Reactor Optimization Benchmark by Reinforced Learning

D. Schwarcz, N. Schneider, G. Oren, U. Steinitz

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays more readily done, but design and optimization with multiple parameters is still a computational challenge. MC transport simulations, coupled with machine learning techniques, offer promising avenues for enhancing the efficiency and effectiveness of nuclear reactor optimization. This paper introduces a novel benchmark problem within the OpenNeoMC framework designed specifically for reinforcement learning. The benchmark involves optimizing a unit cell of a research reactor with two varying parameters (fuel density and water spacing) to maximize neutron flux while maintaining reactor criticality. The test case features distinct local optima, representing different physical regimes, thus posing a challenge for learning algorithms. Through extensive simulations utilizing evolutionary and neuroevolutionary algorithms, we demonstrate the effectiveness of reinforcement learning in navigating complex optimization landscapes with strict constraints. Furthermore, we propose acceleration techniques within the OpenNeoMC framework, including model updating and cross-section usage by RAM utilization, to expedite simulation times. Our findings emphasize the importance of machine learning integration in reactor optimization and contribute to advancing methodologies for addressing intricate optimization challenges in nuclear engineering. The sources of this work are available at our GitHub repository: RLOpenNeoMC.

    Original languageEnglish
    Article number17009
    JournalEPJ Web of Conferences
    Volume302
    DOIs
    StatePublished - 15 Oct 2024
    Event2024 Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, SNA + MC 2024 - Paris, France
    Duration: 20 Oct 202424 Oct 2024

    ASJC Scopus subject areas

    • General Physics and Astronomy

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