2025, 46(2): 1-13.
doi: 10.13832/j.jnpe.2024.09.0027
Abstract:
Scientific computation is pivotal throughout the nuclear industry's technical framework, encompassing everything from the creation of nuclear databases to the design, analysis, validation, and operation of nuclear power engineering, as well as the reprocessing of nuclear fuel and the decommissioning of reactors. Historically, the scientific computing paradigm in the industrial field is mainly based on statistical methods for modeling experimental measurement data, as well as numerical computing methods represented by solving differential/integral equations. As artificial intelligence (AI) technology advances, leveraging AI for scientific computation is emerging as a novel paradigm. This paper introduces the basic principles and main features of this emerging technology field, focusing on the characteristics of the nuclear industry. It summarizes the current research work and analyzes the advantages and disadvantages of artificial intelligence scientific computing methods compared to traditional methods. The paper concludes with a prospective look at the future development trends of intelligent computation in the nuclear field, along with potential application scenarios, offering insights to foster the evolution of AI in scientific computation within the nuclear industry.
Scientific computation is pivotal throughout the nuclear industry's technical framework, encompassing everything from the creation of nuclear databases to the design, analysis, validation, and operation of nuclear power engineering, as well as the reprocessing of nuclear fuel and the decommissioning of reactors. Historically, the scientific computing paradigm in the industrial field is mainly based on statistical methods for modeling experimental measurement data, as well as numerical computing methods represented by solving differential/integral equations. As artificial intelligence (AI) technology advances, leveraging AI for scientific computation is emerging as a novel paradigm. This paper introduces the basic principles and main features of this emerging technology field, focusing on the characteristics of the nuclear industry. It summarizes the current research work and analyzes the advantages and disadvantages of artificial intelligence scientific computing methods compared to traditional methods. The paper concludes with a prospective look at the future development trends of intelligent computation in the nuclear field, along with potential application scenarios, offering insights to foster the evolution of AI in scientific computation within the nuclear industry.
2025, 46(2): 14-37.
doi: 10.13832/j.jnpe.2024.11.0148
Abstract:
The development of digital twin of nuclear reactor has the potential to enhance the safety and economic efficiency of nuclear power plants by achieving a cyber-physical fusion, while the key challenge of cyber-physical fusion is data fusion. Therefore, this paper focuses on the field of digital twin of nuclear reactor, starting from the definition of data fusion, fusion objects, fusion levels, fusion methods, and the relationship between digital twins and data fusion. Subsequently, the application and research status of data fusion methods in the entire life cycle of digital twin in nuclear reactor are discussed from eight perspectives: the construction of digital twin model of nuclear reactor, the optimization issues in the design and construction of nuclear reactor, the inversion and reconstruction of nuclear reactor operating parameters, the prediction of nuclear reactor operating parameters and remaining service life, the calibration of nuclear reactor operating parameters, the feedback and control of nuclear reactor operation, the fault detection, identification and diagnosis of nuclear reactor, and the data fusion of other aspects of digital twin of nuclear reactor. In conclusion, the challenges existing in current research has been identified from the aspects of data and fusion methods, providing references for addressing key data fusion issues in the future development of digital twin for nuclear reactor.
The development of digital twin of nuclear reactor has the potential to enhance the safety and economic efficiency of nuclear power plants by achieving a cyber-physical fusion, while the key challenge of cyber-physical fusion is data fusion. Therefore, this paper focuses on the field of digital twin of nuclear reactor, starting from the definition of data fusion, fusion objects, fusion levels, fusion methods, and the relationship between digital twins and data fusion. Subsequently, the application and research status of data fusion methods in the entire life cycle of digital twin in nuclear reactor are discussed from eight perspectives: the construction of digital twin model of nuclear reactor, the optimization issues in the design and construction of nuclear reactor, the inversion and reconstruction of nuclear reactor operating parameters, the prediction of nuclear reactor operating parameters and remaining service life, the calibration of nuclear reactor operating parameters, the feedback and control of nuclear reactor operation, the fault detection, identification and diagnosis of nuclear reactor, and the data fusion of other aspects of digital twin of nuclear reactor. In conclusion, the challenges existing in current research has been identified from the aspects of data and fusion methods, providing references for addressing key data fusion issues in the future development of digital twin for nuclear reactor.
2025, 46(2): 127-140.
doi: 10.13832/j.jnpe.2024.090039
Abstract:
The advantages of artificial intelligence (AI) algorithms in rapid prediction, self-learning, and strong generalizability have been applied to address the complexities of thermal-hydraulic phenomena and mechanisms in nuclear reactors. These applications include predictions of thermal-hydraulic parameters, optimization of thermal safety analysis codes, and enhancements in computational fluid dynamics (CFD) efficiency. This paper reviews the current state of research on AI algorithms in predicting thermal-hydraulic parameters such as flow regimes, boiling heat transfer, and critical flow. To address challenges such as unknown mechanisms and limited prediction ranges under extreme operating conditions, this study leverages the nonlinear rapid prediction capabilities of AI to expand the scope and accuracy of analyses. For thermal analysis codes constrained by parameter models, the self-learning, adaptive, and highly generalizable features of AI are utilized to improve the identification and prediction of complex phenomenon parameters through model calibration and data assimilation techniques. By employing model reduction and fast prediction methods, AI enhances the computational efficiency and the multidimensional reconstruction of complex thermal-hydraulic physical fields. Furthermore, the study highlights the future prospects of AI algorithms in accurately predicting the full lifecycle performance of key components in large-scale reactor systems, accelerating design iterations for advanced reactors such as liquid-metal fast reactors, and optimizing cross-scale, multiphysics interactions in a more efficient manner.
The advantages of artificial intelligence (AI) algorithms in rapid prediction, self-learning, and strong generalizability have been applied to address the complexities of thermal-hydraulic phenomena and mechanisms in nuclear reactors. These applications include predictions of thermal-hydraulic parameters, optimization of thermal safety analysis codes, and enhancements in computational fluid dynamics (CFD) efficiency. This paper reviews the current state of research on AI algorithms in predicting thermal-hydraulic parameters such as flow regimes, boiling heat transfer, and critical flow. To address challenges such as unknown mechanisms and limited prediction ranges under extreme operating conditions, this study leverages the nonlinear rapid prediction capabilities of AI to expand the scope and accuracy of analyses. For thermal analysis codes constrained by parameter models, the self-learning, adaptive, and highly generalizable features of AI are utilized to improve the identification and prediction of complex phenomenon parameters through model calibration and data assimilation techniques. By employing model reduction and fast prediction methods, AI enhances the computational efficiency and the multidimensional reconstruction of complex thermal-hydraulic physical fields. Furthermore, the study highlights the future prospects of AI algorithms in accurately predicting the full lifecycle performance of key components in large-scale reactor systems, accelerating design iterations for advanced reactors such as liquid-metal fast reactors, and optimizing cross-scale, multiphysics interactions in a more efficient manner.
2025, 46(2): 222-229.
doi: 10.13832/j.jnpe.2024.090025
Abstract:
The detection of abnormal conditions during the operation of nuclear power plant units mainly relies on threshold alarm information from the Digital Control System (DCS), with a lack of trend analysis. This paper investigates the establishment of logical relationships between variables through event logic, and based on this, employs an Auto-Associative Neural Network (AANN) model for anomaly detection of correlated variables. Finally, it uses the Empirical Mode Decomposition (EMD) trend extraction algorithm and the Adaptive Sliding Window Holt Linear Trend (HOLT) model to predict abnormal variables. This approach can provide early warnings for shutdown and reactor trip events, enabling plant operators to detect and resolve issues earlier, thus improving the operational safety of nuclear power plants. Testing experiments were conducted using both simulated data and actual unit anomaly data. The results from real data experiments show a Mean Squared Error (MSE) of 0.1 and a Goodness of Fit (R2) of 0.99, with at least 1 hour of advance warning before shutdown actions. This confirms the accuracy and early warning capabilities of the proposed AANN-HOLT warning algorithm.
The detection of abnormal conditions during the operation of nuclear power plant units mainly relies on threshold alarm information from the Digital Control System (DCS), with a lack of trend analysis. This paper investigates the establishment of logical relationships between variables through event logic, and based on this, employs an Auto-Associative Neural Network (AANN) model for anomaly detection of correlated variables. Finally, it uses the Empirical Mode Decomposition (EMD) trend extraction algorithm and the Adaptive Sliding Window Holt Linear Trend (HOLT) model to predict abnormal variables. This approach can provide early warnings for shutdown and reactor trip events, enabling plant operators to detect and resolve issues earlier, thus improving the operational safety of nuclear power plants. Testing experiments were conducted using both simulated data and actual unit anomaly data. The results from real data experiments show a Mean Squared Error (MSE) of 0.1 and a Goodness of Fit (R2) of 0.99, with at least 1 hour of advance warning before shutdown actions. This confirms the accuracy and early warning capabilities of the proposed AANN-HOLT warning algorithm.
2018, 39(5): 1-3.
doi: 10.13832/j.jnpe.2018.05.0001
摘要：
主要介绍了我国在建、在运核电机组的基本状况和最新进展,以及我国在提升核设施安全水平方面的相关措施。在国家能源局印发的《能源技术创新“十三五”规划》要求之下,我国推出一系列先进核能和小型堆的发展计划,开展了“海洋核动力平台示范工程建设”并建立相关标准。最后总结了中国核电目前面临的挑战和未来的展望。
主要介绍了我国在建、在运核电机组的基本状况和最新进展,以及我国在提升核设施安全水平方面的相关措施。在国家能源局印发的《能源技术创新“十三五”规划》要求之下,我国推出一系列先进核能和小型堆的发展计划,开展了“海洋核动力平台示范工程建设”并建立相关标准。最后总结了中国核电目前面临的挑战和未来的展望。
2019, 40(4): 1-8.
摘要：
热管冷却反应堆采用固态反应堆设计理念，通过热管非能动方式导出堆芯热量。本文总结了热管冷却反应堆的概念初创、积极探索、重大突破的发展历程；分析了热管冷却反应堆的技术特点，包括固态属性、固有安全性高、运行特性简单、易于模块化与易扩展和运输特性良好等核心优势；归纳了热管冷却反应堆中热管性能、材料工艺、能量转换等技术现状，并提出热管冷却反应堆进一步发展将面临的材料、制造工艺、运行可维护性等挑战，从而明确了热管冷却反应堆未来的发展趋势，为革新型热管冷却反应堆技术的发展与应用提供良好的方向指引。总体而言，热管冷却反应堆在深空探测与推进、陆基核电源、深海潜航探索等场景中具有广阔的应用前景，有可能成为改变未来核动力格局的颠覆性技术之一。
热管冷却反应堆采用固态反应堆设计理念，通过热管非能动方式导出堆芯热量。本文总结了热管冷却反应堆的概念初创、积极探索、重大突破的发展历程；分析了热管冷却反应堆的技术特点，包括固态属性、固有安全性高、运行特性简单、易于模块化与易扩展和运输特性良好等核心优势；归纳了热管冷却反应堆中热管性能、材料工艺、能量转换等技术现状，并提出热管冷却反应堆进一步发展将面临的材料、制造工艺、运行可维护性等挑战，从而明确了热管冷却反应堆未来的发展趋势，为革新型热管冷却反应堆技术的发展与应用提供良好的方向指引。总体而言，热管冷却反应堆在深空探测与推进、陆基核电源、深海潜航探索等场景中具有广阔的应用前景，有可能成为改变未来核动力格局的颠覆性技术之一。
2019, 40(6): 1-6.
doi: 10.13832/j.jnpe.2019.06.0001
摘要：
放射性废液得到有效处理是世界各国核工业迅猛发展的前提，其关键技术的现状和发展方向也是我国核工业界关注的焦点。本文介绍了几种放射性废液处理的传统方法及涌现出的新技术，概述了各种方法的原理及优、缺点，同时讨论了放射性废液处理技术今后的研究方向及发展趋势。
放射性废液得到有效处理是世界各国核工业迅猛发展的前提，其关键技术的现状和发展方向也是我国核工业界关注的焦点。本文介绍了几种放射性废液处理的传统方法及涌现出的新技术，概述了各种方法的原理及优、缺点，同时讨论了放射性废液处理技术今后的研究方向及发展趋势。
2023, 44(5): 1-5.
doi: 10.13832/j.jnpe.2023.05.0001
摘要：
“碳达峰、碳中和”目标的提出对我国未来能源体系发展具有深远影响。核能作为稳定的清洁能源，对于“碳达峰、碳中和”目标实现能够发挥更大作用，在发电、供热、制氢等领域均有着巨大的应用前景和需求。经过60余年发展，核能建立了完善的产业链，研发形成了“华龙一号”等具有完全自主知识产权的第三代大型商业压水堆核电技术品牌，研发了具有国际先进水平的多用途模块式小型堆“玲龙一号”，积极探索了钠冷快堆、超高温气冷堆、熔盐堆等第四代先进核能技术，持续开展聚变核能利用。同时我国核能发展也面临一些挑战，先进核能技术亟需突破。本文提出了先进核能技术的发展思路和路径，从在役核电厂智能化运行管理、三代核电批量化部署、固有安全快堆技术研发、积极研发满足高效制氢需求的超高温气冷堆、积极探索能够满足工业供热和平台供电的模块式小型堆技术、国内国际合作发展先进核能关键技术等6个方面进行了展望，为我国先进核能技术的发展给出了具体的研究目标与方向。
“碳达峰、碳中和”目标的提出对我国未来能源体系发展具有深远影响。核能作为稳定的清洁能源，对于“碳达峰、碳中和”目标实现能够发挥更大作用，在发电、供热、制氢等领域均有着巨大的应用前景和需求。经过60余年发展，核能建立了完善的产业链，研发形成了“华龙一号”等具有完全自主知识产权的第三代大型商业压水堆核电技术品牌，研发了具有国际先进水平的多用途模块式小型堆“玲龙一号”，积极探索了钠冷快堆、超高温气冷堆、熔盐堆等第四代先进核能技术，持续开展聚变核能利用。同时我国核能发展也面临一些挑战，先进核能技术亟需突破。本文提出了先进核能技术的发展思路和路径，从在役核电厂智能化运行管理、三代核电批量化部署、固有安全快堆技术研发、积极研发满足高效制氢需求的超高温气冷堆、积极探索能够满足工业供热和平台供电的模块式小型堆技术、国内国际合作发展先进核能关键技术等6个方面进行了展望，为我国先进核能技术的发展给出了具体的研究目标与方向。
2019, 40(3): 103-108.
doi: 10.13832/j.jnpe.2019.03.0103
摘要：
以配置四取中逻辑输入模块的核电厂稳压器数字压力控制装置为研究对象，建立其故障树模型，包括四取中逻辑的动态部分和其他设备的静态部分，采用马尔科夫方法分析动态部分，再根据逻辑关系分析整体故障树，最后，围绕可靠度和重要度评价四取中逻辑的可靠性及其对整个装置可靠性的提升效果，结果表明：四取中逻辑在可靠性方面优化程度相对较高。
以配置四取中逻辑输入模块的核电厂稳压器数字压力控制装置为研究对象，建立其故障树模型，包括四取中逻辑的动态部分和其他设备的静态部分，采用马尔科夫方法分析动态部分，再根据逻辑关系分析整体故障树，最后，围绕可靠度和重要度评价四取中逻辑的可靠性及其对整个装置可靠性的提升效果，结果表明：四取中逻辑在可靠性方面优化程度相对较高。
2019, 40(1): 1-7.
doi: 10.13832/j.jnpe.2019.01.0001
摘要：
“华龙一号”是我国自主设计研发的具有完整知识产权的第三代百万千瓦级压水堆核电技术。本文介绍了“华龙一号”的产生历程，系统论述了“华龙一号”反应堆堆芯与安全设计特点，包括“华龙一号”研发过程中开展的堆芯核设计、热工水力设计、安全设计、设计验证及“华龙一号”持续开展的设计改进与优化等内容，通过采用新的设计理念和设计技术，全面提高了“华龙一号”作为三代核电技术的经济性、灵活性和安全性。
“华龙一号”是我国自主设计研发的具有完整知识产权的第三代百万千瓦级压水堆核电技术。本文介绍了“华龙一号”的产生历程，系统论述了“华龙一号”反应堆堆芯与安全设计特点，包括“华龙一号”研发过程中开展的堆芯核设计、热工水力设计、安全设计、设计验证及“华龙一号”持续开展的设计改进与优化等内容，通过采用新的设计理念和设计技术，全面提高了“华龙一号”作为三代核电技术的经济性、灵活性和安全性。
2020, 41(5): 132-137.
摘要：
为解决核电厂传统监测手段的局限性，提出将核主元分析法（KPCA）引入核电厂设备在线监测领域中，并设计了监测模型建设方法以及在线监测策略。为验证算法的有效性，将其应用在国内某核电机组电动主给水泵的真实监测案例中。仿真结果表明，KPCA算法可适应核电厂设备监测的要求，能比现有阈值监测手段提供更为早期的故障预警。同时，相比于常规的主元分析法（PCA），KPCA算法能够提取各变量之间的非线性关系，识别出设备不同的运行模式，有效减少误报警。
为解决核电厂传统监测手段的局限性，提出将核主元分析法（KPCA）引入核电厂设备在线监测领域中，并设计了监测模型建设方法以及在线监测策略。为验证算法的有效性，将其应用在国内某核电机组电动主给水泵的真实监测案例中。仿真结果表明，KPCA算法可适应核电厂设备监测的要求，能比现有阈值监测手段提供更为早期的故障预警。同时，相比于常规的主元分析法（PCA），KPCA算法能够提取各变量之间的非线性关系，识别出设备不同的运行模式，有效减少误报警。
2022, 43(2): 246-253.
doi: 10.13832/j.jnpe.2022.02.0246
摘要：
为了对核电厂主泵的运行过程进行监测和追踪，进而提高主泵的预警能力，提出了基于差分自回归移动平均（ARIMA）和长短期记忆（LSTM）神经网络组合模型的主泵状态预测方法，并用该方法对某核电厂主泵止推轴承温度和可控泄漏流量进行单步和多步预测，以根均方误差（RMSE）为指标对预测精度进行评估。结果表明，所建立的ARIMA和LSTM神经网络组合模型能够对主泵的状态进行准确的预测和追踪，并且组合模型的预测精度要优于ARIMA和LSTM单一模型，尤其在多步预测中，组合模型的优势更加明显。
为了对核电厂主泵的运行过程进行监测和追踪，进而提高主泵的预警能力，提出了基于差分自回归移动平均（ARIMA）和长短期记忆（LSTM）神经网络组合模型的主泵状态预测方法，并用该方法对某核电厂主泵止推轴承温度和可控泄漏流量进行单步和多步预测，以根均方误差（RMSE）为指标对预测精度进行评估。结果表明，所建立的ARIMA和LSTM神经网络组合模型能够对主泵的状态进行准确的预测和追踪，并且组合模型的预测精度要优于ARIMA和LSTM单一模型，尤其在多步预测中，组合模型的优势更加明显。
2019, 40(1): 74-77.
doi: 10.13832/j.jnpe.2019.01.0074
摘要：
介绍了中广核研究院在事故容错燃料（ATF）包壳领域的最新成果，通过预置粉末式脉冲激光熔覆技术，在不同的功率下制备出不同厚度的锆包壳管Cr保护层；通过高温蒸汽氧化增重数据发现，采用半导体脉冲激光熔覆技术、脉冲激光功率50~60 W、螺距0.8~0.9 mm、角速度10°/s等参数条件下制备Cr涂层可以获得较好的抗高温氧化性能，证明保护的效果直接受涂层质量控制。通过SEM分析了涂层的显微结构，采用扩散机理解释了Cr涂层在1200℃下与锆合金基体相容性良好的原因。
介绍了中广核研究院在事故容错燃料（ATF）包壳领域的最新成果，通过预置粉末式脉冲激光熔覆技术，在不同的功率下制备出不同厚度的锆包壳管Cr保护层；通过高温蒸汽氧化增重数据发现，采用半导体脉冲激光熔覆技术、脉冲激光功率50~60 W、螺距0.8~0.9 mm、角速度10°/s等参数条件下制备Cr涂层可以获得较好的抗高温氧化性能，证明保护的效果直接受涂层质量控制。通过SEM分析了涂层的显微结构，采用扩散机理解释了Cr涂层在1200℃下与锆合金基体相容性良好的原因。
2019, 40(4): 25-28.
摘要：
针对船用核动力装置中止回阀的泄漏问题，利用流固热耦合仿真方法研究了温度快速变化对止回阀的影响，结果表明：止回阀的等效应力和变形量随温度的降低而降低；密封压垫和四合环最大等效应力位于阀门管道两端，最大变形量位于阀门前后部位；密封压垫的最大变形量和收缩率都比四合环大；由于高温高压的作用，密封压垫与阀盖之间产生了明显的间隙，易发生泄漏，且该间隙随温度的降低而扩大，可能加剧泄漏。
针对船用核动力装置中止回阀的泄漏问题，利用流固热耦合仿真方法研究了温度快速变化对止回阀的影响，结果表明：止回阀的等效应力和变形量随温度的降低而降低；密封压垫和四合环最大等效应力位于阀门管道两端，最大变形量位于阀门前后部位；密封压垫的最大变形量和收缩率都比四合环大；由于高温高压的作用，密封压垫与阀盖之间产生了明显的间隙，易发生泄漏，且该间隙随温度的降低而扩大，可能加剧泄漏。
2022, 43(1): 1-6.
doi: 10.13832/j.jnpe.2022.01.0001
Abstract:
2020, 41(4): 1-7.
Abstract:
2023, 44(1): 1-8.
doi: 10.13832/j.jnpe.2023.01.0001
Abstract:
2021, 42(4): 1-5.
doi: 10.13832/j.jnpe.2021.04.0001
Abstract:
2019, 40(6): 1-6.
doi: 10.13832/j.jnpe.2019.06.0001
Abstract:
2022, 43(4): 1-4.
doi: 10.13832/j.jnpe.2022.04.0001
Abstract:
2019, 40(4): 1-8.
Abstract:
2022, 43(2): 1-8.
doi: 10.13832/j.jnpe.2022.02.0001
Abstract:
2023, 44(2): 1-8.
doi: 10.13832/j.jnpe.2023.02.0001
Abstract:
2018, 39(3): 6-9.
doi: 10.13832/j.jnpe.2018.03.0006
Abstract:
