Optimization and Analysis of Micro Modular Reactors (MMR) Using Computational Intelligence and Python Automation

Resumo

The development of Micro Modular Reactors (MMR) represents a significant step towards decentralized and sustainable energy generation. However, the complexity of their design and operation necessitates sophisticated analytical and optimization approaches. This work explores the application of Computational Intelligence Techniques to solve engineering problems inherent to MMR, focusing on the optimization of crucial parameters.
A methodology is proposed that integrates Python automation for processing and analyzing nuclear simulation data with the application of Artificial Intelligence (AI), specifically optimization algorithms. Python automation facilitates interaction with neutronic calculation codes and the efficient handling of large volumes of data, while AI techniques are employed to explore the design space and identify optimal configurations for the reactor.
The research aims to demonstrate how the combination of these computational tools can enhance safety analysis, fuel utilization efficiency, and power distribution in MMR. The application of AI-based optimization algorithms allows for the search for solutions that would be unfeasible or excessively costly with traditional methods.
This study highlights the relevance of Computational Intelligence Techniques for Solving Engineering Problems in the context of nuclear energy, offering a robust framework for the advanced design and analysis of MMR, contributing to the development of safer, more efficient, and optimized energy systems.