This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with conventional simulation techniques. The ML model is trained to identify and correct errors in low-fidelity simulation outputs, traditionally derived from core physics computations. These corrections aim to align the low-fidelity results closely with high-fidelity data. Precise predictions of nuclear reactor parameters like core eigenvalue and power distribution are crucial for efficient fuel management and adherence to technical specifications. Current high-fidelity transport calculations, while accurate, are impractical for real-time predictions due to extensive computational demands. Our approach, therefore, utilizes the standard two-step simulation process-assembly-level lattice physics calculations followed by whole-core nodal diffusion computations-to generate initial results, which are then refined using the ML-based error correction model. The methodology focuses on improving simulation accuracy in regular BWR operations rather than developing a universal ML predictor for reactor physics. By training an advanced neural network model on the difference in high-fidelity and low-fidelity simulations, the model can reduce the nodal power error from low-fidelity simulations to around 1% on average and the core eigenvalue down to under 100 pcm. This result is under the condition of the normal variations of control rod pattern and core flow rate changes in standard BWR operations used in the training and evaluation of the machine learning model. This work suggests a promising approach for achieving more accurate, computationally feasible simulation solutions in nuclear reactor operation and management.
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http://dx.doi.org/10.1038/s41598-024-56388-5 | DOI Listing |
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Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
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Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
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Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, China.
Risk-taking is a concerning yet prevalent issue during adolescence and can be life-threatening. Examining its etiological sources and evolving pathways helps inform strategies to mitigate adolescents' risk-taking behavior. Studies have found that unfavorable environmental factors, such as adverse childhood experiences (ACEs), are associated with momentary levels of risk-taking in adolescents, but little is known about whether ACEs shape the developmental trajectory of risk-taking.
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