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- W4296616430 abstract "We present a study for particle transport design optimization that combines OpenMC (Monte Carlo transport code) with NEORL (NeuroEvolution Optimization with Reinforcement Learning). The proposed open-source framework, OpenNeoMC, empowers particle transport with state-of-the-art optimization techniques to facilitate design optimization and efficient selection of the simulation settings. The new framework is designed to have great concision and usability in integrating OpenMC into a variety of optimization techniques such as differential evolution, evolution strategies, bat algorithm, ensemble of differential evolution variants, and others. We present two optimization applications, namely criticality search of small modular reactor (SMR) and maximum keff search of a nuclear assembly. The deviation from criticality found by OpenNeoMC can be as small as the order of 1 pcm in the criticality search of SMR. For the second problem, an optimal geometric configuration of the nuclear assembly is found efficiently. The results demonstrate that the framework can be a powerful open-source tool to solve large-scale optimization problems in expensive particle transport simulations, and scalable enough to other Monte Carlo codes." @default.
- W4296616430 created "2022-09-23" @default.
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- W4296616430 date "2023-01-01" @default.
- W4296616430 modified "2023-09-27" @default.
- W4296616430 title "OpenNeoMC: A framework for design optimization in particle transport simulations based on OpenMC and NEORL" @default.
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- W4296616430 doi "https://doi.org/10.1016/j.anucene.2022.109450" @default.
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