Annals of Nuclear Energy最新文献

Reliability of passive safety system in nuclear power plants: advances, emerging technologies, and persistent challenges 核电厂被动安全系统的可靠性：进展、新兴技术和持续挑战

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-02-03 DOI: 10.1016/j.anucene.2026.112161

Shamsuddeen Lawal , Chenyang Wang , Minjun Peng

The global imperative for decarbonization has reaffirmed the nuclear energy’s role as a low-carbon baseload source, contingent on robust safety assurances. The shift toward Passive System System (PSS), which utilize natural phenomena like gravity and natural circulation enhances resilience but poses unique reliability challenges. Conventional Probabilistic Risk Assessment (PRA) inadequately models functional failure, where performance degrades due to uncertain physical phenomena despite all components operational, and struggles with dominant epistemic uncertainties in novel designs. This review synthesizes methodological advances, tracing the evaluation from computationally intensive first-generation framework (e.g., RMPS/ASPRA) to machine learning-driven paradigm integrating AI-based surrogate models (e.g., Kriging, Polynomial Chaos Expansion, Physics-Informed Neural Networks). These enable efficient quantification of functional failure probabilities, epistemic uncertainty mapping via Bayesian and adaptive sampling, and revelation of time-dependent risk pathways via Dynamic PRA (DPRA) invisible to static methods. However, the irreplaceable role of machine learning in addressing computational bottleneck introduces new issues, including “black-box” opacity, regulatory challeges for licensing, hybrid active–passive system integration, data scarcity for Gene III+, SMR, Gen-IV designs, and long-term material degradation effects. We conclude that PSS reliability hinges on Explainable AI (XAI) to demystify models, standardized validation protocol, integrated cyber-physical-security framework. This transformation, particularly through Physics-Informed Machine Learning tools like PINNs, is essential to generate the rigorous, regulatory-acceptance evidence needed for licensing and deploying advanced reactors.

全球对脱碳的迫切需求再次确认了核能作为低碳基本负荷来源的作用，这取决于强有力的安全保证。向被动系统系统（PSS）的转变，利用重力和自然循环等自然现象增强了弹性，但也带来了独特的可靠性挑战。传统的概率风险评估（PRA）不能充分地模拟功能故障，尽管所有组件都在运行，但由于不确定的物理现象导致性能下降，并且在新设计中与主要的认知不确定性作斗争。这篇综述综合了方法上的进步，追踪了从计算密集型的第一代框架（例如，RMPS/ASPRA）到整合基于人工智能的代理模型（例如，Kriging，多项式混沌扩展，物理信息神经网络）的机器学习驱动范式的评估。这使得功能失效概率的有效量化，通过贝叶斯和自适应采样的认知不确定性映射，以及通过静态方法不可见的动态PRA （DPRA）揭示时间相关风险路径成为可能。然而，机器学习在解决计算瓶颈方面不可替代的作用带来了新的问题，包括“黑箱”不透明、许可的监管挑战、混合主动式被动系统集成、基因III+、SMR、Gen-IV设计的数据稀缺以及长期材料降解效应。我们得出结论，PSS的可靠性取决于可解释的AI （XAI）来揭开模型的神秘面纱，标准化的验证协议，集成的网络物理安全框架。这种转变，特别是通过物理信息机器学习工具（如pinn），对于生成许可和部署先进反应堆所需的严格、监管接受的证据至关重要。

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引用次数: 0

Neutronic calculation route for molten salt fast reactors conception studies 熔盐快堆中子计算路线概念研究

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-31 DOI: 10.1016/j.anucene.2026.112170

D. Marjanovic, G. Valocchi, J. Tommasi

This work investigates the validity and limitations of neutronic modelling routes commonly used in coupled simulations of molten salt fast reactors (MSFRs). This work is carried out as part of the ISAC project funded by the France2030 government program. A two-step deterministic baseline scheme is first developed using APOLLO3® under isothermal, static conditions and benchmarked against Monte Carlo references. The scheme’s accuracy is evaluated across an encompassing set of core configurations, Heavy, Accumulators, and Thermalizing Accumulators. This set aims to capture key neutronic features and assess whether modelling trade-offs are generalizable across diverse reactor behaviours. Once validated, the baseline serves to assess the impact of typical simplifications such as coarse energy group collapsing and diffusion approximations, usually employed in high fidelity multi-physics simulations. Results show that these reduced models can introduce large biases, particularly in configurations with strong spatial and spectral heterogeneities, such as thermalizing accumulators. The study then evaluates whether temperature-induced variations justify iterative neutronic updates or if simplified calculation routes can be used to capture trends and support low-cost rescaling. Key observables such as flux, absorption, and reactivity are examined. Findings support hybrid strategies that reuse accurate baseline results and adjust reactivity using sensitivity-based corrections, offering a promising balance between accuracy and computational cost for MSFR design studies.

本文研究了熔盐快堆（MSFRs）耦合模拟中常用的中子模拟路线的有效性和局限性。这项工作是由法国2030政府计划资助的ISAC项目的一部分。首先在等温静态条件下使用APOLLO3®开发了两步确定性基线方案，并对蒙特卡罗参考进行了基准测试。该方案的准确性是评估跨一套核心配置，重，蓄能器，和热化蓄能器。这一套旨在捕捉关键的中子特征，并评估是否建模权衡在不同的反应堆行为是可推广的。一旦验证，基线用于评估典型简化的影响，如粗能量群坍缩和扩散近似，通常用于高保真多物理场模拟。结果表明，这些简化的模型会引入较大的偏差，特别是在具有强空间和光谱异质性的配置中，如热化蓄能器。然后，该研究评估了温度引起的变化是否证明了迭代中子更新的合理性，或者简化的计算路线是否可以用来捕捉趋势并支持低成本的重新缩放。关键的可观测值，如通量，吸收和反应性进行了检查。研究结果支持混合策略，即重用准确的基线结果，并使用基于灵敏度的校正来调整反应性，为MSFR设计研究提供了准确性和计算成本之间的平衡。

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引用次数: 0

Investigation on ion migration in the secondary side of steam generators based on porous medium method 基于多孔介质法的蒸汽发生器二次侧离子迁移研究

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-30 DOI: 10.1016/j.anucene.2026.112173

Huidong Shi , Taikun Guo , Yu Hu , Jie Feng , Ruifeng Tian , Jiming Wen

In the operation of nuclear power systems, leakage incidents in condensers can cause significant threats to safe operation. The intrusion of seawater into the secondary loop, containing chloride ions (Cl-) and other insoluble impurities, may corrode heat transfer tubes and lead to severe consequences. This study focuses on investigating the migration and precipitation of Cl- and other insoluble impurities in the secondary side of the steam generator. The heat transfer and flow characteristics were numerically simulated, and the distributions of secondary side temperature, void fraction, and velocity were analysed. On this basis, the study investigated the migration of Cl-, their precipitation induced by evaporation and crystallization processes, as well as the migration and precipitation of insoluble impurities. The results demonstrate that the secondary side inlet flow velocity, temperature, and primary side flow velocity all exhibit significant influences on chloride ion concentration. Cl- accumulates on the hot side of the conical expansion support plate, reaching a peak concentration of 30.3 parts per million (ppm). The insoluble impurities are primarily deposited on the flow distribution plate.

在核电系统运行中，凝汽器泄漏事故会对安全运行造成重大威胁。含有氯离子（Cl-）和其他不溶性杂质的海水侵入二级回路，可能腐蚀换热管并导致严重后果。本研究主要研究了Cl-和其他不溶性杂质在蒸汽发生器二次侧的迁移和沉淀。数值模拟了传热和流动特性，分析了二次侧温度、空隙率和速度的分布。在此基础上，研究了Cl-的迁移、蒸发结晶过程引起的沉淀以及不溶性杂质的迁移和沉淀。结果表明，二次侧进口流速、温度和一次侧流速对氯离子浓度均有显著影响。Cl-积聚在锥形膨胀支撑板的热侧，峰值浓度达到30.3 ppm。不溶性杂质主要沉积在配流板上。

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引用次数: 0

Assessing the nuclear fuel cycle under global capacity expansion scenarios toward 2050 评估2050年前全球产能扩张情景下的核燃料循环

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-29 DOI: 10.1016/j.anucene.2026.112175

Iván Merino Rodríguez , Bairon Faundes , Yerko Véliz , Pablo Romojaro , Victor J. Casas-Molina , Francisco Álvarez-Velarde

This study examines the implications of tripling global nuclear capacity by 2050 on the nuclear fuel cycle, based on national projections and COP28 climate commitments. Regionally disaggregated electricity scenarios were generated and used as inputs for the ANICCA simulation code, applying Monte Carlo methods to assess uncertainty in fuel cycle metrics. Three strategies were analyzed: open cycle, partially closed cycle (Pu mono-recycling in LWRs), and advanced closed cycle (Pu and MA multi-recycling in LFRs).

Results show that the open cycle could require about 15 million tons of natural uranium by 2100, surpassing identified reserves. Pu mono-recycling reduces uranium and enrichment needs by ∼9% and achieves Pu balance post-2050. The advanced cycle cuts minor actinide accumulation by ∼50%, easing long-term repository burdens.

These results highlight the need to explore advanced fuel cycles and expand infrastructure for reprocessing, MOX fabrication, and waste management to meet sustainability goals under high nuclear deployment scenarios.

本研究基于各国预测和COP28气候承诺，探讨了到2050年全球核能力增加两倍对核燃料循环的影响。生成了按区域分列的电力情景，并将其用作ANICCA模拟代码的输入，应用蒙特卡罗方法评估燃料循环指标的不确定性。分析了三种策略：开式循环、半封闭循环（LWRs中Pu单一回收）和高级封闭循环（LFRs中Pu和MA多重回收）。结果表明，到2100年，开放式循环可能需要约1500万吨天然铀，超过已确定的储量。Pu单循环减少了约9%的铀和浓缩需求，并在2050年后实现了Pu平衡。先进的循环减少了约50%的微量锕系元素积累，减轻了储存库的长期负担。这些结果强调了探索先进燃料循环和扩大后处理、MOX制造和废物管理基础设施的必要性，以满足高核部署情景下的可持续发展目标。

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引用次数: 0

A macroscopic depletion method for pebble-bed HTR fuel-cycle analysis with AGREE 用AGREE分析球床HTR燃料循环的宏观耗尽法

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-28 DOI: 10.1016/j.anucene.2026.112166

Sefa Bektaş , Volkan Seker , Andrew Ward , Üner Çolak , Thomas Downar

Because of the low-carbon generation nature of nuclear energy and its reliability to provide base load electricity, there is a recognized need to consider nuclear reactors as a future source of energy. However, emerging technologies such as the next generation of advanced small modular reactors are being assessed for safety performance which includes the validation and verification of computer codes used to model and simulate reactor transient behavior during anticipated accident scenarios. Most of the current generation of reactor safety codes have been validated primarily for large light-water reactor systems and the unique features and physics of a small modular high-temperature gas-cooled pebble bed reactors can differ significantly from the LWR and require a different set of experimental facilities and a different range of validation data. The objective of this paper was to extend that validation of the AGREE HTR safety analysis code to fuel depletion using the HTR-200 benchmark problem. The depletion capability was developed for AGREE using a quasi-batchwise fuel loading method and applied to the once-through-then-out (OTTO) fuel pass to achieve an equilibrium condition. The depletion analysis is performed using a two-step macroscopic cross section approach for full-core depletion which was implemented in AGREE. The two-step method used both Monte Carlo and deterministic reactor physics methods in which the Serpent Monte Carlo code was generated region-wise cross sections for the AGREE deterministic full core depletion. The spatially homogenized and energy condensed macroscopic cross-sections accounted for the effects of both instantaneous and history variables to include fuel burnup. Validation was performed by comparing AGREE results with both the legacy HTR code VSOP results reported in the benchmark reference documentation and the full-core, temperature-dependent Monte Carlo Serpent simulation results.

由于核能的低碳发电性质及其提供基本负荷电力的可靠性，人们公认有必要考虑将核反应堆作为未来的能源来源。然而，新兴技术，如下一代先进的小型模块化反应堆，正在进行安全性能评估，其中包括验证和验证用于模拟和模拟反应堆在预期事故情景中的瞬态行为的计算机代码。当前一代的大多数反应堆安全规范主要针对大型轻水反应堆系统进行了验证，小型模块化高温气冷球床反应堆的独特特性和物理特性可能与轻水堆有很大不同，需要不同的实验设施和不同范围的验证数据。本文的目的是利用HTR-200基准问题，将AGREE HTR安全分析代码的验证扩展到燃料消耗。采用准批量燃料装载方法开发了AGREE的耗尽能力，并将其应用于一通即出（OTTO）燃料通道以达到平衡条件。耗尽分析是使用两步宏观截面方法进行全岩心耗尽的，该方法在AGREE中实施。两步法同时使用蒙特卡罗和确定性反应堆物理方法，其中毒蛇蒙特卡罗代码生成了AGREE确定性全堆芯耗尽的区域截面。空间均质化和能量凝聚的宏观截面考虑了瞬时和历史变量的影响，包括燃料燃耗。通过将AGREE结果与基准参考文档中报告的遗留HTR代码VSOP结果和全核、温度相关的Monte Carlo Serpent模拟结果进行比较，来执行验证。

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引用次数: 0

Artificial neural network approach for optimizing X-ray shielding by leveraging the photoelectric absorption edge 利用光电吸收边优化x射线屏蔽的人工神经网络方法

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-28 DOI: 10.1016/j.anucene.2026.112142

M.R. Alipoor, M. Eshghi

The growing use of ionizing radiation necessitates efficient, non-toxic, and lightweight shielding materials. This research employs an Artificial Neural Network refined with Stochastic Gradient Descent to predict the X-ray attenuation of lead-free composites. The model estimates the linear attenuation coefficient based on material density, photon energy, and absorption edge energy. Validation against Geant4 simulations showed excellent accuracy with an average difference of ±0.4 and achieved a nearly perfect regression fit (R² = 0.999). The optimized composites developed using this model achieved superior attenuation, surpassing 99.9% at 40 keV and 92.7% at 120 keV with a mere 1 mm thickness. This study confirms that a physics-informed machine learning approach can rapidly develop high-performance, lead-free shielding for medical and industrial applications.

越来越多的电离辐射使用需要高效、无毒和轻质的屏蔽材料。本研究采用随机梯度下降的人工神经网络来预测无铅复合材料的x射线衰减。该模型基于材料密度、光子能量和吸收边缘能量来估计线性衰减系数。通过对Geant4模拟的验证，显示了极好的准确性，平均差值为±0.4，实现了近乎完美的回归拟合（R2 = 0.999）。使用该模型开发的优化复合材料在厚度仅为1 mm的情况下，在40 keV下衰减超过99.9%，在120 keV下衰减超过92.7%。这项研究证实，基于物理的机器学习方法可以快速开发用于医疗和工业应用的高性能无铅屏蔽。

引用次数: 0

Pin-resolved ex-core detector response function methodology based on the 2D/1D high-fidelity adjoint neutron transport calculation 基于二维/一维高保真伴随中子输运计算的引脚分辨力前核探测器响应函数方法

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-27 DOI: 10.1016/j.anucene.2026.112169

Zhinan Xie , Chen Hao , Wen Yin

Accurate calculation of the detector response function is critical in power reconstruction using ex-core detectors. The Monte Carlo and 3D discrete ordinates methods have been applied to calculate the ex-core detectors response function. However, since ex-core detectors are located far from the reactor core, the large neutron flux gradients between the core and the ex-core detector, as well as the weak contribution of in-core neutrons to the detector response, lead to significant limitations of the computational efficiency and accuracy for the Monte Carlo method. And for 3D discrete ordinates method, the resolution of the detector response function is limited due to the homogenization approximations. Therefore, achieving efficient and accurate whole-core, pin-resolved detector response function calculations remain a significant challenge. To address the challenges of computational efficiency and resolution inherent in conventional methods, the 2D Method of Characteristics / 1D Nodal Expansion Method coupling method with multi-group coarse mesh finite difference method acceleration is used to carry out high-fidelity adjoint transport calculations, enabling direct pin-resolved detector response function calculation. Numerical verification is performed using the 2D EPRI-9 model, the 3D C5G7 model and the low temperature heating reactor. The results demonstrate that the 2D/1D method can accurately and efficiently compute pin-resolved detector response function, achieving well agreement with Monte Carlo results.

探测器响应函数的精确计算是利用脱芯探测器进行功率重构的关键。采用蒙特卡罗法和三维离散坐标法计算了前核探测器的响应函数。然而，由于前堆芯探测器距离反应堆堆芯较远，堆芯与前堆芯探测器之间的中子通量梯度较大，以及堆芯中子对探测器响应的贡献较小，导致蒙特卡罗方法的计算效率和精度受到很大限制。而对于三维离散坐标法，由于均匀化近似，探测器响应函数的分辨率受到限制。因此，实现高效、准确的全核、引脚解析检测器响应函数计算仍然是一个重大挑战。为了解决传统方法固有的计算效率和分辨率问题，采用二维特征法/一维节点展开法耦合多组粗网格有限差分法加速进行高保真伴随输运计算，实现直接针分辨探测器响应函数计算。采用二维EPRI-9模型、三维C5G7模型和低温加热反应器进行数值验证。结果表明，二维/一维方法可以准确、高效地计算引脚分辨探测器响应函数，与蒙特卡罗结果吻合较好。

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引用次数: 0

Reinvestigating the off-grid project priorities of small-scale nuclear reactors using an enhanced integrated fuzzy decision support system 基于增强型集成模糊决策支持系统的小型核反应堆离网工程优先级再研究

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-27 DOI: 10.1016/j.anucene.2026.112160

Serhat Yüksel , Hasan Dinçer , Merve Acar , Edanur Ergün , Serkan Eti

Small-scale nuclear reactors represent a promising option for providing reliable and continuous energy in off-grid and isolated regions; however, limited investment budgets necessitate a clear prioritization of project objectives. This study addresses the need to identify the most critical project priorities and the most suitable off-grid energy applications for small-scale nuclear reactor deployments. To this end, a novel decision-making framework is developed by integrating artificial intelligence-based expert weighting with advanced fuzzy modeling techniques to effectively manage uncertainty and incomplete evaluations. The proposed approach enables a systematic assessment of strategic priorities without being tied to a specific reactor technology. The results indicate that security supported by passive safety systems is the most influential project priority, followed by cost effectiveness and operational flexibility. When alternative off-grid applications are evaluated, steady energy supply for rural industry fields emerges as the most appropriate option due to its strong and balanced performance across safety, economic, and operational dimensions. These findings highlight the interdependence between technical design considerations and application-level decisions. Overall, the study provides practical insights for policymakers and project managers by identifying strategic priorities that can enhance the effectiveness, feasibility, and long-term viability of small-scale nuclear energy investments in off-grid contexts.

小型核反应堆是在离网和偏远地区提供可靠和持续能源的有希望的选择；然而，有限的投资预算需要明确项目目标的优先次序。本研究解决了确定最关键的项目优先级和最适合小型核反应堆部署的离网能源应用的需要。为此，将基于人工智能的专家权重与先进的模糊建模技术相结合，开发了一种新的决策框架，以有效地管理不确定性和不完整的评估。拟议的方法能够在不依赖于特定反应堆技术的情况下对战略优先事项进行系统评估。结果表明，被动安全系统支持的安全性是最具影响力的项目优先级，其次是成本效益和操作灵活性。当评估替代性离网应用时，稳定的农村工业领域能源供应成为最合适的选择，因为它在安全、经济和运营方面具有强大而平衡的性能。这些发现突出了技术设计考虑和应用程序级决策之间的相互依赖关系。总体而言，该研究通过确定可以提高离网环境下小型核能投资的有效性、可行性和长期可行性的战略重点，为政策制定者和项目经理提供了实用的见解。

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引用次数: 0

Operational strategies for nuclear district heating systems in extremely cold climates 极冷气候下核区域供热系统的运行策略

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-27 DOI: 10.1016/j.anucene.2026.112162

Ruo-Jun Xue , Han-Wen Liang , Hao-Fang Chong , Min-Jun Peng

As energy structures evolve and environmental standards rise, nuclear energy shows clear advantages in district heating, particularly in cold regions. To ensure consistent quality and stability of supply, accurate and responsive load-following capability is essential for the effective use of nuclear energy in the heating sector.

This study presents a simulation model for a 400 MW pool-type nuclear heating reactor, accompanied by the development of a comprehensive system simulation platform using Python and Computational Fluid Dynamics (CFD) for both one-dimensional (1D) and three-dimensional (3D) coupled analyses. The research systematically examines the operational parameters and strategies of the nuclear heating system under extreme cold climate conditions. The simulation results indicate that under the constant-temperature heating mode, after accounting for the thermal delay characteristics of the reactor pool, the number of power adjustments required during the 139-day heating season is reduced by 22 instances. Under the variable-temperature heating mode, the independent heating configuration of this reactor can satisfy the thermal demand during the heating period in cold regions.

随着能源结构的演变和环境标准的提高，核能在区域供热方面显示出明显的优势，特别是在寒冷地区。为了确保供应的质量和稳定性，准确和响应的负荷跟踪能力对于在供热部门有效利用核能至关重要。本研究提出了400mw池式核加热堆的仿真模型，并利用Python和计算流体动力学（CFD）开发了一个综合系统仿真平台，用于一维（1D）和三维（3D）耦合分析。该研究系统地考察了在极端寒冷气候条件下核加热系统的运行参数和策略。仿真结果表明，在恒温加热模式下，考虑反应堆池热延迟特性后，139天采暖季所需的功率调整次数减少了22次。在变温加热模式下，该反应器的独立加热配置可以满足寒冷地区采暖期间的热需求。

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引用次数: 0

A deep learning-based prognostic approach for predicting PWR degradation and remaining useful life using GNN-PTC-LSTM 基于GNN-PTC-LSTM的基于深度学习的压水堆退化和剩余使用寿命预测方法

IF 2.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Annals of Nuclear Energy

Pub Date : 2026-01-27 DOI: 10.1016/j.anucene.2026.112172

Shadman Ahmad Khattak , Liu Yong-Kuo , Liu Yu-Kun , Gao Jia-Rong , Shi Zhou-Xin , Liu Ji

Early fault detection and prediction are key for ensuring the proper functioning and safety operation of complex nuclear systems especially in nuclear power production. Remaining Useful Life (RUL) prediction in Pressurized Water Reactors (PWRs) is one of the crucial parameters to prevent gradual degradation and disaster. In this paper a hybrid model composed of graph neural network,physics topology constraint and long short term memory network (GNN-PTC-LSTM) is proposed for fault prognostic and remaining useful life prediction based on the nuclear power plant using 2 loop PWR PCTRAN datasets. The proposed framework employs Graph Neural Networks (GNNs) to capture the spatial dependencies between reactor subsystems while integrating a PTC-LSTM module that incorporates physical topology and plant dynamics as temporal sequence learning constraints. In contrast to traditional LSTMs which are purely statistical model the physics-informed PTC-LSTM integrates a priori understanding of domain knowledge to mitigate non-physical predictions models outputs that violate known physical/topological constraints and reduce false alarms due to spurious data correlations. Additionally, One-Class SVM is used for anomaly detection in multivariate telemetry data to allow early discovery of abnormal behaviours. The proposed GNN-PTC-LSTM framework achieved an overall fault classification accuracy of 99.1%, an early warning accuracy of 98.2%, and competitive RUL prediction performance with a mean absolute error (MAE) of 0.0042 and root mean square error (RMSE) of 0.0105 under simulated PWR accident scenarios.

早期故障检测和预测是保证复杂核系统正常运行和安全运行的关键，特别是在核电生产中。压水堆剩余使用寿命（RUL）预测是防止压水堆逐渐退化和发生灾害的关键参数之一。本文利用2环压水堆PCTRAN数据集，提出了一种由图神经网络、物理拓扑约束和长短期记忆网络组成的混合模型（GNN-PTC-LSTM），用于核电厂的故障预测和剩余使用寿命预测。提出的框架采用图神经网络（gnn）来捕获反应堆子系统之间的空间依赖关系，同时集成PTC-LSTM模块，该模块将物理拓扑和植物动态作为时间序列学习约束。与纯统计模型的传统lstm相比，物理信息灵通的PTC-LSTM集成了对领域知识的先验理解，以减轻违反已知物理/拓扑约束的非物理预测模型输出，并减少由于虚假数据相关性引起的误报。此外，将一类支持向量机用于多变量遥测数据的异常检测，以便及早发现异常行为。所提出的GNN-PTC-LSTM框架在模拟压水堆事故场景下，总体故障分类准确率为99.1%，预警准确率为98.2%，竞争性RUL预测性能的平均绝对误差（MAE）为0.0042，均方根误差（RMSE）为0.0105。

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引用次数: 0