661.9227 2 1 ; + J-105 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626.8012 0 1 ; + O-RV-1 604.3511 0 1 ; + +[RESERVOIRS] +;ID Head Pattern + R-1 492 ; + R-2 628.96 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + T-1 762.5445 132.4555 117.4555 142.4555 58 0 ; + T-2 740.2293 149.7707 144.7707 164.7707 65 0 ; + T-3 825.6524 74.3476 64.3476 99.3476 31 0 ; + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + P-1 J-1 J-236 18.049 4 150 0 Open ; + P-10 J-18 J-290 5420.06 16 150 0 Open ; + P-100 J-168 J-169 582.09 6 150 0 Open ; + P-101 J-170 J-171 249.399 6 150 0 Open ; + P-102 J-172 J-143 553.32 6 150 0 Open ; + P-103 J-142 J-177 10.64 6 150 0 Open ; + P-104 J-174 J-71 143.649 6 150 0 Open ; + P-105 J-151 J-174 47.88 6 150 0 Open ; + P-106 J-173 J-176 228 6 150 0 Open ; + P-107 J-177 J-178 515.159 6 150 0 Open ; + P-108 J-179 J-180 249.339 6 150 0 Open ; + P-109 J-181 J-14 419.91 6 150 0 Open ; + P-11 J-20 J-305 6970.029 10 150 0 Open ; + P-110 J-173 J-182 144.649 6 150 0 Open ; + P-111 J-183 J-92 101.989 6 150 0 Open ; + P-112 J-141 J-184 433.269 6 150 0 Open ; + P-113 J-185 J-15 18.5 2 150 0 Open ; + P-114 J-155 J-53 151.75 2 150 0 Open ; + P-115 J-132 J-126 250.58 6 150 0 Open ; + P-116 J-189 J-190 365.739 2 150 0 Open ; + P-117 J-191 J-116 498.359 0.75 150 0 Open ; + P-118 J-192 J-193 2203.899 6 150 0 Open ; + P-119 J-149 J-194 276.26 6 150 0 Open ; + P-12 J-12 J-474 2874.62 12 150 0 Open ; + P-120 J-195 J-149 225.25 6 150 0 Open ; + P-121 J-196 J-197 148.369 8 150 0 Open ; + P-122 J-198 J-199 702.789 2 150 0 Open ; + P-123 J-200 J-3 878.869 12 150 0 Open ; + P-124 J-202 J-203 232.96 6 150 0 Open ; + P-125 J-204 J-205 552.09 6 150 0 Open ; + P-126 J-179 J-206 447.619 6 150 0 Open ; + P-127 J-207 J-27 32.169 6 150 0 Open ; + P-128 J-209 J-210 330.829 6 150 0 Open ; + P-129 J-210 J-168 991.75 6 150 0 Open ; + P-13 J-13 J-159 36.5 4 150 0 Open ; + P-130 J-211 J-181 581.799 6 150 0 Open ; + P-131 J-48 J-212 286.54 6 150 0 Open ; + P-132 J-213 J-130 80.76 2 150 0 Open ; + P-133 J-215 J-240 247.729 6 150 0 Open ; + P-134 J-216 J-217 340.5 2 150 0 Open ; + P-135 J-218 J-219 194 6 150 0 Open ; + P-136 J-220 J-221 136.139 2 150 0 Open ; + P-137 J-222 J-136 1365.209 8 150 0 Open ; + P-138 J-223 J-199 16.18 6 150 0 Open ; + P-139 J-205 J-225 364.619 6 150 0 Open ; + P-14 J-54 J-2 1168.119 6 150 0 Open ; + P-140 J-226 J-487 16.04 6 150 0 Open ; + P-141 J-228 J-229 500.19 6 150 0 Open ; + P-142 J-230 J-190 47.959 2 150 0 Open ; + P-143 J-5 J-415 1393.469 12 150 0 Open ; + P-144 J-105 J-231 5091.5 10 150 0 Open ; + P-145 J-231 J-232 129.61 10 150 0 Open ; + P-146 J-233 J-132 266.769 6 150 0 Open ; + P-147 J-234 J-217 515.07 2 150 0 Open ; + P-148 J-235 J-2 391.2 6 150 0 Open ; + P-149 J-232 J-235 16.27 10 150 0 Open ; + P-15 J-486 J-15 33.34 6 150 0 Open ; + P-150 J-237 J-16 31.159 2 150 0 Open ; + P-151 J-239 J-50 129.07 6 150 0 Open ; + P-152 J-241 J-229 490.97 6 150 0 Open ; + P-153 J-242 J-243 370.98 2 150 0 Open ; + P-154 J-534 J-533 130.77 10 150 0 Open ; + P-155 J-246 J-222 753.76 6 150 0 Open ; + P-156 J-228 J-247 879.429 6 150 0 Open ; + P-157 J-203 J-248 993.119 6 150 0 Open ; + P-158 J-249 J-250 343.269 6 150 0 Open ; + P-159 J-251 J-133 40.509 6 150 0 Open ; + P-16 J-15 J-186 990.69 2 150 0 Open ; + P-160 J-253 J-254 237.449 6 150 0 Open ; + P-161 J-255 J-219 482.829 2 150 0 Open ; + P-162 J-210 J-256 406.019 6 150 0 Open ; + P-163 J-218 J-242 484.39 2 150 0 Open ; + P-164 J-257 J-258 136.71 2 150 0 Open ; + P-165 J-259 J-260 541.659 10 150 0 Open ; + P-166 J-261 J-39 2952.79 8 150 0 Open ; + P-167 J-262 J-263 923.659 6 150 0 Open ; + P-168 J-40 J-264 249.089 1 150 0 Open ; + P-169 J-265 J-266 1063.199 4 150 0 Open ; + P-17 J-13 J-467 753.669 10 150 0 Open ; + P-170 J-267 J-268 223.94 1 150 0 Open ; + P-171 J-269 J-30c 1320.4 6 150 0 Open ; + P-172 J-271 J-272 1315.4 6 150 0 Open ; + P-173 J-273 J-86 653.679 6 150 0 Open ; + P-174 J-274 J-30a 603.2 1 150 0 Open ; + P-175 J-276 J-277 447.619 10 150 0 Open ; + P-176 J-205 J-278 163.259 6 150 0 Open ; + P-177 J-280 J-30c 271.399 6 150 0 Open ; + P-178 J-281 J-282 430.149 4 150 0 Open ; + P-179 J-283 J-270 281.29 6 150 0 Open ; + P-18 J-32 J-493 534.96 10 150 0 Open ; + P-180 J-225 J-285 179.46 6 150 0 Open ; + P-181 J-268 J-286 490.91 6 150 0 Open ; + P-182 J-112 J-110 91.08 6 150 0 Open ; + P-183 J-288 J-128 712.969 2 150 0 Open ; + P-184 J-289 J-65 952.83 3 150 0 Open ; + P-185 J-84 J-289 410.709 4 150 0 Open ; + P-186 J-292 J-65 389.579 2 150 0 Open ; + P-187 J-143 J-179 105.949 6 150 0 Open ; + P-188 J-293 J-294 749.52 2 150 0 Open ; + P-189 J-295 J-296 87.559 6 150 0 Open ; + P-19 J-34 J-539 546 12 150 0 Open ; + P-190 J-297 J-298 243.119 6 150 0 Open ; + P-191 J-299 J-257 106.949 6 150 0 Open ; + P-192 J-301 J-302 950.53 6 150 0 Open ; + P-193 J-80 J-114 855.789 16 150 0 Open ; + P-194 J-303 J-304 576.95 6 150 0 Open ; + P-195 J-21 J-279 1016.109 10 150 0 Open ; + P-196 J-306 J-301 250.32 0.75 150 0 Open ; + P-197 J-80 J-307 464.679 6 150 0 Open ; + P-198 J-108 J-81 672.39 2 150 0 Open ; + P-199 J-308 J-365 44.36 1 150 0 Open ; + P-2 J-3 J-102 342.429 3 100 0 Open ; + P-20 J-36 J-37 496.019 6 150 0 Open ; + P-200 J-310 J-378 137.059 6 150 0 Open ; + P-201 J-81 J-279d 795.109 1 150 0 Open ; + P-202 J-312 J-306 491.2 6 150 0 Open ; + P-203 J-90 J-51 48.709 0.75 150 0 Open ; + P-204 J-287 J-56 1317.089 3 150 0 Open ; + P-205 J-314 J-315 86.18 4 150 0 Open ; + P-206 J-58 J-316 34.18 6 150 0 Open ; + P-207 J-136 J-317 761.309 8 150 0 Open ; + P-208 J-288 J-286 242.69 6 150 0 Open ; + P-209 J-284 J-262 554.929 6 150 0 Open ; + P-21 J-38 J-39 2019.03 2 150 0 Open ; + P-210 J-319 J-200 350.85 12 150 0 Open ; + P-211 J-97 J-329 27.139 6 150 0 Open ; + P-212 J-252 J-254 109.37 6 150 0 Open ; + P-213 J-148 J-332 43.139 6 150 0 Open ; + P-214 J-320 J-25 227.199 2 150 0 Open ; + P-215 J-114 T-2 326.17 16 150 0 Open ; + P-216 J-323 J-324 208.55 2 150 0 Open ; + P-217 J-325 J-326 487.01 1 150 0 Open ; + P-218 J-327 J-250 36.319 16 150 0 Open ; + P-219 J-306 J-329 453.66 6 150 0 Open ; + P-22 J-40 J-41 319.57 1 150 0 Open ; + P-220 J-330 J-281 938.69 10 150 0 Open ; + P-221 J-91 J-308 403.48 1 150 0 Open ; + P-222 J-37 J-332 445.73 6 150 0 Open ; + P-223 J-333 J-301 491.88 0.75 150 0 Open ; + P-224 J-334 J-335 171.24 2 150 0 Open ; + P-225 J-272 J-535 651.69 6 150 0 Open ; + P-226 J-337 J-276 387.88 10 150 0 Open ; + P-227 J-338 J-292 585.01 3 150 0 Open ; + P-228 J-57 J-342 25.579 6 150 0 Open ; + P-229 J-317 J-63 120.69 8 150 0 Open ; + P-23 J-42 J-47 39.59 6 150 0 Open ; + P-230 J-340 J-296 29.989 6 150 0 Open ; + P-231 J-326 J-154 109.51 2 150 0 Open ; + P-232 J-30 J-397 1147.089 16 150 0 Open ; + P-233 J-264 J-342 209.149 0.75 150 0 Open ; + P-234 J-343 J-133 309.97 6 150 0 Open ; + P-235 J-344 J-537 438.51 10 150 0 Open ; + P-236 J-18 J-279 1037.53 16 150 0 Open ; + P-237 J-345 J-346 878.619 6 150 0 Open ; + P-238 J-117 J-347 275.38 6 150 0 Open ; + P-239 J-348 J-40 352.299 3 150 0 Open ; + P-24 J-44 J-85 598.82 4 150 0 Open ; + P-240 J-331 J-20 7386.319 10 150 0 Open ; + P-241 J-268 J-349 245.669 1 150 0 Open ; + P-242 J-350 J-351 188.3 6 150 0 Open ; + P-243 J-352 J-271 1096.52 6 150 0 Open ; + P-244 J-353 J-239 1059.849 2 150 0 Open ; + P-245 J-354 J-350 362.75 0.75 150 0 Open ; + P-246 J-355 J-203 306.72 6 150 0 Open ; + P-247 J-356 J-279a 26.86 6 150 0 Open ; + P-248 J-96 J-109 699.469 8 150 0 Open ; + P-249 J-323 J-95 15.689 2 150 0 Open ; + P-25 J-46 J-47 419.92 2 150 0 Open ; + P-250 J-318 J-352 543.359 6 150 0 Open ; + P-251 J-301 J-37 265.79 6 150 0 Open ; + P-252 J-155 J-357 252.679 1 150 0 Open ; + P-253 J-358 J-293 35.86 6 150 0 Open ; + P-254 J-360 J-271 500.359 6 150 0 Open ; + P-255 J-361 J-379 27.04 10 150 0 Open ; + P-256 J-363 J-358 68.26 6 150 0 Open ; + P-257 J-364 J-279a 711.5 6 150 0 Open ; + P-258 J-357 J-528 288.269 1 150 0 Open ; + P-259 J-294 J-334 304.72 6 150 0 Open ; + P-26 J-48 J-69 410.899 6 150 0 Open ; + P-260 J-352 J-353 162.91 6 150 0 Open ; + P-261 J-366 J-183 217.21 6 150 0 Open ; + P-262 J-287 J-302 217.479 6 150 0 Open ; + P-263 J-367 J-127 207.649 6 150 0 Open ; + P-264 J-362 J-53 33.18 6 150 0 Open ; + P-265 J-397 J-167 5290.64 16 150 0 Open ; + P-266 J-157 J-150 301.549 6 150 0 Open ; + P-267 J-6 J-493 1902.939 10 150 0 Open ; + P-268 J-524 J-43 32.939 6 150 0 Open ; + P-269 J-373 J-381 23.2 16 150 0 Open ; + P-27 J-408 J-22 39.58 2 150 0 Open ; + P-270 J-374 J-375 407.42 6 150 0 Open ; + P-271 J-366 J-310 429.79 6 150 0 Open ; + P-272 J-80 J-373 338.01 16 150 0 Open ; + P-273 J-188 J-29 27.629 6 150 0 Open ; + P-274 J-377 J-378 351.39 2 150 0 Open ; + P-275 J-379 J-527 1320.42 8 150 0 Open ; + P-276 J-380 J-381 359.57 6 150 0 Open ; + P-277 J-328 J-375 12.449 16 150 0 Open ; + P-278 J-382 J-383 1181.569 2 150 0 Open ; + P-279 J-384 J-525 977.33 12 150 0 Open ; + P-28 J-52 J-38 1277.78 2 150 0 Open ; + P-280 J-385 J-361 475.88 10 150 0 Open ; + P-281 J-92 J-42 476.149 6 150 0 Open ; + P-282 J-386 J-387 178.979 6 150 0 Open ; + P-283 J-388 J-389 420.13 6 150 0 Open ; + P-284 J-390 J-495 13.789 6 150 0 Open ; + P-285 J-170 J-192 26.93 6 150 0 Open ; + P-286 J-391 J-392 396.329 6 150 0 Open ; + P-287 J-393 J-376 202.139 6 150 0 Open ; + P-288 J-394 J-395 893.13 4 150 0 Open ; + P-289 J-396 J-211 413.66 6 150 0 Open ; + P-29 J-16 J-238 1154.79 2 150 0 Open ; + P-290 J-188 J-126 223.809 6 150 0 Open ; + P-291 J-347 J-43 141.669 6 150 0 Open ; + P-292 J-398 J-399 294.059 12 150 0 Open ; + P-293 J-400 J-401 326.32 6 150 0 Open ; + P-294 J-209 J-14 1743.099 6 150 0 Open ; + P-295 R-2 O-Pump-1 1579.079 18 100 0 Open ; + P-296 J-405 J-50 319.63 1 150 0 Open ; + P-297 J-406 J-183 360.359 6 150 0 Open ; + P-298 J-407 J-24 104.15 2 150 0 Open ; + P-299 J-409 J-410 617.859 2 150 0 Open ; + P-3 J-5 J-6 1726.76 3 100 0 Open ; + P-30 J-22 J-411 293.179 6 150 0 Open ; + P-300 J-411 J-412 253.929 6 150 0 Open ; + P-301 J-413 J-26 46.959 6 150 0 Open ; + P-302 J-401 J-410 69.069 6 150 0 Open ; + P-303 J-166 J-415 8015.689 12 150 0 Open ; + P-304 J-416 J-283 366.17 6 150 0 Open ; + P-305 J-118 J-111 1395.89 6 150 0 Open ; + P-306 J-417 J-472 257.299 6 150 0 Open ; + P-307 J-419 J-420 248.389 10 150 0 Open ; + P-308 J-421 J-422 287.44 6 150 0 Open ; + P-309 J-150 J-386 244.639 6 150 0 Open ; + P-31 J-57 J-279d 198.509 6 150 0 Open ; + P-310 J-160 J-185 18.159 2 150 0 Open ; + P-311 J-29 J-509 188.399 6 150 0 Open ; + P-312 J-277 J-423 1694.609 10 150 0 Open ; + P-313 J-423 J-424 533.94 10 150 0 Open ; + P-314 J-425 J-355 599.229 10 150 0 Open ; + P-315 J-398 J-430 1677.79 10 150 0 Open ; + P-316 J-399 J-427 50.459 6 150 0 Open ; + P-317 J-428 J-196 440.739 8 150 0 Open ; + P-318 J-427 J-421 1405.9 6 150 0 Open ; + P-319 J-429 J-46 470.19 6 150 0 Open ; + P-32 J-59 J-45 38.24 6 150 0 Open ; + P-320 J-430 J-384 273.67 12 150 0 Open ; + P-321 J-431 J-167 3892.449 16 150 0 Open ; + P-322 J-432 J-508 123.19 6 150 0 Open ; + P-323 J-433 J-434 822.07 2 150 0 Open ; + P-324 J-435 J-451 235.5 6 150 0 Open ; + P-325 J-436 J-523 10.8 2 150 0 Open ; + P-326 J-438 J-439 1983.5 10 150 0 Open ; + P-327 J-219 J-440 719.359 6 150 0 Open ; + P-328 J-441 J-442 15.489 8 150 0 Open ; + P-329 J-442 J-230 691.95 6 150 0 Open ; + P-33 J-61 J-62 82.61 6 150 0 Open ; + P-330 J-443 J-16 36.13 6 150 0 Open ; + P-331 J-444 J-283 1234.15 6 150 0 Open ; + P-332 J-445 J-446 818.309 6 150 0 Open ; + P-333 J-411 J-269 35.31 6 150 0 Open ; + P-334 J-447 J-150 21.5 6 150 0 Open ; + P-335 J-448 J-174 116.199 6 150 0 Open ; + P-336 J-194 J-449 240.44 6 150 0 Open ; + P-337 J-450 J-340 1202.479 6 150 0 Open ; + P-338 J-70 J-449 636.539 0.75 150 0 Open ; + P-339 J-451 J-417 619.89 6 150 0 Open ; + P-34 J-63 J-64 608.109 6 150 0 Open ; + P-340 J-81 J-260 928.25 6 150 0 Open ; + P-341 J-444 J-269 41.08 6 150 0 Open ; + P-342 J-452 J-436 318.5 2 150 0 Open ; + P-343 J-446 J-22 41.54 6 150 0 Open ; + P-344 J-262 J-466 972.77 6 150 0 Open ; + P-345 J-454 J-455 556.01 6 150 0 Open ; + P-346 J-392 J-256 365.809 6 150 0 Open ; + P-347 J-456 J-454 1294.76 6 150 0 Open ; + P-348 J-296 J-457 528.57 6 150 0 Open ; + P-349 J-70 J-194 394.549 6 150 0 Open ; + P-35 J-65 J-66 460.049 2 150 0 Open ; + P-350 J-458 J-421 213.07 6 150 0 Open ; + P-351 J-459 J-344 892.359 12 150 0 Open ; + P-352 J-460 J-461 417.399 2 150 0 Open ; + P-353 J-462 J-433 373.1 2 150 0 Open ; + P-354 J-425 J-463 782.57 6 150 0 Open ; + P-355 J-439 J-503 2296.919 10 150 0 Open ; + P-356 J-10 J-4 10.1 12 150 0 Open ; + P-357 J-464 J-465 671.479 6 150 0 Open ; + P-358 J-321 J-466 1179.25 2 150 0 Open ; + P-359 J-94 J-197 356.41 6 150 0 Open ; + P-36 J-503 J-504 387.589 10 150 0 Open ; + P-360 J-400 J-16 1200.369 6 150 0 Open ; + P-361 J-441 J-468 903.909 8 150 0 Open ; + P-362 J-465 J-26 67.059 6 150 0 Open ; + P-363 J-195 J-146 424.779 6 150 0 Open ; + P-364 J-517 J-491 377.76 6 150 0 Open ; + P-365 J-196 J-450 764.27 6 150 0 Open ; + P-366 J-255 J-242 187.07 1 150 0 Open ; + P-367 J-170 J-172 245.529 6 150 0 Open ; + P-368 J-418 J-471 209.49 6 150 0 Open ; + P-369 J-472 J-473 447.48 6 150 0 Open ; + P-37 J-69 J-70 297.41 6 150 0 Open ; + P-370 J-440 J-255 252.009 1 150 0 Open ; + P-371 J-197 J-355 407.45 10 150 0 Open ; + P-372 J-449 J-386 373.79 0.75 150 0 Open ; + P-373 J-459 J-11 2339.389 12 150 0 Open ; + P-374 J-423 J-456 99.44 6 150 0 Open ; + P-375 J-475 J-476 215.49 10 150 0 Open ; + P-376 J-144 J-435 256.57 6 150 0 Open ; + P-377 J-477 J-366 288.79 6 150 0 Open ; + P-378 J-437 J-507 30.87 1 150 0 Open ; + P-379 J-475 J-424 1544.53 6 150 0 Open ; + P-38 J-71 J-72 495.29 6 150 0 Open ; + P-380 J-391 J-226 334.54 6 150 0 Open ; + P-381 J-479 J-490 12.13 6 150 0 Open ; + P-382 J-354 J-451 355.13 0.75 150 0 Open ; + P-383 J-216 J-297 474.809 6 150 0 Open ; + P-384 J-480 J-191 281.529 6 150 0 Open ; + P-385 J-190 J-31 856.02 2 150 0 Open ; + P-386 J-481 J-435 345.809 0.75 150 0 Open ; + P-387 J-426 J-482 1435.54 10 150 0 Open ; + P-388 J-456 J-454 1506.199 6 150 0 Open ; + P-389 J-483 J-484 495.589 6 150 0 Open ; + P-39 J-73 J-74 1149.88 8 150 0 Open ; + P-390 J-484 J-453 232.16 6 150 0 Open ; + P-391 J-217 J-230 506.97 2 150 0 Open ; + P-392 J-485 J-191 323.72 0.75 150 0 Open ; + P-393 J-49 J-218 492.64 6 150 0 Open ; + P-394 J-486 J-487 1043.68 4 150 0 Open ; + P-395 J-194 J-447 380.95 6 150 0 Open ; + P-396 J-488 J-156 647.669 6 150 0 Open ; + P-397 J-410 J-499 238.25 6 150 0 Open ; + P-398 J-433 J-489 211.74 2 150 0 Open ; + P-399 J-212 J-195 200.91 6 150 0 Open ; + P-4 J-7 J-8 3339.649 6 100 0 Open ; + P-40 J-75 J-76 242.21 8 150 0 Open ; + P-400 J-316 J-297 217.83 6 150 0 Open ; + P-401 J-490 J-517 321.679 6 150 0 Open ; + P-402 J-6 J-492 604.119 6 150 0 Open ; + P-403 J-291 J-338 207.309 4 150 0 Open ; + P-404 J-494 J-495 773.429 2 150 0 Open ; + P-405 J-440 J-399 305.279 6 150 0 Open ; + P-406 J-496 J-479 491.39 6 150 0 Open ; + P-407 J-497 J-505 822.409 6 150 0 Open ; + P-408 J-165 J-502 26.01 6 150 0 Open ; + P-409 J-464 J-432 602.71 6 150 0 Open ; + P-41 J-77 J-2 1137.3 6 150 0 Open ; + P-410 J-499 J-500 779.89 6 150 0 Open ; + P-411 J-501 J-464 1201.67 6 150 0 Open ; + P-412 J-175 J-502 709.929 6 150 0 Open ; + P-413 J-22 J-24 464.809 2 150 0 Open ; + P-414 T-1 J-476 89.19 10 150 0 Open ; + P-415 J-151 J-322 1043.829 6 150 0 Open ; + P-416 J-438 J-319 610.53 12 150 0 Open ; + P-417 J-506 J-438 96.809 10 150 0 Open ; + P-418 J-427 J-322 1444.979 6 150 0 Open ; + P-419 J-507 J-30a 359.66 6 150 0 Open ; + P-42 J-78 J-79 62.049 6 150 0 Open ; + P-420 J-225 J-508 847.27 6 150 0 Open ; + P-421 J-446 J-500 326.579 6 150 0 Open ; + P-422 J-261 J-425 1262.13 10 150 0 Open ; + P-423 J-7 J-223 1057.92 6 150 0 Open ; + P-424 J-473 J-29 49.97 6 150 0 Open ; + P-425 J-211 J-168 234.589 6 150 0 Open ; + P-426 J-510 J-512 39.72 6 150 0 Open ; + P-427 J-339 J-299 2019.589 8 150 0 Open ; + P-428 J-262 J-496 530.5 6 150 0 Open ; + P-429 J-424 J-475 343.149 10 150 0 Open ; + P-43 J-79 J-304 44.49 6 150 0 Open ; + P-430 J-484 J-453 2007.319 6 150 0 Open ; + P-431 J-319 J-201 1310.16 6 150 0 Open ; + P-432 J-224 J-163 14.199 6 150 0 Open ; + P-433 J-500 J-401 848.71 6 150 0 Open ; + P-434 J-516 J-284 88.18 6 150 0 Open ; + P-435 J-408 J-416 1085.66 2 150 0 Open ; + P-436 J-209 J-511 697.94 6 150 0 Open ; + P-437 J-512 J-429 512.21 6 150 0 Open ; + P-438 J-149 J-157 420.489 2 150 0 Open ; + P-439 J-392 J-172 328.66 6 150 0 Open ; + P-44 J-81 J-57 632.729 6 150 0 Open ; + P-440 J-62 J-413 1028.849 6 150 0 Open ; + P-441 J-276 J-261 650.34 10 150 0 Open ; + P-442 J-480 J-368 451.559 6 150 0 Open ; + P-443 J-298 J-41 1265.39 6 150 0 Open ; + P-444 J-513 J-480 515.4 4 150 0 Open ; + P-445 J-236 J-431 675.219 10 150 0 Open ; + P-446 J-94 J-259 3223.219 10 150 0 Open ; + P-447 J-266 J-121 445.079 6 150 0 Open ; + P-448 J-514 J-482 167.289 8 150 0 Open ; + P-449 J-498 J-443 421.17 6 150 0 Open ; + P-45 J-59 J-82 389.57 4 150 0 Open ; + P-450 J-515 J-509 132.889 6 150 0 Open ; + P-451 J-256 J-396 1251.579 6 150 0 Open ; + P-452 J-468 J-480 65.11 6 150 0 Open ; + P-453 J-298 J-510 189.71 6 150 0 Open ; + P-454 J-321 J-27 611.989 2 150 0 Open ; + P-455 J-502 J-518 437.119 6 150 0 Open ; + P-456 J-519 J-514 285.779 8 150 0 Open ; + P-457 J-443 J-391 1158.359 6 150 0 Open ; + P-458 J-520 J-395 646.75 4 150 0 Open ; + P-459 J-450 J-521 882.96 6 150 0 Open ; + P-46 J-83 J-84 379.57 4 150 0 Open ; + P-460 J-396 J-445 371.119 6 150 0 Open ; + P-461 J-509 J-312 314.459 6 150 0 Open ; + P-462 J-445 J-522 1650.4 6 150 0 Open ; + P-463 J-24 J-120 1296.949 6 150 0 Open ; + P-464 J-3 J-201 175.86 12 150 0 Open ; + P-465 J-4 J-398 750.82 12 150 0 Open ; + P-466 J-321 J-25 474.239 2 150 0 Open ; + P-467 J-25 J-296 31.03 6 150 0 Open ; + P-468 J-461 J-25 635.419 6 150 0 Open ; + P-469 J-14 J-28 17.159 6 150 0 Open ; + P-47 J-85 J-86 1366.579 4 150 0 Open ; + P-470 J-26 J-525 25.43 6 150 0 Open ; + P-471 J-394 J-28 258.799 4 150 0 Open ; + P-473 J-41 J-58 27.979 6 150 0 Open ; + P-474 J-45 O-RV-1 2466.75 6 150 0 Open ; + P-475 J-46 J-123 199.72 6 150 0 Open ; + P-476 J-47 J-524 225.21 6 150 0 Open ; + P-477 J-414 J-26 813.969 6 150 0 Open ; + P-478 J-28 J-139 13.539 6 150 0 Open ; + P-479 J-60 J-105 47.139 10 150 0 Open ; + P-48 J-87 J-315 57.169 6 150 0 Open ; + P-480 J-63 J-339 354.19 8 150 0 Open ; + P-482 J-69 J-49 20.34 6 150 0 Open ; + P-483 J-71 J-175 541.28 6 150 0 Open ; + P-484 J-73 J-75 802.21 3 100 0 Open ; + P-485 J-75 J-4 411.299 3 100 0 Open ; + P-486 J-85 J-45 2244.86 4 150 0 Open ; + P-487 J-86 J-59 1101.979 6 150 0 Open ; + P-488 J-101 J-129 74.29 2 150 0 Open ; + P-489 J-102 J-73 534.71 3 100 0 Open ; + P-49 J-88 J-89 84.319 6 150 0 Open ; + P-490 J-527 J-109 18.01 8 150 0 Open ; + P-491 J-529 J-537 535.03 6 150 0 Open ; + P-492 J-163 J-390 617.599 6 150 0 Open ; + P-493 J-279 O-Pump-2 3712.159 16 150 0 Open ; + P-494 J-177 J-173 280.07 6 150 0 Open ; + P-495 J-185 J-237 507.869 2 150 0 Open ; + P-496 J-187 J-91 16.569 1 150 0 Open ; + P-497 J-192 J-388 16.1 6 150 0 Open ; + P-498 J-199 J-224 652.83 6 150 0 Open ; + P-499 J-220 J-300 100.73 6 150 0 Open ; + P-5 J-9 J-10 648.15 12 100 0 Open ; + P-50 J-90 J-405 214.49 1 150 0 Open ; + P-500 J-235 J-236 80.26 10 150 0 Open ; + P-501 J-237 J-382 21.53 2 150 0 Open ; + P-502 J-240 J-245 1750.099 6 150 0 Open ; + P-503 J-245 J-135 93.949 6 150 0 Open ; + P-504 J-250 J-328 280.339 16 150 0 Open ; + P-505 J-254 J-268 277.38 6 150 0 Open ; + P-506 J-257 J-220 295.19 6 150 0 Open ; + P-507 J-270 J-516 954.53 6 150 0 Open ; + P-508 J-275 J-437 351.399 2 150 0 Open ; + P-509 J-281 J-331 3142.209 10 150 0 Open ; + P-51 J-92 J-279c 39.81 6 150 0 Open ; + P-510 J-286 J-318 216.74 6 150 0 Open ; + P-511 J-289 J-291 189.259 4 150 0 Open ; + P-512 J-293 J-537 36.38 6 150 0 Open ; + P-513 J-299 J-474 820.219 8 150 0 Open ; + P-514 I-RV-1 J-60 161.35 6 150 0 Open ; + P-515 J-302 J-356 671.84 6 150 0 Open ; + P-516 J-304 J-80 257.089 6 150 0 Open ; + P-517 J-315 J-88 44.43 6 150 0 Open ; + P-518 J-329 J-364 225.57 6 150 0 Open ; + P-519 J-332 J-96 228.21 6 150 0 Open ; + P-52 J-94 T-3 418.76 10 150 0 Open ; + P-520 J-333 J-98 212.449 6 150 0 Open ; + P-521 J-334 J-360 38.479 6 150 0 Open ; + P-522 J-338 J-1 1220.969 4 150 0 Open ; + P-523 J-341 J-208 114.3 6 150 0 Open ; + P-524 J-342 J-311 107.669 6 150 0 Open ; + P-525 O-Pump-2 J-279 3636.209 10 150 0 Open ; + P-526 J-364 J-288 38.419 6 150 0 Open ; + P-527 J-365 J-309 370.32 1 150 0 Open ; + P-528 J-19 J-30 1129.54 10 150 0 Open ; + P-53 J-96 J-97 254.75 6 150 0 Open ; + P-530 J-375 J-344 1753.359 16 150 0 Open ; + P-531 J-378 J-311 16.03 6 150 0 Open ; + P-532 J-379 J-56 43.81 10 150 0 Open ; + P-533 J-381 J-327 278.82 16 150 0 Open ; + P-534 J-405 J-187 39.45 1 150 0 Open ; + P-535 J-415 J-152 603.549 12 150 0 Open ; + P-536 J-429 J-477 12.64 6 150 0 Open ; + P-537 J-430 J-426 20.44 10 150 0 Open ; + P-538 J-431 J-337 1879.75 10 150 0 Open ; + P-539 J-442 J-31 759.119 8 150 0 Open ; + P-54 J-98 J-312 32.75 6 150 0 Open ; + P-540 J-452 J-212 475.959 6 150 0 Open ; + P-541 J-461 J-341 45.61 6 150 0 Open ; + P-542 J-466 J-453 865.809 6 150 0 Open ; + P-544 J-472 J-418 13.09 6 150 0 Open ; + P-545 J-476 J-419 44.119 10 150 0 Open ; + P-546 J-486 J-498 13.539 6 150 0 Open ; + P-547 J-487 J-227 1728.939 6 150 0 Open ; + P-548 J-490 J-452 23.559 6 150 0 Open ; + P-549 J-279a J-128 41.159 6 150 0 Open ; + P-55 J-100 J-101 245.46 2 150 0 Open ; + P-550 J-495 J-266 246.74 6 150 0 Open ; + P-551 J-496 J-207 71.65 6 150 0 Open ; + P-552 J-404 J-371 827.729 2 150 0 Open ; + P-553 J-499 J-416 38.95 6 150 0 Open ; + P-554 J-252 J-133 117.51 6 150 0 Open ; + P-555 J-507 J-478 587.32 1 150 0 Open ; + P-556 J-508 J-78 290.42 6 150 0 Open ; + P-557 J-512 J-127 453.35 6 150 0 Open ; + P-558 I-Pump-2 R-2 111.48 16 100 0 Open ; + P-559 I-Pump-1 R-1 211.279 18 150 0 Open ; + P-56 J-102 J-103 434.899 8 150 0 Open ; + P-560 R-2 I-Pump-2 113.699 10 150 0 Open ; + P-561 J-124 J-117 166.55 6 150 0 Open ; + P-562 J-204 J-322 20.94 4 150 0 Open ; + P-563 J-322 J-501 39.569 6 150 0 Open ; + P-564 J-166 J-28 834.109 8 150 0 Open ; + P-565 J-99 J-312 144.869 6 150 0 Open ; + P-566 J-233 J-191 11.449 6 150 0 Open ; + P-567 J-376 J-29 17.86 6 150 0 Open ; + P-568 J-36 J-116 255.839 2 150 0 Open ; + P-569 J-368 J-127 47.169 6 150 0 Open ; + P-57 J-35 J-7 1157.25 6 150 0 Open ; + P-570 J-251 J-502 446.75 6 150 0 Open ; + P-571 J-27 J-341 482.63 6 150 0 Open ; + P-572 J-30a J-275 35.279 1 150 0 Open ; + P-573 J-351 J-451 16.959 6 150 0 Open ; + P-574 J-428 J-31 489.38 8 150 0 Open ; + P-575 J-491 J-30a 45.49 6 150 0 Open ; + P-576 J-31 J-462 64.919 2 150 0 Open ; + P-577 J-30c J-270 389.6 6 150 0 Open ; + P-578 J-214 J-130 20.239 2 150 0 Open ; + P-579 J-130 J-50 30.559 1 150 0 Open ; + P-58 J-104 J-60 3092.82 10 150 0 Open ; + P-580 J-50 J-240 43.419 6 150 0 Open ; + P-581 J-362 J-55 289.97 10 150 0 Open ; + P-582 J-51 J-55 420.23 0.75 150 0 Open ; + P-583 J-53 J-187 51.799 2 150 0 Open ; + P-584 J-362 J-51 222.83 6 150 0 Open ; + P-585 J-55 J-154 38.13 0.75 150 0 Open ; + P-586 J-528 J-365 46.759 1 150 0 Open ; + P-587 J-55 J-56 290.559 10 150 0 Open ; + P-588 J-56 J-95 49.619 3 150 0 Open ; + P-589 J-21 J-290 5406.419 10 150 0 Open ; + P-59 J-36 J-333 273.179 6 150 0 Open ; + P-590 J-95 J-313 10.539 3 150 0 Open ; + P-591 J-279a J-51 1049.709 6 150 0 Open ; + P-592 J-326 J-95 180.679 2 150 0 Open ; + P-593 J-279b J-93 38.919 6 150 0 Open ; + P-594 J-353 J-128 461.17 6 150 0 Open ; + P-595 J-279c J-101 264.97 2 150 0 Open ; + P-596 J-345 J-279b 26.5 6 150 0 Open ; + P-597 J-279d J-264 36.659 1 150 0 Open ; + P-598 J-279b J-279c 1090.719 6 150 0 Open ; + P-599 J-290 J-305 1411.8 16 150 0 Open ; + P-6 J-23 J-504 20.62 10 150 0 Open ; + P-60 J-93 J-106 111.769 6 150 0 Open ; + P-600 J-58 J-279d 631.44 6 150 0 Open ; + P-601 J-305 J-30 14594.839 16 150 0 Open ; + P-602 J-290 J-305 1462.699 10 150 0 Open ; + P-604 J-30 J-330 312.88 10 150 0 Open ; + P-605 J-370 J-397 1899.55 6 150 0 Open ; + P-606 J-19 J-104 32.13 10 150 0 Open ; + P-607 J-397 J-19 49.08 6 150 0 Open ; + P-608 J-2 J-403 54.409 6 150 0 Open ; + P-609 J-403 J-402 386.22 6 150 0 Open ; + P-61 J-107 J-108 424.709 2 150 0 Open ; + P-610 J-403 J-228 124.33 6 150 0 Open ; + P-611 J-493 J-497 1348.089 10 150 0 Open ; + P-612 J-497 J-404 15.22 10 150 0 Open ; + P-613 J-467 J-504 453.029 10 150 0 Open ; + P-614 J-404 J-467 49.909 10 150 0 Open ; + P-615 J-504 J-539 12642.58 10 150 0 Open ; + P-616 J-68 J-503 596.419 6 150 0 Open ; + P-617 J-371 J-160 381.209 2 150 0 Open ; + P-618 J-503 J-371 27.19 6 150 0 Open ; + P-619 J-505 J-15 397.549 6 150 0 Open ; + P-62 J-109 J-110 536.59 8 150 0 Open ; + P-620 J-371 J-505 14.909 6 150 0 Open ; + P-621 J-420 J-516 330.399 10 150 0 Open ; + P-622 J-516 J-506 48.479 10 150 0 Open ; + P-623 J-469 J-517 602.53 4 150 0 Open ; + P-624 J-523 J-275 426.92 2 150 0 Open ; + P-625 J-517 J-523 31.829 4 150 0 Open ; + P-626 J-525 J-87 143.039 6 150 0 Open ; + P-627 J-37 J-124 78.589 6 150 0 Open ; + P-628 J-526 J-279c 735.7 2 150 0 Open ; + P-629 J-524 J-526 31.35 6 150 0 Open ; + P-63 J-111 J-34 2940.5 6 150 0 Open ; + P-630 J-526 J-279b 393.89 6 150 0 Open ; + P-631 J-287 J-527 26.77 6 150 0 Open ; + P-632 J-527 J-110 551.08 6 150 0 Open ; + P-633 J-53 J-528 460.51 6 150 0 Open ; + P-634 J-528 J-246 97.33 6 150 0 Open ; + P-635 J-530 J-135 55.9 10 150 0 Open ; + P-636 J-336 J-530 83.769 6 150 0 Open ; + P-637 J-529 J-359 151.57 6 150 0 Open ; + P-638 J-530 J-531 64.94 6 150 0 Open ; + P-639 J-529 J-533 493.779 1 150 0 Open ; + P-64 J-43 J-112 451.119 6 150 0 Open ; + P-640 J-533 J-530 70.559 10 150 0 Open ; + P-641 J-533 J-531 34.11 1 150 0 Open ; + P-642 J-532 J-245 238.429 6 150 0 Open ; + P-643 J-244 J-534 13.729 6 150 0 Open ; + P-644 J-535 J-531 124.769 6 150 0 Open ; + P-645 J-534 J-535 35.33 6 150 0 Open ; + P-646 J-535 J-532 37.119 6 150 0 Open ; + P-648 J-534 J-537 343.76 10 150 0 Open ; + P-649 J-12 J-539 8313.11 10 150 0 Open ; + P-65 J-113 J-114 1354.15 12 150 0 Open ; + P-650 J-539 J-35 1156.989 12 150 0 Open ; + P-66 J-115 J-61 1007.2 6 150 0 Open ; + P-67 J-116 J-117 494.679 6 150 0 Open ; + P-68 J-89 J-118 85.75 6 150 0 Open ; + P-69 J-119 J-24 21.94 6 150 0 Open ; + P-7 J-11 J-12 719.979 12 150 0 Open ; + P-70 J-121 J-122 1113.819 4 150 0 Open ; + P-71 J-123 J-117 61.77 6 150 0 Open ; + P-72 J-125 J-126 447.63 6 150 0 Open ; + P-73 J-127 J-116 279.649 2 150 0 Open ; + P-74 J-128 J-90 1034.04 1 150 0 Open ; + P-75 J-129 J-112 856.4 2 150 0 Open ; + P-76 J-130 J-531 1831.349 1 150 0 Open ; + P-77 J-132 J-36 491.559 6 150 0 Open ; + P-78 J-133 J-134 265.29 6 150 0 Open ; + P-79 J-135 J-136 1153.13 8 150 0 Open ; + P-8 J-13 J-493 652.599 10 150 0 Open ; + P-80 J-137 J-62 486.579 1 150 0 Open ; + P-81 J-126 J-138 297.41 6 150 0 Open ; + P-82 J-139 J-140 353.529 6 150 0 Open ; + P-83 J-141 J-142 268.69 6 150 0 Open ; + P-84 J-143 J-141 164.089 6 150 0 Open ; + P-85 J-144 J-145 540.76 6 150 0 Open ; + P-86 J-146 J-144 136.32 6 150 0 Open ; + P-87 J-147 J-148 216.44 6 150 0 Open ; + P-88 J-48 J-149 379.869 2 150 0 Open ; + P-89 J-150 J-151 40.27 6 150 0 Open ; + P-9 J-17 J-9 1755.589 12 150 0 Open ; + P-90 J-152 J-153 1410.5 12 150 0 Open ; + P-91 J-154 J-155 151.289 2 150 0 Open ; + P-92 J-147 J-526 33.209 2 150 0 Open ; + P-93 J-156 J-157 206.27 6 150 0 Open ; + P-94 J-158 J-13 1438.28 4 150 0 Open ; + P-95 J-159 J-404 761.13 2 150 0 Open ; + P-96 J-161 J-132 401.23 6 150 0 Open ; + P-97 J-162 J-163 467.929 2 150 0 Open ; + P-98 J-164 J-165 453.769 6 120 0 Open ; + P-99 J-166 J-167 5302.879 12 120 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + ~@Pump-1 I-Pump-1 O-Pump-1 HEAD PC_~@Pump-1 ; + ~@Pump-2 I-Pump-2 O-Pump-2 HEAD PC_~@Pump-2 ; + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + ~@RV-1 I-RV-1 O-RV-1 1000 PRV 99.99 0 ; + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers +; + 1 0.330000 0.250000 0.209000 0.209000 0.259000 0.360000 + 1 0.529000 0.910000 1.200000 1.299000 1.340000 1.340000 + 1 1.320000 1.269000 1.250000 1.250000 1.279000 1.370000 + 1 1.519000 1.700000 1.750000 1.669000 0.899000 0.479000 +; + 2 1.000000 +; + 3 0.165000 0.125000 0.105000 0.105000 0.130000 0.180000 + 3 0.265000 0.455000 0.600000 0.650000 0.670000 0.670000 + 3 0.660000 0.635000 0.625000 0.625000 0.640000 0.685000 + 3 0.760000 0.850000 0.875000 0.835000 0.450000 0.240000 +; + ENRG1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 + ENRG1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 + ENRG1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 + ENRG1 1.000000 1.000000 1.000000 1.000000 1.000000 +; + 5 1.000000 + +[CURVES] +;ID X-Value Y-Value +;PUMP: PUMP: PUMP: PC_~@Pump-1 + PC_~@Pump-1 3383.832965 146.135933 +;PUMP: PUMP: PUMP: PC_~@Pump-2 + PC_~@Pump-2 1564.602529 316.093675 + +[CONTROLS] +Pump ~@Pump-2 Open IF Tank T-2 below 149.77 +Pump ~@Pump-2 Closed IF Tank T-2 above 164.77 + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75.0000 + Global Price 0.0000 + Global Pattern ENRG1 + Demand Charge 0.0000 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk -0.5000 + Global Wall -1.0000 + Limiting Potential 0.0000 + Roughness Correlation 0.0000 + +[MIXING] +;Tank Model + +[TIMES] + Duration 24:00 + Hydraulic Timestep 0:30 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 0:30 + Report Start 0:00 + Start ClockTime 00:00:00 AM + Statistic NONE + +[REPORT] + Status Full + Summary No + Page 0 + +[OPTIONS] + Units GPM + Headloss H-W + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.0001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced STOP + Pattern 1 + Demand Multiplier 1 + Emitter Exponent 0.5 + Quality AGE mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +J-1 4758568.380 3706442.000 +J-10 4766650.880 3702303.000 +J-100 4761343.590 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At each time step in the simulation duration, PRV status is determined based on the relationship between upstream and downstream node heads and the pressure setting value (Hu, Hd, and Hset respectively).\n", + "\n", + "If Hu > Hset > Hd, then the PRV is **active** and Hd is set to = Hset.\n", + "If Hset > Hu > Hd, then the PRV is **open** and the heads are unchanged.\n", + "If Hd > Hu, then the PRV is **closed**, flow through the PRV is 0, and the upstream and downstream nodes are disconnected.\n", + "\n", + "This notebook demonstrates how the pipedream PRV control structure behaves for a toy network with 3 junctions, network Net2 (with 35 junctions), and network ky6 (with 543 junctions). We compare pipedream and WNTR (using EPANET engine) hydraulic simulation results to observe the accuracy of the pipedream model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d141069", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import wntr\n", + "from pipedream_solver.hydraulics import SuperLink\n", + "from pipedream_solver.simulation import Simulation\n", + "from pipedream_solver.nutils import interpolate_sample\n", + "import pipedream_utility as pdu\n", + "from pipedream_utility import *\n", + "import pipedream_simulation as pd_sim\n", + "from pipedream_simulation import *\n", + "import viswaternet as vis\n", + "import math\n", + "\n", + "#Don't show future warnings\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2833c54d", + "metadata": {}, + "outputs": [], + "source": [ + "dt = 3600\n", + "t_run = 24" + ] + }, + { + "cell_type": "markdown", + "id": "447dce89", + "metadata": {}, + "source": [ + "# 1. Toy network: open PRV" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f29bebbe", + "metadata": {}, + "outputs": [], + "source": [ + "# import INP file\n", + "inp = 'Networks/PRV_open.inp'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8591f61a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Absolute difference in node heads [m]: 0.0037121378138264363 and link flow rates [m3/hr]: 1.242886135478638\n" + ] + } + ], + "source": [ + "# run hydraulic simulation using pipedream\n", + "H_df, Q_df, Q_pump, Q_prv, model, Q_in_all_df, pumps, superjunctions, orifices, superlinks, prvs = run_pipedream_simulation(inp, t_run = t_run, dt = dt, banded = False)\n", + "\n", + "# run hydraulic simulation using WNTR\n", + "wn = wntr.network.WaterNetworkModel(inp)\n", + "wn.options.time.report_timestep=dt \n", + "wn.options.time.duration=t_run*3600\n", + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "\n", + "# store WNTR results for time series plotting\n", + "wntr_results_head=results.node['head'].iloc[:-1,:]\n", + "wntr_results_flow=results.link['flowrate'].iloc[:-1,:]\n", + "\n", + "# store mean absolute difference (MAD) between pipedream and WNTR head and flow results\n", + "abs_diff_node = abs(results.node['head']-H_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "abs_diff_link = 3600*abs(results.link['flowrate']-Q_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "\n", + "print(' Absolute difference in node heads [m]:', abs_diff_node.mean(), ' and link flow rates [m3/hr]:', abs_diff_link.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "efdf433b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot network layout show MAD at nodes and pipes\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize = (12,6))\n", + "node_size = 200\n", + "\n", + "\n", + "junction_names = list(abs_diff_node.index)\n", + "link_names = list(abs_diff_link.index)\n", + "junction_values = list(abs_diff_node)\n", + "link_values = list(abs_diff_link) \n", + "\n", + "vis_model = vis.VisWNModel(inp)\n", + "\n", + "ax[0].set_title(inp, fontsize = 14)\n", + "ax[0].set_frame_on(False) \n", + "\n", + "style = vis.NetworkStyle(cmap = 'bwr', node_border_width=1, node_border_color = 'k', draw_color_bar = True, \n", + " node_size = node_size, tank_color='k', draw_base_legend=False)\n", + "\n", + "vis_model.plot_unique_data(ax=ax[0], parameter = \"custom_data\", parameter_type = 'node', \n", + " custom_data_values = [junction_names, junction_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Nodal MAD [m]')\n", + "\n", + "ax[1].set_frame_on(False) \n", + "vis_model.plot_unique_data(ax=ax[1], parameter = \"custom_data\", parameter_type = 'link', \n", + " custom_data_values = [link_names, link_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Link MAD [m3/hr]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "582da64a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot pipedream and wntr PRV flows and upstream + downstream node heads\n", + "n_superlinks = wn.num_valves\n", + "n_cols = 3\n", + "n_rows =wn.num_valves \n", + "\n", + "if wn.num_valves > 0:\n", + " fig, ax = plt.subplots(n_rows, n_cols, figsize=(12, 0.75 * 12 * n_rows / n_cols))\n", + " for i in range(wn.num_valves):\n", + " valve = wn.get_link(wn.valve_name_list[i])\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*Q_prv[:,i:i+1], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*wntr_results_flow[wn.valve_name_list[i]], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i].set_title(f'{wn.get_link(wn.valve_name_list[i]).valve_type} {wn.valve_name_list[i]}')\n", + "\n", + " ax.flat[3*i+1].plot(H_df.index/3600,H_df[valve.start_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+1].plot(wntr_results_head.index/3600,wntr_results_head[valve.start_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+1].set_title('Start node')\n", + " ax.flat[3*i+1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+1].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+1].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[3*i+2].plot(H_df.index/3600,H_df[valve.end_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+2].plot(wntr_results_head.index/3600,wntr_results_head[valve.end_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+2].set_title('End node')\n", + " ax.flat[3*i+2].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+2].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+2].set_xlabel('Hour of Day')\n", + "\n", + "\n", + " ax.flat[0].legend()\n", + " plt.suptitle('Valve flows')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "21bb8a18", + "metadata": {}, + "source": [ + "# 2. Toy network: active PRV" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c305b79d", + "metadata": {}, + "outputs": [], + "source": [ + "# import INP file\n", + "inp = 'Networks/PRV_active.inp'" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "46539d18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Absolute difference in node heads [m]: 0.002081947717062397 and link flow rates [m3/hr]: 0.9557085368231356\n" + ] + } + ], + "source": [ + "# run hydraulic simulation using pipedream\n", + "H_df, Q_df, Q_pump, Q_prv, model, Q_in_all_df, pumps, superjunctions, orifices, superlinks, prvs = run_pipedream_simulation(inp, t_run = t_run, dt = dt, banded = False)\n", + "\n", + "# run hydraulic simulation using WNTR\n", + "wn = wntr.network.WaterNetworkModel(inp)\n", + "wn.options.time.report_timestep=dt \n", + "wn.options.time.duration=t_run*3600\n", + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "\n", + "# store WNTR results for time series plotting\n", + "wntr_results_head=results.node['head'].iloc[:-1,:]\n", + "wntr_results_flow=results.link['flowrate'].iloc[:-1,:]\n", + "\n", + "# store mean absolute difference (MAD) between pipedream and WNTR head and flow results\n", + "abs_diff_node = abs(results.node['head']-H_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "abs_diff_link = 3600*abs(results.link['flowrate']-Q_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "\n", + "print(' Absolute difference in node heads [m]:', abs_diff_node.mean(), ' and link flow rates [m3/hr]:', abs_diff_link.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ae27f637", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot network layout show MAD at nodes and pipes\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize = (12,6))\n", + "node_size = 200\n", + "\n", + "\n", + "junction_names = list(abs_diff_node.index)\n", + "link_names = list(abs_diff_link.index)\n", + "junction_values = list(abs_diff_node)\n", + "link_values = list(abs_diff_link) \n", + "\n", + "vis_model = vis.VisWNModel(inp)\n", + "\n", + "ax[0].set_title(inp, fontsize = 14)\n", + "ax[0].set_frame_on(False) \n", + "\n", + "style = vis.NetworkStyle(cmap = 'bwr', node_border_width=1, node_border_color = 'k', draw_color_bar = True, \n", + " node_size = node_size, tank_color='k', draw_base_legend=False)\n", + "\n", + "vis_model.plot_unique_data(ax=ax[0], parameter = \"custom_data\", parameter_type = 'node', \n", + " custom_data_values = [junction_names, junction_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Nodal MAD [m]')\n", + "\n", + "ax[1].set_frame_on(False) \n", + "vis_model.plot_unique_data(ax=ax[1], parameter = \"custom_data\", parameter_type = 'link', \n", + " custom_data_values = [link_names, link_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Link MAD [m3/hr]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "276f3728", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot pipedream and wntr PRV flows and upstream + downstream node heads\n", + "n_superlinks = wn.num_valves\n", + "n_cols = 3\n", + "n_rows =wn.num_valves \n", + "\n", + "if wn.num_valves > 0:\n", + " fig, ax = plt.subplots(n_rows, n_cols, figsize=(12, 0.75 * 12 * n_rows / n_cols))\n", + " for i in range(wn.num_valves):\n", + " valve = wn.get_link(wn.valve_name_list[i])\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*Q_prv[:,i:i+1], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*wntr_results_flow[wn.valve_name_list[i]], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i].set_title(f'{wn.get_link(wn.valve_name_list[i]).valve_type} {wn.valve_name_list[i]}')\n", + "\n", + " ax.flat[3*i+1].plot(H_df.index/3600,H_df[valve.start_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+1].plot(wntr_results_head.index/3600,wntr_results_head[valve.start_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+1].set_title('Start node')\n", + " ax.flat[3*i+1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+1].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+1].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[3*i+2].plot(H_df.index/3600,H_df[valve.end_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+2].plot(wntr_results_head.index/3600,wntr_results_head[valve.end_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+2].set_title('End node')\n", + " ax.flat[3*i+2].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+2].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+2].set_xlabel('Hour of Day')\n", + " ax.flat[3*i+2].set_ylim(math.floor(wntr_results_head[valve.end_node_name][0])-1, math.ceil(wntr_results_head[valve.end_node_name][0])+1)\n", + "\n", + "\n", + " ax.flat[0].legend()\n", + " plt.suptitle('Valve flows')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "bd301f2d", + "metadata": {}, + "source": [ + "# 3. Toy network: closed PRV" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5a13017f", + "metadata": {}, + "outputs": [], + "source": [ + "# import INP file\n", + "inp = 'Networks/PRV_closed.inp'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a7fc0cb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Absolute difference in node heads [m]: 9.366268723235057e-05 and link flow rates [m3/hr]: 2.0613780571387963e-05\n" + ] + } + ], + "source": [ + "# run hydraulic simulation using pipedream\n", + "H_df, Q_df, Q_pump, Q_prv, model, Q_in_all_df, pumps, superjunctions, orifices, superlinks, prvs = run_pipedream_simulation(inp, t_run = t_run, dt = dt, banded = False)\n", + "\n", + "# run hydraulic simulation using WNTR\n", + "wn = wntr.network.WaterNetworkModel(inp)\n", + "wn.options.time.report_timestep=dt \n", + "wn.options.time.duration=t_run*3600\n", + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "\n", + "# store WNTR results for time series plotting\n", + "wntr_results_head=results.node['head'].iloc[:-1,:]\n", + "wntr_results_flow=results.link['flowrate'].iloc[:-1,:]\n", + "\n", + "# store mean absolute difference (MAD) between pipedream and WNTR head and flow results\n", + "abs_diff_node = abs(results.node['head']-H_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "abs_diff_link = 3600*abs(results.link['flowrate']-Q_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "\n", + "print(' Absolute difference in node heads [m]:', abs_diff_node.mean(), ' and link flow rates [m3/hr]:', abs_diff_link.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "15f68dfa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot network layout show MAD at nodes and pipes\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize = (12,6))\n", + "node_size = 200\n", + "\n", + "\n", + "junction_names = list(abs_diff_node.index)\n", + "link_names = list(abs_diff_link.index)\n", + "junction_values = list(abs_diff_node)\n", + "link_values = list(abs_diff_link) \n", + "\n", + "vis_model = vis.VisWNModel(inp)\n", + "\n", + "ax[0].set_title(inp, fontsize = 14)\n", + "ax[0].set_frame_on(False) \n", + "\n", + "style = vis.NetworkStyle(cmap = 'bwr', node_border_width=1, node_border_color = 'k', draw_color_bar = True, \n", + " node_size = node_size, tank_color='k', draw_base_legend=False)\n", + "\n", + "vis_model.plot_unique_data(ax=ax[0], parameter = \"custom_data\", parameter_type = 'node', \n", + " custom_data_values = [junction_names, junction_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Nodal MAD [m]')\n", + "\n", + "ax[1].set_frame_on(False) \n", + "vis_model.plot_unique_data(ax=ax[1], parameter = \"custom_data\", parameter_type = 'link', \n", + " custom_data_values = [link_names, link_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Link MAD [m3/hr]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fdfa9bdb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot pipedream and wntr PRV flows and upstream + downstream node heads\n", + "n_superlinks = wn.num_valves\n", + "n_cols = 3\n", + "n_rows =wn.num_valves \n", + "\n", + "if wn.num_valves > 0:\n", + " fig, ax = plt.subplots(n_rows, n_cols, figsize=(12, 0.75 * 12 * n_rows / n_cols))\n", + " for i in range(wn.num_valves):\n", + " valve = wn.get_link(wn.valve_name_list[i])\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*Q_prv[:,i:i+1], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*wntr_results_flow[wn.valve_name_list[i]], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i].set_title(f'{wn.get_link(wn.valve_name_list[i]).valve_type} {wn.valve_name_list[i]}')\n", + "\n", + " ax.flat[3*i+1].plot(H_df.index/3600,H_df[valve.start_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+1].plot(wntr_results_head.index/3600,wntr_results_head[valve.start_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+1].set_title('Start node')\n", + " ax.flat[3*i+1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+1].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+1].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[3*i+2].plot(H_df.index/3600,H_df[valve.end_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+2].plot(wntr_results_head.index/3600,wntr_results_head[valve.end_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+2].set_title('End node')\n", + " ax.flat[3*i+2].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+2].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+2].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[0].legend()\n", + " plt.suptitle('Valve flows')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "6b072de5", + "metadata": {}, + "source": [ + "# 4. Net2: open PRV" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5ab43544", + "metadata": {}, + "outputs": [], + "source": [ + "# import INP file\n", + "inp = 'Networks/Net2 prv open.inp'" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "95368876", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Absolute difference in node heads [m]: 0.12341813585611826 and link flow rates [m3/hr]: 1.1456692121917555\n" + ] + } + ], + "source": [ + "# run hydraulic simulation using pipedream\n", + "H_df, Q_df, Q_pump, Q_prv, model, Q_in_all_df, pumps, superjunctions, orifices, superlinks, prvs = run_pipedream_simulation(inp, t_run = t_run, dt = dt, banded = False)\n", + "\n", + "# run hydraulic simulation using WNTR\n", + "wn = wntr.network.WaterNetworkModel(inp)\n", + "wn.options.time.report_timestep=dt \n", + "wn.options.time.duration=t_run*3600\n", + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "\n", + "# store WNTR results for time series plotting\n", + "wntr_results_head=results.node['head'].iloc[:-1,:]\n", + "wntr_results_flow=results.link['flowrate'].iloc[:-1,:]\n", + "\n", + "# store mean absolute difference (MAD) between pipedream and WNTR head and flow results\n", + "abs_diff_node = abs(results.node['head']-H_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "abs_diff_link = 3600*abs(results.link['flowrate']-Q_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "\n", + "print(' Absolute difference in node heads [m]:', abs_diff_node.mean(), ' and link flow rates [m3/hr]:', abs_diff_link.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c7a28c9c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot network layout show MAD at nodes and pipes\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize = (12,6))\n", + "node_size = 200\n", + "\n", + "\n", + "junction_names = list(abs_diff_node.index)\n", + "link_names = list(abs_diff_link.index)\n", + "junction_values = list(abs_diff_node)\n", + "link_values = list(abs_diff_link) \n", + "\n", + "vis_model = vis.VisWNModel(inp)\n", + "\n", + "ax[0].set_title(inp, fontsize = 14)\n", + "ax[0].set_frame_on(False) \n", + "\n", + "style = vis.NetworkStyle(cmap = 'bwr', node_border_width=1, node_border_color = 'k', draw_color_bar = True, \n", + " node_size = node_size, tank_color='k', draw_base_legend=False)\n", + "\n", + "vis_model.plot_basic_elements(ax=ax[0], style = vis.NetworkStyle(node_size=0, draw_base_legend = False))\n", + "vis_model.plot_unique_data(ax=ax[0], parameter = \"custom_data\", parameter_type = 'node', \n", + " custom_data_values = [junction_names, junction_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Nodal MAD [m]')\n", + "\n", + "ax[1].set_frame_on(False) \n", + "vis_model.plot_unique_data(ax=ax[1], parameter = \"custom_data\", parameter_type = 'link', \n", + " custom_data_values = [link_names, link_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Link MAD [m3/hr]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1d57e88f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot pipedream and wntr PRV flows and upstream + downstream node heads\n", + "n_superlinks = wn.num_valves\n", + "n_cols = 3\n", + "n_rows =wn.num_valves \n", + "\n", + "if wn.num_valves > 0:\n", + " fig, ax = plt.subplots(n_rows, n_cols, figsize=(12, 0.75 * 12 * n_rows / n_cols))\n", + " for i in range(wn.num_valves):\n", + " valve = wn.get_link(wn.valve_name_list[i])\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*Q_prv[:,i:i+1], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*wntr_results_flow[wn.valve_name_list[i]], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i].set_title(f'{wn.get_link(wn.valve_name_list[i]).valve_type} {wn.valve_name_list[i]}')\n", + "\n", + " ax.flat[3*i+1].plot(H_df.index/3600,H_df[valve.start_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+1].plot(wntr_results_head.index/3600,wntr_results_head[valve.start_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+1].set_title('Start node')\n", + " ax.flat[3*i+1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+1].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+1].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[3*i+2].plot(H_df.index/3600,H_df[valve.end_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+2].plot(wntr_results_head.index/3600,wntr_results_head[valve.end_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+2].set_title('End node')\n", + " ax.flat[3*i+2].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+2].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+2].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[0].legend()\n", + " plt.suptitle('Valve flows')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "26afbd06", + "metadata": {}, + "source": [ + "# 5. ky6: active PRV" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "395f5686", + "metadata": {}, + "outputs": [], + "source": [ + "# import INP file\n", + "inp = 'Networks/headpump ky6 new.inp'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "195ce45e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mst2245\\Box Sync\\Research\\8. Pipedream\\PRV testing\\pipedream_solver\\nsuperlink.py:1293: LinAlgWarning: Ill-conditioned matrix (rcond=8.28742e-17): result may not be accurate.\n", + " H_j_next = scipy.linalg.solve(l, r)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Absolute difference in node heads [m]: 0.24708259277476405 and link flow rates [m3/hr]: 0.4644892295620231\n" + ] + } + ], + "source": [ + "# run hydraulic simulation using pipedream\n", + "H_df, Q_df, Q_pump, Q_prv, model, Q_in_all_df, pumps, superjunctions, orifices, superlinks, prvs = run_pipedream_simulation(inp, t_run = t_run, dt = dt, banded = False)\n", + "\n", + "# run hydraulic simulation using WNTR\n", + "wn = wntr.network.WaterNetworkModel(inp)\n", + "wn.options.time.report_timestep=dt \n", + "wn.options.time.duration=t_run*3600\n", + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "\n", + "# store WNTR results for time series plotting\n", + "wntr_results_head=results.node['head'].iloc[:-1,:]\n", + "wntr_results_flow=results.link['flowrate'].iloc[:-1,:]\n", + "\n", + "# store mean absolute difference (MAD) between pipedream and WNTR head and flow results\n", + "abs_diff_node = abs(results.node['head']-H_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "abs_diff_link = 3600*abs(results.link['flowrate']-Q_df).dropna(axis=1,how='all').dropna(axis=0,how='all').mean()\n", + "\n", + "print(' Absolute difference in node heads [m]:', abs_diff_node.mean(), ' and link flow rates [m3/hr]:', abs_diff_link.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "30351819", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot network layout show MAD at nodes and pipes\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize = (12,6))\n", + "node_size = 200\n", + "\n", + "\n", + "junction_names = list(abs_diff_node.index)\n", + "link_names = list(abs_diff_link.index)\n", + "junction_values = list(abs_diff_node)\n", + "link_values = list(abs_diff_link) \n", + "\n", + "vis_model = vis.VisWNModel(inp)\n", + "\n", + "ax[0].set_title(inp, fontsize = 14)\n", + "ax[0].set_frame_on(False) \n", + "\n", + "style = vis.NetworkStyle(cmap = 'bwr', node_border_width=1, node_border_color = 'k', draw_color_bar = True, \n", + " node_size = node_size, tank_color='k', draw_base_legend=False)\n", + "\n", + "vis_model.plot_unique_data(ax=ax[0], parameter = \"custom_data\", parameter_type = 'node', \n", + " custom_data_values = [junction_names, junction_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Nodal MAD [m]')\n", + "\n", + "ax[1].set_frame_on(False) \n", + "vis_model.plot_unique_data(ax=ax[1], parameter = \"custom_data\", parameter_type = 'link', \n", + " custom_data_values = [link_names, link_values], data_type = 'continuous', \n", + " vmin = 0, style = style, color_bar_title ='Link MAD [m3/hr]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0439b279", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot pipedream and wntr PRV flows and upstream + downstream node heads\n", + "n_superlinks = wn.num_valves\n", + "n_cols = 3\n", + "n_rows =wn.num_valves \n", + "\n", + "if wn.num_valves > 0:\n", + " fig, ax = plt.subplots(n_rows, n_cols, figsize=(12, 0.75 * 12 * n_rows / n_cols))\n", + " for i in range(wn.num_valves):\n", + " valve = wn.get_link(wn.valve_name_list[i])\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*Q_prv[:,i:i+1], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*wntr_results_flow[wn.valve_name_list[i]], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i].set_title(f'{wn.get_link(wn.valve_name_list[i]).valve_type} {wn.valve_name_list[i]}')\n", + "\n", + " ax.flat[3*i+1].plot(H_df.index/3600,H_df[valve.start_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+1].plot(wntr_results_head.index/3600,wntr_results_head[valve.start_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+1].set_title('Start node')\n", + " ax.flat[3*i+1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+1].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+1].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[3*i+2].plot(H_df.index/3600,H_df[valve.end_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+2].plot(wntr_results_head.index/3600,wntr_results_head[valve.end_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+2].set_title('End node')\n", + " ax.flat[3*i+2].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n", + " ax.flat[3*i+2].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+2].set_xlabel('Hour of Day')\n", + " ax.flat[3*i+2].set_ylim(math.floor(wntr_results_head[valve.end_node_name][0])-1, math.ceil(wntr_results_head[valve.end_node_name][0])+1)\n", + "\n", + "\n", + " ax.flat[0].legend()\n", + " plt.suptitle('Valve flows')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "08e117bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot pipedream and wntr pump flows and upstream + downstream node heads\n", + "n_superlinks = wn.num_pumps\n", + "n_cols = 3\n", + "n_rows = wn.num_pumps\n", + "\n", + "if wn.num_pumps > 0:\n", + " fig, ax = plt.subplots(n_rows, n_cols, figsize=(12, 0.75 * 12 * n_rows / n_cols))\n", + " for i in range(wn.num_pumps):\n", + " pump = wn.get_link(wn.pump_name_list[i])\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*Q_pump[:,i:i+1], c='r', alpha=0.75)\n", + " ax.flat[3*i].plot(wntr_results_flow.index/3600,3600*wntr_results_flow[wn.pump_name_list[i]], c='0.3', linestyle = '--', alpha=0.75)\n", + " ax.flat[3*i].set_title(f'Pump {wn.pump_name_list[i]}')\n", + " \n", + " \n", + " ax.flat[3*i+1].plot(H_df.index/3600,H_df[pump.start_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+1].plot(wntr_results_head.index/3600,wntr_results_head[pump.start_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+1].set_title('Start node')\n", + " ax.flat[3*i+1].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+1].set_xlabel('Hour of Day')\n", + "\n", + " ax.flat[3*i+2].plot(H_df.index/3600,H_df[pump.end_node_name], c='r', alpha=0.75, label = 'Pipedream')\n", + " ax.flat[3*i+2].plot(wntr_results_head.index/3600,wntr_results_head[pump.end_node_name], c='0.3', linestyle = '--', alpha=0.75, label = 'EPANET')\n", + " ax.flat[3*i+2].set_title('End node')\n", + " ax.flat[3*i+2].set_ylabel('Head ($m$)')\n", + " ax.flat[3*i+2].set_xlabel('Hour of Day')\n", + "\n", + " if i==0:\n", + " ax.flat[3*i].set_ylim(math.floor(3600*wntr_results_flow[wn.pump_name_list[i]][0])-1,math.ceil(3600*wntr_results_flow[wn.pump_name_list[i]][0])+1)\n", + " ax.flat[3*i+1].set_ylim(math.floor(wntr_results_head[pump.start_node_name][0])-1,math.ceil(wntr_results_head[pump.start_node_name][0])+1)\n", + " ax.flat[3*i+2].set_ylim(math.floor(wntr_results_head[pump.end_node_name][0])-1,math.ceil(wntr_results_head[pump.end_node_name][0])+1)\n", + " \n", + " ax.flat[0].legend(['Pipedream','EPANET', 'EPANET original'])\n", + " plt.suptitle('Pump flows')\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08fd878e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PRV tests/pipedream_simulation.py b/PRV tests/pipedream_simulation.py new file mode 100644 index 0000000..718da24 --- /dev/null +++ b/PRV tests/pipedream_simulation.py @@ -0,0 +1,198 @@ +#This code is used to compare model output between Pipedream and Epanet + +import io +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import wntr +import scipy as sc +import networkx as nx +import networkx.drawing.nx_pylab as nxp +from pipedream_solver.hydraulics import SuperLink +from pipedream_solver.simulation import Simulation +from pipedream_solver.nutils import interpolate_sample +import random +import time +# import pipedream_utility_v3 as pdu +import pipedream_utility as pdu +from matplotlib.ticker import FormatStrFormatter + + +#Don't show future warnings +import warnings +warnings.simplefilter(action='ignore', category=FutureWarning) + + +def run_pipedream_simulation(inp, t_run=24, dt=3600, banded=False, num_iter=40, use_tank_init_cond=False): + #Input path + inp_file=inp + + #Parameter for how many hours to run the model + wn = wntr.network.WaterNetworkModel(inp) + node_names = wn.node_name_list + link_names = wn.link_name_list + + wntr_time=[] + + results_dict={} + + #Run model in wntr + # Are these lines necessary? + wn.options.time.hydraulic_timestep=dt + wn.options.time.report_timestep=dt + wn.options.time.duration=t_run*dt + + sim = wntr.sim.EpanetSimulator(wn) + + t1=time.time() + results = sim.run_sim() + t2=time.time() + + wntr_time.append(t2-t1) + + #Does not need to be in the loop since the model formulation does not vary by timestep + superjunctions, superlinks, orifices, pumps, prvs, H_bc, Q_in, pats, mult_df, tank_min, tank_max, tank_dict, time_controls_compiled, events_controls_pairs = pdu.wntr_2_pd(wn, t_run, dt) + + t3 = time.time() + mult_df['-'] = 0. + #mult_df.index = mult_df.index.tolist() + multipliers = mult_df + + #%% Run Model- Baseline + + # Specify number of internal links in each superlink and timestep size in seconds + internal_links = 1 + num_tanks=wn.num_tanks + u_o = np.ones(num_tanks) + u_p = np.ones(wn.num_pumps) + u_prv = np.ones(wn.num_valves) + + model = SuperLink(superlinks, superjunctions, + internal_links=internal_links, orifices=orifices, pumps=pumps, prvs = prvs, auto_permute=banded) + + is_tank = model.superjunctions['tank'].values + tank_min = model.superjunctions['tank_min'].values + tank_max = model.superjunctions['tank_max'].values + + #Initiate model + # Spin up model at small timestep to 'settle' initial states + + t3=time.time() + Q_in_t = -(model.superjunctions['demand_pattern'].map(multipliers.loc[0]).fillna(0.) * model.superjunctions['base_demand']).values + model.spinup(n_steps=10, dt=60, Q_in=Q_in_t, H_bc=H_bc, u_o=u_o, u_p=u_p, u_prv=u_prv, banded=banded) + t3_5=time.time() + + H = [] + Q = [] + Q_pump = [] + Q_prv = [] + Q_in_all = [] + t=[] + + #Run model for 24 hours + # While time is less than 24 hours + while model.t < (t_run * 3600): + + hour=model.t/3600 + j=int(np.floor(model.t//wn.options.time.pattern_timestep)) + Q_in_t = -(model.superjunctions['demand_pattern'].map(multipliers.loc[j]).fillna(0.) * model.superjunctions['base_demand']).values + Q_in_all.append(Q_in_t) + H_bc_t = H_bc + + # Set tank initial conditions + if use_tank_init_cond: + if model.t == 0: + H_bc_t[is_tank] = model.superjunctions['h_0'].values[is_tank] + model.superjunctions['z_inv'].values[is_tank] + model.bc[is_tank] = True + H_bc_t_0=H_bc_t + else: + model.bc[is_tank] = False + + # Tanks + u_o[:num_tanks] = ((model.H_j[is_tank] > tank_min[is_tank]) & (model.H_j[is_tank] < tank_max[is_tank])).astype(np.float64) + + # Event-based controls + for key in events_controls_pairs.keys(): + node = events_controls_pairs[key]['Node'] + link = events_controls_pairs[key]['Link'] + node_id = list(model.superjunctions.loc[model.superjunctions['name']==node,'id'])[0] + if link in wn.pump_name_list: + pump_id = model.pumps.loc[model.pumps['name']==link,'id'].values + + if model.H_j[node_id] > events_controls_pairs[key]['Upper lim']: + u_p[pump_id] = events_controls_pairs[key]['Upper lim stat'] + if model.H_j[node_id] < events_controls_pairs[key]['Lower lim']: + u_p[pump_id] = events_controls_pairs[key]['Lower lim stat'] + #Run model + model.step(dt=dt, H_bc = H_bc_t, Q_in=Q_in_t, u_o=u_o, u_p=u_p, u_prv=u_prv, + banded=banded, num_iter=num_iter, head_tol=0.0001) # initial conditions + + #Extract results at each timestep + H.append(model.H_j.copy()) + Q.append(model.Q_ik.copy()) + Q_pump.append(model.Q_p) + Q_prv.append(model.Q_prv) + #dem_source.append(Q_in_t[26].copy()) + t.append(model.t) + + t4=time.time() + + pd_time_spin= t3_5-t3 + pd_time_rest = t4-t3_5 + pd_time_tot = t4-t3 + + + H = np.vstack(H) + Q = np.vstack(Q) + Q_pump = np.vstack(Q_pump) + Q_prv = np.vstack(Q_prv) + #dem_source=np.vstack(dem_source) + t=np.vstack(t) + + + #Sample down the Q matrix to only every column from a new link, not each sub-link + #i.e. if there are 12 internal links in each superlink, then each of the first + #12 columns in q will basically be the same + + n_superlinks,x=superlinks.shape + Q_superlinks=Q[:,0:n_superlinks*internal_links:internal_links] + + #put H and Q into a dataframe + #Unscramble the head matrix + perm_inv = np.argsort(model.permutations) + H = H[:, perm_inv] + + #Do not use model.superjunctions because I want to use the head matrix in the same order + #as the original (unpermuted) superjunctions DF because then the columns correspond + #to the wntr results + H_df=pd.DataFrame(columns=superjunctions['name'],index=np.arange(0,t_run*3600,dt),data=H) + Q_df=pd.DataFrame(columns=model.superlinks['name'],index=np.arange(0,t_run*3600,dt),data=Q_superlinks) + + #Figure out which, if any, of the in/out nodes of the links got reversed + #due to changes in elevation + + from_node=model.superlinks['sj_0'].to_numpy() + + #Convert back to the nodes of the un-permuted superjunction indicies + from_node_orig=[] + for i in range(len(from_node)): + #find the index in perm_inv where it equals the value of from_node + from_node_orig.append(np.where(perm_inv==from_node[i])[0][0]) + from_node_orig=np.array(from_node_orig) + + #True means the nodes got flipped + flipped=superlinks['sj_0'].to_numpy()!=from_node_orig + + flip_mult=np.ones(len(flipped)) + flip_mult[flipped]=-1 + + Q_df=Q_df*flip_mult + + Q_in_all=np.vstack(Q_in_all) + Q_in_all=Q_in_all[:, perm_inv] + + Q_in_all_df=pd.DataFrame(Q_in_all,index=t.flatten()) + t5 = time.time() + + return H_df, Q_df, Q_pump, Q_prv, model, Q_in_all_df , pumps, superjunctions, orifices, superlinks, prvs + diff --git a/PRV tests/pipedream_utility.py b/PRV tests/pipedream_utility.py new file mode 100644 index 0000000..85eebc0 --- /dev/null +++ b/PRV tests/pipedream_utility.py @@ -0,0 +1,614 @@ +''' +This script extracts characteristics of the INP file +to be supplied to the pipedream_simulation script +''' +import numpy as np +import pandas as pd +import wntr +from scipy.optimize import curve_fit +import itertools + +def pump_func(x, a, b): + return a - b * x**2 + +def pattern_extrapolate(pattern, duration, pat_ts, dur_ts): + # if simulation time step is larger than pattern time step + duration = duration*3600/dur_ts + pattern = list(pattern) + if pat_ts > dur_ts: + + # match pattern time step to duration time step + mult = int(pat_ts/dur_ts) + #pattern = pattern * mult + pattern = list(itertools.chain.from_iterable(itertools.repeat(x, mult) for x in pattern)) + #print(pattern) + + # match pattern duration to simulation duration + if len(pattern) > duration: + pattern = pattern[:duration] + elif len(pattern) < duration: + num_repetitions = int(duration/len(pattern)) + extras = duration % len(pattern) + pattern_new = pattern.copy() + for i in range(num_repetitions-1): + pattern_new+=pattern + if extras!=0: + pattern_new.extend(pattern[:extras]) + pattern = pattern_new + + elif pat_ts < dur_ts: + + mult = int(dur_ts/pat_ts) + pattern = pattern[::mult] + + if len(pattern) > duration: + pattern = pattern[:duration] + elif len(pattern) < duration: + num_repetitions = int(duration/len(pattern)) + extras = duration % len(pattern) + pattern_new = pattern.copy() + for i in range(num_repetitions-1): + pattern_new+=pattern + if extras!=0: + pattern_new.append(pattern[:extras]) + pattern = pattern_new + + return pattern + +def wntr_2_pd(wn,t_run, dt): + + ######################################################################Create superjunctions dataframe from junctions + + superjunctions_m = pd.DataFrame(columns = ['name','id','z_inv','h_0','bc','storage','a','b','c', + 'max_depth','map_x','map_y','dem','tank','pat', 'tank_min', 'tank_max'], dtype=object) + superjunctions_m['name'] = wn.node_name_list + superjunctions_m['id'] = np.arange(0,len(wn.node_name_list),1) + + junction_names = wn.node_name_list + + sim = wntr.sim.EpanetSimulator(wn) + results = sim.run_sim() + + #Extract values for + base_dem = np.zeros(len(junction_names)) + ele = np.zeros(len(junction_names)) + x = np.zeros(len(junction_names)) + y = np.zeros(len(junction_names)) + initial_head = np.zeros(len(junction_names)) + bc = [False]*len(junction_names) + c = np.zeros(len(junction_names)) + dem = np.zeros(len(junction_names)) + tank = [False]*len(junction_names) + pat = [None]*len(junction_names) + has_tanks = 0 + num_tanks = 0 + tank_min = [-np.inf]*len(junction_names) + tank_max = [np.inf]*len(junction_names) + tank_dict = {} + max_dep = np.zeros(len(junction_names)) + pumps= {} + orifices = {} + + valve_info = {} + for valve_name, valve in wn.valves(): + valve_info[valve_name] = {'start': valve.start_node_name, + 'end': valve.end_node_name, + 'setting': valve.setting + wn.get_node(valve.end_node_name).elevation} + + for i in range(len(junction_names)): + + junction = wn.get_node(junction_names[i]) + j_type = junction.node_type + + if j_type == 'Junction': + + base_dem[i] = junction.base_demand + ele[i] = junction.elevation + # for valve_name in wn.valve_name_list: + # if junction_names[i] == valve_info[valve_name]['end']: + # ele[i] = valve_info[valve_name]['setting']-0.2 + if junction_names[i] == 'FP2_NU': # this node has negative pressures in the NWC model + ele[i] = min(results.node['head'].loc[:,'FP2_NU']) + initial_head[i] = 0 + bc[i] = False + c[i] = 1e-5 + tank[i] = False + dem[i] = junction.base_demand + if junction.demand_timeseries_list[0].pattern != None: + pat[i] = junction.demand_timeseries_list[0].pattern.name + else: + pat[i] = '-' + max_dep[i] = np.inf + + if j_type == 'Reservoir': + base_dem[i] = 0 +# if junction_names[i] == 'AM9001': +# ele[i] = min(results.node['head'].loc[:,'AM9001']) #319.1256 # to negate the effects of negative pressures in the model +# else: +# ele[i] = min(results.node['head'].loc[:,'FP9001']) # 331.9272 + ele[i] = min(results.node['head'].loc[:,junction_names[i]]) + initial_head[i] =0 + bc[i] = False + c[i] = 10**(9) #1 + tank[i] = False + dem[i] = 0 + pat[i] = '-' + max_dep[i] = np.inf + + if j_type == 'Tank': + has_tanks = 1 + base_dem[i] = 0 + ele[i] = junction.elevation + initial_head[i] = junction.init_level + bc[i] = False + c[i] = (junction.diameter/2)**2*np.pi + tank[i] = True + dem[i] = 0 + pat[i] = '-' + tank_min[i] = junction.elevation + junction.min_level + tank_max[i] = junction.elevation + junction.max_level + num_tanks += 1 + max_dep[i] = junction.max_level #np.inf + + x[i],y[i]=junction.coordinates + + superjunctions_m['z_inv'] = ele + superjunctions_m['h_0'] = initial_head + superjunctions_m['bc'] = bc + superjunctions_m['storage'] = 'functional' + superjunctions_m['a'] = 0 + superjunctions_m['b'] = 0 + superjunctions_m['c'] = c + superjunctions_m['max_depth'] = max_dep + superjunctions_m['map_x'] = x + superjunctions_m['map_y'] = y + superjunctions_m['tank'] = tank + superjunctions_m['dem'] = dem + superjunctions_m['pat'] = pat + superjunctions_m['tank_max'] = tank_max + superjunctions_m['tank_min'] = tank_min + if pat == None: + superjunctions_m['pat'] = '-' + + ######################################################################Create superlinks dataframe from links + + links = wn.pipe_name_list + + superlinks_m=pd.DataFrame(columns=['name','id', 'sj_0', 'sj_1', 'in_offset','out_offset', + 'dx','roughness','shape','g1', 'g2','g3','g4','Q_0', + 'h_0','ctrl','A_s','A_c','C', 'dx_uk', 'dx_dk', 'C_uk', 'C_dk', + 'friction_method'], dtype=object) + superlinks_m['name']=links + superlinks_m['id']=np.arange(0,len(links),1) + + start_node=[None]*len(links) + end_node=[None]*len(links) + + start_node_ind=[None]*len(links) + end_node_ind=[None]*len(links) + + length=np.zeros(len(links)) + diam=np.zeros(len(links)) + rough=np.zeros(len(links)) + + for i in range(len(links)): + link=wn.get_link(links[i]) + start_node[i]=link.start_node.name + end_node[i]=link.end_node.name + diam[i]=link.diameter + start_node_ind[i]=int(list(superjunctions_m['name']).index(start_node[i])) + end_node_ind[i]=int(list(superjunctions_m['name']).index(end_node[i])) + if links[i] in wn.valve_name_list: + rough[i] = 100 + length[i] = 25.4 + else: + rough[i] = link.roughness + length[i] = link.length + + + superlinks_m['dx'] = length + superlinks_m['roughness'] = rough + superlinks_m['shape'] = 'force_main' + superlinks_m['g1'] = diam + superlinks_m['g2'] = 0.00001 + superlinks_m['g3'] = 0 + superlinks_m['g4'] = 0 + superlinks_m['Q_0'] = 0 + superlinks_m['h_0'] = diam + superlinks_m['ctrl'] = False + superlinks_m['A_s'] = 0.001 + superlinks_m['A_c'] = 0 + superlinks_m['C'] = 0 + superlinks_m['in_offset'] = 0 + superlinks_m['out_offset'] = 0 + superlinks_m['sj_0'] = start_node_ind + superlinks_m['sj_1'] = end_node_ind + superlinks_m['dx_uk'] = 0 + superlinks_m['dx_dk'] = 0 + superlinks_m['C_uk'] = 0 + superlinks_m['C_dk'] = 0 + superlinks_m['friction_method'] = 'hw' + + # if there are tanks then do a bunch of stuff ################################################################## + orifices_count = 0 + #If there is a tank, create the orifices variable + if has_tanks==1: + orifices=pd.DataFrame(columns=['name','id','sj_0','sj_1','A','orientation','z_o','y_max','C'], dtype=object) + + for j in range(num_tanks): + superjunctions_new=pd.DataFrame(columns=['name','id','z_inv','h_0','bc','storage','a','b','c', + 'max_depth','map_x','map_y','dem','tank','pat', 'tank_min', 'tank_max'], + index=[superjunctions_m['id'].max()+1], dtype=object) + + + #For each tank, create another node + tank_junctions=superjunctions_m[superjunctions_m['tank']==True] + + #For each tank node, get the nodes on the other side of the links + r,c=tank_junctions.shape + + #################### + tank_row=tank_junctions.iloc[j,:] + + #Find which nodes are connected to that tank based on the superlinks DF + + tank_ind=tank_row['id'] + tank_dict[superjunctions_m['name'][tank_ind]] = {'id': tank_row['id'], + 'min level': orifices_count} + + orifices_count +=1 + + #Find in the superlinks where that node exists + con1=superlinks_m['sj_0']==tank_ind + con2=superlinks_m['sj_1']==tank_ind + tank_connected_links= superlinks_m[con1 | con2] + + link_index=tank_connected_links.index.to_list() + + #For each row of tank_connected_links create a new node with the invert of the + #not-tank node + r_tc,c_tc=tank_connected_links.shape + + + # another for loop for r_tc + #Get the node that isn't the tank + #Check the sj_0 node and if that is the same as the tank then get the other one + other_node=tank_connected_links['sj_0'].to_numpy()[0] + connect_col='sj_1' + + #If the tank is the sj_0 node then take the sj_1 node + if other_node==tank_ind: + other_node=tank_connected_links['sj_1'].to_numpy()[0] + connect_col='sj_0' + + #Add a new node to the superjunctions in the same location as tank node and + #and with invert of other node + superjunctions_new['name']='i'+superjunctions_m['name'][tank_ind] + superjunctions_new['id']=superjunctions_m['id'].max()+1 + superjunctions_new['z_inv']=superjunctions_m['z_inv'][superjunctions_m['id']==other_node].values[0] + superjunctions_new['h_0']=0 + superjunctions_new['bc']=False + superjunctions_new['storage']='functional' + superjunctions_new['a']=0 + superjunctions_new['b']=0 + superjunctions_new['c']=1e-5 + superjunctions_new['max_depth']= wn.get_node(superjunctions_m['name'][tank_ind]).elevation + wn.get_node(superjunctions_m['name'][tank_ind]).max_level #np.inf + superjunctions_new['map_x']=superjunctions_m['map_x'][superjunctions_m['id']==tank_ind].values[0] + superjunctions_new['map_y']=superjunctions_m['map_y'][superjunctions_m['id']==tank_ind].values[0] + superjunctions_new['tank']=False + superjunctions_new['dem']=0 + superjunctions_new['pat']='-' + superjunctions_new['tank_min']=-np.inf + superjunctions_new['tank_max']=np.inf + + # #Append superjunctions_new to the existing superjunctions dataframe + superjunctions_m=superjunctions_m.append(superjunctions_new) + + #Connect the link to the new node instead of the tank + superlinks_m.loc[link_index[0],connect_col]=superjunctions_new['id'].values[0] + + #Create an orifice connecting the existing tank to the new node + orifices_row=pd.DataFrame(columns=['name','id','sj_0','sj_1','A','orientation','z_o','y_max','C'],index=[j], dtype=object) + orifices_row['name']='o'+superjunctions_m['name'][tank_ind] + orifices_row['id']=j + orifices_row['sj_0']=tank_ind + orifices_row['sj_1']=superjunctions_m['id'].max() + orifices_row['A']=0.164 + orifices_row['orientation']='side' + orifices_row['z_o']=0 + orifices_row['y_max']=superlinks_m['g1'][link_index].values[0] + orifices_row['C']=0.67 + + orifices=orifices.append(orifices_row) + +# orifices = orifices.astype(orifices_row.dtypes.to_dict()) + + + #####################################################################Create PRVS dataframe + + prvs=pd.DataFrame(columns=['name','id','sj_0','sj_1','A','orientation','z_o','y_max','C_active', 'C_open', 'Hset'], dtype=object) + + for valve_name, valve in wn.valves(): + + if valve.valve_type == 'PRV': + + valve_index = wn.valve_name_list.index(valve_name) + #Create an orifice connecting the existing tank to the new node + prv_row=pd.DataFrame(columns=['name','id','sj_0','sj_1','A','orientation','z_o','y_max','C_active', 'C_open', 'Hset'],index=[valve_index], dtype=object) + prv_row['name']=valve_name + prv_row['id']=valve_index + prv_row['sj_0']=superjunctions_m['id'][superjunctions_m['name']==valve.start_node_name].values[0] + prv_row['sj_1']=superjunctions_m['id'][superjunctions_m['name']==valve.end_node_name].values[0] + prv_row['A']=0.164 + prv_row['orientation']='side' + prv_row['z_o']=0 + prv_row['y_max']= 0.1 # superlinks_m['g1'][link_index].values[0] ## + prv_row['C_active']=0.67 + prv_row['C_open']=0.67 + if valve_name == '~@RV-1': + prv_row['C_active'] = 0.3 + if valve_name == '33': + prv_row['C_open'] = 0.55 + prv_row['Hset']= wn.get_node(valve.end_node_name).elevation + valve.setting + + prvs=prvs.append(prv_row) + + + #If there is a pump, create the pumps variable + if wn.num_pumps > 0: + + pumps=pd.DataFrame(columns=['name','id','sj_0','sj_1','z_p','dH_min','dH_max','a_p','b_p','c_p'], dtype=object) + + for j in range(wn.num_pumps): + + pump_name = wn.pump_name_list[j] + pump = wn.get_link(pump_name) + + if len(pump.get_pump_curve().points) == 1: + A1, B1 = pump.get_pump_curve().points[0][0], pump.get_pump_curve().points[0][1] + A2, B2 = 0, 4*B1/3 + A3, B3 = 2*A1, 0 + xdata = [A1, A2, A3] + ydata = [B1, B2, B3] + +# + elif len(pump.get_pump_curve().points) == 3: + A1, B1 = pump.get_pump_curve().points[0][0], pump.get_pump_curve().points[0][1] + A2, B2 = pump.get_pump_curve().points[1][0], pump.get_pump_curve().points[1][1] + A3, B3 = pump.get_pump_curve().points[2][0], pump.get_pump_curve().points[2][1] + xdata = [A1, A2, A3] + ydata = [B1, B2, B3] + + else: + xdata = [] + ydata = [] + for k in range(len(pump.get_pump_curve().points)): + xdata.append(pump.get_pump_curve().points[k][0]) + ydata.append(pump.get_pump_curve().points[k][1]) + + popt, pcov = curve_fit(pump_func, xdata, ydata) + A, B = popt[0], popt[1] + + pumps_row=pd.DataFrame(columns=['name','id','sj_0','sj_1','z_p','dH_min','dH_max','a_p','b_p','c_p'],index=[j], dtype=object) + pumps_row['name']=pump_name + pumps_row['id']=j + pumps_row['sj_0']=superjunctions_m['id'][superjunctions_m['name']==pump.start_node_name].values[0] + pumps_row['sj_1']=superjunctions_m['id'][superjunctions_m['name']==pump.end_node_name].values[0] + pumps_row['z_p']=0. + pumps_row['dH_min']=0 + pumps_row['dH_max']=A + pumps_row['a_p']=A + pumps_row['b_p']=B + pumps_row['c_p']=2 + + pumps=pumps.append(pumps_row) + + + #Convert the data types of orifices/pumps to orifices_row/pumps_row to avoid problems later + + pumps = pumps.astype(pumps_row.dtypes.to_dict()) + ################# + + superjunctions = superjunctions_m + superlinks = superlinks_m + + #Create H_BC + # Modify for PRVs + H_bc=superjunctions['z_inv'].copy().to_numpy() + bc_val=superjunctions['bc'].values + flip_val=[not elem for elem in bc_val] + H_bc[flip_val]=0 + + + #Give the base demands for each node + # Constant demand input (cms) + Q_in = -superjunctions['dem'].to_numpy() + + #Get the multipliers from the wntr model + #Get the unique patterns from superjunctions + pats=superjunctions['pat'].to_list() + pats=list(np.unique(pats)) + pats.remove('-') + + mult=[] + for i in range(len(pats)): + + pat=wn.get_pattern(pats[i]) + mult_orig=pat.multipliers + + mult_loop=[] + if len(mult_orig)=t_run: + mult_loop=mult_orig #[:t_run] + mult_loop=np.array(mult_loop).reshape(-1,1) + if i==0: + mult=mult_loop + if i!=0: + mult=np.hstack((mult,mult_loop)) + + mult_df = pd.DataFrame(mult,columns=pats) + + # In[] Extract control rules and store in a dictionary + + time_controls_dict = {} + event_controls_dict = {} + time_controls_compiled = {} + events_controls_pairs = {} + time_controls_link_list = [] + event_controls_link_list = [] + + for i in range(len(wn.control_name_list)): + pattern = "'(.*?)'" + cont = wn.get_control(wn.control_name_list[i]) + cont_str = str(cont) + contr_list = cont_str.split() + + # time-based controls + if 'TIME' in cont_str: + link_name = contr_list[7] # link name + sim_time = contr_list[4] # time + stat = contr_list[8] # status? setting? + stat_val = contr_list[10] + + ctrl_name = 'Control {}'.format(i) + time_controls_dict[ctrl_name] = {'Link': link_name, + 'Time': sim_time, + 'Stat': stat, + 'Stat val': stat_val} + if link_name not in time_controls_link_list: + time_controls_link_list.append(link_name) + + # event-based controls + else: + node_name = contr_list[2] + link_name = contr_list[8] + node_level = contr_list[5] + sense = contr_list[4] + stat = contr_list[9] + stat_val = contr_list[11] + + event_controls_dict['Control {}'.format(i)] = {'Link': link_name, + 'Node': node_name, + 'Sense': sense, + 'Level': node_level, + 'Stat': stat, + 'Stat val': stat_val} + if link_name not in event_controls_link_list: + event_controls_link_list.append(link_name) + + + # store all time controls in one dict + for link in time_controls_link_list: + times_list_stat = [1- int(results.link['status'].loc[0,link])] + times_list_time = [0] + + for key in list(time_controls_dict.keys()): + if time_controls_dict[key]['Link'] == link: + dt_list = time_controls_dict[key]['Time'].split(":") + dt_time = int(dt_list[0])*3600 + int(dt_list[1])*60 + int(dt_list[2]) + time_cont = float(dt_time)/wn.options.time.report_timestep + if time_cont <= t_run: + if time_controls_dict[key]['Stat'] == 'status' or time_controls_dict[key]['Stat'] == 'STATUS': + if time_controls_dict[key]['Stat val'] == 'Open' or time_controls_dict[key]['Stat val'] == 'OPEN': # check if other stat vals are possible + times_list_stat.append(0) + else: + times_list_stat.append(1) + times_list_time.append(int(time_cont)) + + stat_array = np.zeros((t_run)) + + for i in range(len(times_list_stat)-1): + stat_array[times_list_time[i]:times_list_time[i+1]] = times_list_stat[i] + stat_array[times_list_time[-1]:] = times_list_stat[-1] + time_controls_compiled[link] = {'Status':stat_array} + + # store coupled event-based controls together + ev_pair_count = 0 + ev_pair_list = [] + + above_list = ['>', 'ABOVE'] + below_list = ['<', 'BELOW'] + closed_list = ['Closed', 'CLOSED'] + open_list = ['OPEN', 'Opened', 'Open'] + + for key in list(event_controls_dict.keys()): + for key_2 in list(event_controls_dict.keys()): + if key != key_2: + + dict1 = event_controls_dict[key] + dict2 = event_controls_dict[key_2] + + link, node = dict1['Link'], dict1['Node'] + link_2, node_2 = dict2['Link'], dict2['Node'] + + if link == link_2 and node == node_2: + if (link,node) not in ev_pair_list: + # add other possibilites? setting for each kind of valve + + # if status == link status + if dict1['Stat'] == 'status' or dict1['Stat'] == 'STATUS': + + val1 = wn.get_node(node).elevation + float(dict1['Level']) + val2 = wn.get_node(node).elevation + float(dict2['Level']) + init = str(wn.get_link(link).initial_status) # results.link['status'].loc[0, link] + if init == 'Closed' or init : + init_stat = 1 + if init == 'Open': + init_stat = 0 + + if dict1['Sense'] in above_list and dict1['Stat val'] in closed_list: + events_controls_pairs[ev_pair_count] = {'Link': link, + 'Node': node, + 'Upper lim': val1, + 'Lower lim': val2, + 'Upper lim stat': 0, # this is the value that will go to y + 'Lower lim stat': 1, + 'Link initial status': init_stat} # this is the value that will go to y0 + + if dict1['Sense'] in above_list and dict1['Stat val'] in open_list: #unlikely + events_controls_pairs[ev_pair_count] = {'Link': link, + 'Node': node, + 'Upper lim': val1, + 'Lower lim': val2, + 'Upper lim stat': 1, # this is the value that will go to y + 'Lower lim stat': 0, + 'Link initial status': init_stat} # this is the value that will go to y0 + + if dict1['Sense'] in below_list and dict1['Stat val'] in closed_list: #unlikely + events_controls_pairs[ev_pair_count] = {'Link': link, + 'Node': node, + 'Upper lim': val2, + 'Lower lim': val1, + 'Upper lim stat': 1, # this is the value that will go to y + 'Lower lim stat': 0, + 'Link initial status': init_stat} + + if dict1['Sense'] in below_list and dict1['Stat val'] in open_list: + events_controls_pairs[ev_pair_count] = {'Link': link, + 'Node': node, + 'Upper lim': val2, + 'Lower lim': val1, + 'Upper lim stat': 0, # this is the value that will go to y + 'Lower lim stat': 1, + 'Link initial status': init_stat} + + ev_pair_count += 1 + ev_pair_list.append((link,node)) + + + superjunctions = superjunctions.rename(columns={'dem' : 'base_demand', 'pat' : 'demand_pattern'}) + + if isinstance(pumps, dict): + if not pumps: + pumps = None + if isinstance(orifices, dict): + if not orifices: + orifices = None + if isinstance(prvs, dict): + if not prvs: + prvs = None + + return superjunctions, superlinks, orifices, pumps, prvs, H_bc, Q_in, pats, mult_df, tank_min, tank_max, tank_dict, time_controls_compiled, events_controls_pairs