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Simulation-to-reality transferability framework for operating-parameter forecasting in nuclear reactors using domain adaptation
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AI
DOI：10.1007/s41365-025-01643-1.png)
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Abstract
En 中文
Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry. Owing to economic and security concerns, a common method is to train data generated by simulators. However, achieving a satisfactory performance in practical applications is difficult because simulators imperfectly emulate reality. To bridge this gap, we propose a novel framework called simulation-to-reality domain adaptation (SRDA) for forecasting the operating parameters of nuclear reactors. The SRDA model employs a transformer-based feature extractor to capture dynamic characteristics and temporal dependencies. A parameter predictor with an improved logarithmic loss function is specifically designed to adapt to varying reactor powers. To fuse prior reactor knowledge from simulations with reality, the domain discriminator utilizes an adversarial strategy to ensure the learning of deep domain-invariant features, and the multiple kernel maximum mean discrepancy minimizes their discrepancies. Experiments on neutron fluxes and temperatures from a pressurized water reactor illustrate that the SRDA model surpasses various advanced methods in terms of predictive performance. This study is the first to use domain adaptation for real-world reactor prediction and presents a feasible solution for enhancing the transferability and generalizability of simulated data.
Keywords:
Nuclear power plant (NPP)
Pressurized water reactor (PWR)
Domain adaptation
Knowledge transfer
Transformer
Forecasting
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