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<p>Amidst the growing global emphasis on nuclear safety, the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events. Moreover, the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy. However, data-driven deep learning techniques often lack interpretability, which hinders their applicability in the nuclear energy sector. To address this problem, this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core. Innovative techniques, such as regional decomposition, intelligent <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(k_\textrm{eff}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>k</mi> <mtext>eff</mtext> </msub> </math></EquationSource> </InlineEquation> (effective multiplication factor) search, and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(k_\textrm{eff}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>k</mi> <mtext>eff</mtext> </msub> </math></EquationSource> </InlineEquation> inversion, have been introduced for the calculation. Furthermore, hyperparameters of the model are automatically optimized using a whale optimization algorithm. A series of computational examples are used to validate the proposed model, demonstrating its applicability, generality, and high accuracy in calculating the neutron flux within the nuclear reactor. The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future, including reactor digital twinning. This approach is data-light, requires little to no training data, and still delivers remarkably precise output data.</p>

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Application research of a hybrid data- and knowledge-driven artificial intelligence scientific computing model in neutron diffusion calculation for nuclear reactors

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Fu-Lin Zeng,
  • Xiao-Long Zhang,
  • Peng-Cheng Zhao,
  • Zi-Jing Liu

摘要

Amidst the growing global emphasis on nuclear safety, the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events. Moreover, the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy. However, data-driven deep learning techniques often lack interpretability, which hinders their applicability in the nuclear energy sector. To address this problem, this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core. Innovative techniques, such as regional decomposition, intelligent \(k_\textrm{eff}\) k eff (effective multiplication factor) search, and \(k_\textrm{eff}\) k eff inversion, have been introduced for the calculation. Furthermore, hyperparameters of the model are automatically optimized using a whale optimization algorithm. A series of computational examples are used to validate the proposed model, demonstrating its applicability, generality, and high accuracy in calculating the neutron flux within the nuclear reactor. The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future, including reactor digital twinning. This approach is data-light, requires little to no training data, and still delivers remarkably precise output data.