Cognitive Supernova Generator (CSG)

Fusion–Fission Hybrid Reactor — Open Neutronics Validation

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Overview

The Cognitive Supernova Generator (CSG) is a compact fusion–fission hybrid reactor concept designed to demonstrate tritium self-sufficiency using natural materials through geometry and neutron economy rather than isotopic enrichment.

This repository publishes OpenMC neutronics models, analysis scripts, and reference results to enable independent verification and technical discussion.

This is not a commercial product, funding request, or operational reactor design.
It is an open technical disclosure for scientific review.

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Core Concept

CSG combines:

• Distributed D–T fusion neutron sources
• A subcritical uranium blanket
• A lithium-based tritium breeding region
• Geometry-optimized neutron utilization

Primary goal:
Achieve TBR ≥ 1.05 using natural lithium (7.5% Li-6) and natural uranium (0.711% U-235).

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Primary Verification Run (Natural Materials)

Main script

icosahedron_natural_materials.py

This script executes an OpenMC fixed-source neutronics model with:

• 32 distributed fusion neutron sources
• Truncated icosahedral (soccer-ball) source geometry
• Source radius ≈ 340 cm
• Natural lithium breeder (7.5% Li-6, no enrichment)
• Natural uranium where applicable
• No fissile or lithium enrichment beyond natural abundance

Purpose of this run

• Demonstrate tritium breeding above unity without enrichment
• Validate geometry-driven neutron economy
• Serve as the reference verification case for the CSG concept

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Reference Output

An example output from the primary run is included:

icosahedron_natural_results.json

This file records:

• Total Tritium Breeding Ratio (TBR)
• Li-6 and Li-7 reaction contributions
• Monte Carlo statistical uncertainty
• Key geometry and material parameters

Independent executions of the primary script should reproduce results within Monte Carlo uncertainty, assuming equivalent OpenMC versions and nuclear data libraries.

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Geometry & Optimization Studies

Additional scripts explore:

• Spherical vs icosahedral source placement
• Source radius optimization
• Fast scouting vs high-fidelity validation

Representative files include:

• sphere_vs_icosahedron_CORRECTED.py
• automated_radius_survey.py
• analyze_geometry_comparison.py

These studies show that source geometry and placement dominate performance, often exceeding the impact of enrichment.

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Analysis & Post-Processing

Included analysis scripts extract and evaluate:

• Tritium production pathways
• Fission contribution and neutron multiplication
• Geometry performance comparisons
• Cost implications of enrichment vs natural materials

Results are written to human-readable JSON for transparency and reuse.

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Reproducibility (High Level)

1. Install Python and OpenMC
2. Configure nuclear data libraries
3. Run:

   python3 icosahedron_natural_materials.py
   openmc
   

4. Review printed output and generated JSON results

Exact numerical agreement is not required; agreement within statistical uncertainty is expected.

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Scope & Intent

This repository exists to:

• Enable independent verification and critique
• Publish reproducible neutronics artifacts
• Explore cost-reduction pathways for fusion–fission hybrids
• Support peaceful, humanitarian, and environmental applications

It does not include engineering design, safety cases, licensing documentation, or deployment guidance.

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License

Released under an open license.
See LICENSE.md for full terms.

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Author

Frank Cooper
Energy Systems Researcher
December 2025