Molten-Salt-Fast-Reactor-Simulation

This project models a Molten Salt Fast Reactor (MSFR) and provides tools for comprehensive analysis and prediction, including:

• Linear stability analysis
• Non-linear time-domain modeling
• System control modeling
• Machine learning prediction

Stability Analysis

We analyze the eigenvalues of the MSFR dynamic matrix to assess system stability under variations of:

• Fuel temperature feedback (α_fuel)
• Mass flow rate
• Reactor power

Non-Linear Model

Time-domain simulation of reactor dynamics including:

• Delayed neutron groups
• Temperature feedback
• Circulating fuel effects

System Control

Basic control strategies for regulating reactor behavior.

Machine Learning Prediction

Exploration of ML techniques for predicting reactor state evolution.

Governing Physics

The model includes standard reactor kinetics parameters:

• Delayed neutron fractions (βᵢ)
• Decay constants (λᵢ)
• Neutron generation time (Λ)
• Temperature reactivity feedback (αₜ)

Dependencies

• numpy
• scipy
• matplotlib
• scikit-learn (if ML section used)