Annals of Nuclear Energy最新文献_第10页

Thermomechanics coupling to Monte Carlo particle transport on unstructured mesh geometries using Cardinal 热力学耦合到蒙特卡罗粒子输运在非结构网格几何使用基数

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

Annals of Nuclear Energy

Pub Date : 2025-12-31 DOI: 10.1016/j.anucene.2025.112068

Mahmoud Eltawila , Pierre-Clément A. Simon , Guillaume L. Giudicelli , Helen Brooks , Nicholas Wozniak , April J. Novak

Geometry deformation due to thermal expansion influences neutron transport in many systems. Studying this phenomenon involves coupling models for neutronics, thermal hydraulics, and solid mechanics. To enable high fidelity modeling of these coupled physics, new capabilities were introduced in Cardinal, coupling OpenMC Monte Carlo particle transport models with MOOSE thermomechanical physics on unstructured moving-mesh geometries. In this work, we present a fully open-source capability leveraging on-the-fly mesh skinning to automatically regenerate OpenMC geometry, which allows multiphysics feedback from temperature, density, and geometry changes. The new capability is verified using an analytic benchmark slab problem, which couples S

_{2}

neutron transport with thermal conduction, convective boundary conditions, Doppler-broadened cross sections, and nonlinear thermal expansion effects along the heated slab. Cardinal reproduces the analytic solutions for the neutron flux, heating, k

_{eff}

, and temperature with demonstrated convergence in various error terms including mesh resolution and cross section temperature library spacing. For the nominal benchmark conditions and with a fine mesh, maximum relative errors for neutron flux, temperature, and heating are lower than 1%, while errors in integral quantities such as

k_{eff}

and slab length are within 1 pcm and 48 µm, respectively. This work (i) presents a new numerical approach to thermomechanics coupling with OpenMC models, (ii) is the first (to our knowledge) to utilize a mechanical partial differential equation (PDE) solution to solve the (Griesheimer and Kooreman, 2022) analytic benchmark, and (iii) develops this verified capability within an open-source package.

在许多系统中，热膨胀引起的几何变形影响中子输运。研究这种现象涉及到中子学、热工水力学和固体力学的耦合模型。为了实现这些耦合物理的高保真建模，Cardinal引入了新的功能，将OpenMC蒙特卡罗粒子输运模型与MOOSE热机械物理在非结构化移动网格几何上耦合。在这项工作中，我们提出了一个完全开源的功能，利用实时网格蒙皮来自动重新生成OpenMC几何形状，它允许来自温度、密度和几何形状变化的多物理场反馈。利用一个解析基准板问题验证了这一新能力，该问题将S2中子输运与热传导、对流边界条件、多普勒展宽截面和沿加热板的非线性热膨胀效应耦合在一起。Cardinal再现了中子通量、加热、keff和温度的解析解，并在各种误差项（包括网格分辨率和截面温度库间距）中展示了收敛性。在标称基准条件和细网格条件下，中子通量、温度和加热的最大相对误差小于1%，而keff和slab长度等积分量的误差分别在1 pcm和48µm以内。这项工作(i)提出了一种新的数值方法来与OpenMC模型耦合热力学，（ii）是第一个（据我们所知）利用机械偏微分方程（PDE）解决方案来解决（Griesheimer和Kooreman， 2022）分析基准，（iii）在一个开源包中开发了这种验证能力。

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引用次数: 0

Vibration analysis of large equipment with Non-Uniform mass under road transportation 大型非均匀质量设备公路运输振动分析

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

Annals of Nuclear Energy

Pub Date : 2025-12-31 DOI: 10.1016/j.anucene.2025.112102

Tiandi Fan , Huajie Wu , Changzheng Chen , Xianming Sun , lipeng Wang

During the transportation of large-scale equipment, road excitation can trigger resonance, potentially impairing the structural stability of the vehicle system. Moreover, due to its special structure and non-uniform mass distribution—such as reactor pool mass concentration from high-density Lead-Bismuth Eutectic (LBE) coolant—transportation vibration response and stability have become urgent key technical issues. Besides, liquid LBE coolant may slosh during transportation, requiring stricter stability rules. However, current research focuses more on anti-vibration design or isolation, with few studies on the equipment’s vibration. To solve this problem, this study uses Lagrange’s differential equations to create a new separated vibration model. It explains how mobile LBE reactors respond to vibration, and analyzes vibration responses under different road classes, driving speeds, and loading masses. This study confirms the developed separated model’s ability to capture on-board equipment and vehicle body vibration responses, offering references for large equipment transportation safety and insights for mobile LBE reactor design.

在大型设备的运输过程中，道路激励会引发共振，可能会损害车辆系统的结构稳定性。此外，由于其特殊的结构和不均匀的质量分布，如高密度铅铋共晶（LBE）冷却液的堆池质量浓度，输送振动响应和稳定性已成为迫切需要解决的关键技术问题。此外，液态LBE冷却剂在运输过程中可能会晃动，需要更严格的稳定性规则。但目前的研究多集中在设备的抗振设计或隔振上，对设备的振动研究较少。为了解决这一问题，本研究利用拉格朗日微分方程建立了一个新的分离振动模型。介绍了移动LBE电抗器对振动的响应，并分析了不同道路等级、行驶速度和装载质量下的振动响应。该研究证实了所开发的分离模型能够捕获车载设备和车身振动响应，为大型设备运输安全提供参考，并为移动LBE反应堆设计提供见解。

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引用次数: 0

Radiological shielding performance for offsite emergency center near research reactor 研究堆旁应急中心的辐射屏蔽性能

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

Annals of Nuclear Energy

Pub Date : 2025-12-30 DOI: 10.1016/j.anucene.2025.112108

Arya Pramana Sembiring , Juyoul Kim

Gerrit Agustinus Siwabessy Multi-Purpose Reactor (RSG-GAS) is Indonesia’s largest research reactor. This research reactor needs radiation risk mitigation in a densely populated area. This study examines the radiation shielding effectiveness of Building No. 93 as an offsite emergency center during a Beyond Design-Basis Accident (BDBA). An Anticipated Transient Without Scram (ATWS) scenario with five fuel element meltdowns was simulated using HotSpot, a Gaussian plume-based atmospheric dispersion software, and dose attenuation was performed using Monte Carlo. The result shows that the dual-layered concrete wall can reduce the incoming total dose rate to 0.558 mSv/hour, allowing safe occupancy for 3.74 days before reaching Indonesia’s emergency dose limit of 50 mSv for workers and the public. This study confirms that Building No. 93 meets regulatory standards and is suitable as an offsite emergency center for emergency operators during severe nuclear incidents. This study established a framework for future emergency evaluation and management strategy.

Gerrit Agustinus Siwabessy多用途反应堆（RSG-GAS）是印度尼西亚最大的研究反应堆。这个研究反应堆需要在人口密集的地区降低辐射风险。本研究考察了93号楼作为非现场应急中心在超设计基础事故（BDBA）中的辐射屏蔽效能。使用基于高斯羽流的大气扩散软件HotSpot模拟了5个燃料元件熔毁的预期瞬态无Scram （ATWS）情景，并使用蒙特卡罗进行了剂量衰减。结果表明，双层混凝土墙可将入射总剂量率降低至0.558毫西弗/小时，在达到印度尼西亚对工作人员和公众50毫西弗的紧急剂量限值之前，允许安全居住3.74天。本研究证实93号楼符合规范标准，适合作为严重核事故应急操作人员的场外应急中心。本研究为未来的应急评估和管理策略建立了框架。

引用次数: 0

An open source multiphysics workflow for the analysis of subcritical transmutation systems 用于分析亚临界嬗变系统的开源多物理场工作流

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

Annals of Nuclear Energy

Pub Date : 2025-12-30 DOI: 10.1016/j.anucene.2025.112072

Matthew Nyberg , Joseph Eickman , Una Baker , Patrick Shriwise , Ben Lindley

Two challenges were identified in modeling externally driven systems (EDS) for transuranic (TRU) burning with existing workflows: complex, spatially varying sources and flexible open-source production of cross-sections. A workflow is presented coupling the source and multi-group cross-section generation within OpenMC to the GeN-Foam multiphysics solver. First, OpenMC generated nuclear data was exported for transuranic-based molten salt reactor GeN-Foam model, and neutronics characteristics were verified against an equivalent OpenMC model. This TRU-based fueled salt was compared to uranium-thorium molten salt at steady state and over a range of potential accident scenarios. Secondly, a method utilizing the OpenMC C++ API sampled a fusion source onto a mesh used within GeN-Foam simulations, enabling closely coupled EDS multiphysics analysis. This spatially accurate source definition was verified against OpenMC models, and then was demonstrated for transient scenarios. This workflow enables closely coupled multiphysics analysis of complex critical or subcritical systems using open-source tools.

在利用现有工作流程为超铀（TRU）燃烧建模外部驱动系统（EDS）时，确定了两个挑战：复杂的、空间变化的来源和灵活的开源截面生产。提出了将OpenMC中的源和多组截面生成与GeN-Foam多物理场求解器相耦合的工作流程。首先，导出OpenMC生成的超铀熔盐堆GeN-Foam模型的核数据，并与等效OpenMC模型进行中子特性验证。在稳定状态和一系列潜在事故情景下，将这种基于trur的燃料盐与铀钍熔盐进行了比较。其次，利用OpenMC c++ API将融合源采样到GeN-Foam模拟中使用的网格上，从而实现紧密耦合的EDS多物理场分析。在OpenMC模型中验证了这种空间精确的源定义，然后在瞬态场景中进行了演示。该工作流可以使用开源工具对复杂的关键或次关键系统进行紧密耦合的多物理场分析。

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引用次数: 0

A state-of-the-art review of accurate and rapid prediction methods for thermal striping phenomenon in nuclear reactors 核反应堆热条带现象准确快速预测方法的最新进展

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

Annals of Nuclear Energy

Pub Date : 2025-12-27 DOI: 10.1016/j.anucene.2025.112098

Mohsin Raza , Ikram UI Haq , Waqar ul Hassan , Jun-Liang Guo , Hong-Na Zhang , Xiao-Bin Li , Yue Wang , Wei-Hua Cai , Shu-Qi Meng , Fang Chen , Yu-Long Mao , Feng-Chen Li

This review analyzes advanced predictive methodologies related to thermal striping in nuclear reactors, which involves the interaction of hot and cold fluid streams leading to temperature fluctuations, thermal stresses, and potential structural vulnerabilities. This study emphasizes high-fidelity simulation techniques, such as direct numerical simulations (DNS) and large eddy simulations (LES), which effectively capture transient flow dynamics with high fidelity. The review also explores data-driven innovations like the machine learning (ML), which exhibits significant potential for improving predictive accuracy by integrating physical principles with high-quality datasets. Primary failure mechanisms, such as thermal fatigue, stress corrosion cracking, and thermal embrittlement, are thoroughly examined. Well-established reduced-order modeling (ROM) approaches, such as proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD)-based reduced models—reduce the dimensionality and can substantially lower computational cost; with stable reduced integration, near real-time predictions are achievable within calibrated operating ranges. This review highlights the significant impact of multi-scale hybrid modeling for rapid and accurate prediction of thermal striping. This review identifies key limitations of current modeling approaches, particularly in balancing computational cost, accuracy, and speed. A detailed comparison shows that while traditional models offer precision, they are often too slow or expensive for real-time use.

On the other hand, ROM and ML enable faster predictions but may sacrifice accuracy in complex scenarios. Based on this trade-off, this study highlights hybrid modeling approaches as a promising solution for balancing accuracy, speed, and computational cost prediction of thermal striping. Finally, this study outlines critical research gaps and suggests future directions that may guide the development of smarter and more reliable prediction tools for thermal fluid systems and advance reactor technology.

本文分析了与核反应堆热条纹相关的先进预测方法，热条纹涉及冷热流体流的相互作用，导致温度波动、热应力和潜在的结构脆弱性。本研究强调高保真模拟技术，如直接数值模拟（DNS）和大涡模拟（LES），可以有效地高保真地捕捉瞬态流动动力学。该综述还探讨了数据驱动的创新，如机器学习（ML），它通过将物理原理与高质量数据集相结合，显示出提高预测准确性的巨大潜力。主要的失效机制，如热疲劳，应力腐蚀开裂，热脆，彻底检查。基于适当正交分解（POD）和基于动态模态分解（DMD）的降阶模型等成熟的降阶建模方法可以降低维数，大大降低计算成本；通过稳定的集成，可以在校准的操作范围内实现近乎实时的预测。本文综述了多尺度混合模型对快速准确预测热条带化的重要影响。这篇综述指出了当前建模方法的主要局限性，特别是在平衡计算成本、准确性和速度方面。一项详细的比较表明，虽然传统模型提供了精度，但对于实时使用来说，它们往往太慢或太昂贵。另一方面，ROM和ML可以实现更快的预测，但在复杂的场景中可能会牺牲准确性。基于这种权衡，本研究强调了混合建模方法作为平衡精度，速度和计算成本预测的有前途的解决方案。最后，本研究概述了关键的研究差距，并提出了未来的方向，可能指导开发更智能、更可靠的热流体系统预测工具和先进的反应堆技术。

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引用次数: 0

High-fidelity transient neutronics/thermal-hydraulics coupling analyses of control rod ejection accident in a prismatic gas-cooled reactor core 柱形气冷堆堆芯控制棒抛射事故的高保真瞬态中子/热工-水力耦合分析

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

Annals of Nuclear Energy

Pub Date : 2025-12-27 DOI: 10.1016/j.anucene.2025.112097

Xingyu Zhao , Xiaoyu Guo , Guodong Liu , Yuntao Zheng , Yaru Li , Lanyu Zhou , Shuliang Huang , Shanfang Huang , Qiaoyan Chen , Kan Wang

This paper presents a high-fidelity neutronics/thermal-hydraulics transient coupling approach using the Monte Carlo code RMC and CFD code ANSYS Fluent, applied to control rod ejection accidents in a prismatic high-temperature gas-cooled reactor (HTGR) core. The Picard-iteration-based scheme uses RMC time-space dynamics calculation to update both power amplitude and shape. Data transfer and mesh mapping are realized through hierarchical data format (hdf) and a multi-superposition mesh mapping strategy. Verification through pressurized water reactor (PWR) mini-core cases shows good agreement with reference results. Various control rod ejection scenarios with different locations and reactivity insertions are simulated in the prismatic HTGR. The time-dependent results demonstrate thermal-hydraulics feedback amplified by larger reactivity, confirming favorable passive safety. Compared with conventional methods, the high-fidelity approach yields less fluctuating results and reduces redundant conservatism, thus enhancing the overall efficiency. The high-fidelity approach also has a certain capability to simulate prompt supercritical transients.

利用蒙特卡罗代码RMC和CFD代码ANSYS Fluent，提出了一种高保真的中子/热工-水力学瞬态耦合方法，并应用于柱形高温气冷堆堆芯控制棒抛射事故。基于picard迭代的方案使用RMC时空动力学计算来更新功率振幅和形状。通过分层数据格式（hdf）和多重叠加网格映射策略实现数据传输和网格映射。压水堆微堆芯实例验证结果与参考结果吻合较好。在柱形高温高温堆中模拟了不同位置和反应性插入的各种控制棒弹射场景。与时间相关的结果表明，较大的反应性放大了热工液压反馈，证实了良好的被动安全性。与传统方法相比，高保真度方法产生的结果波动较小，减少了冗余保守性，从而提高了整体效率。高保真度方法还具有一定的模拟瞬态超临界的能力。

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引用次数: 0

Development and application of a mechanism-based fission gas release model in FROBA fuel performance code 基于机理的裂变气体释放模型在FROBA燃料性能规范中的开发与应用

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

Annals of Nuclear Energy

Pub Date : 2025-12-26 DOI: 10.1016/j.anucene.2025.112094

Kou Minghai , Xiao Xinkun , Yu Songjiao , Chen Ronghua , Jiang Pinting , Dai Mingliang , Zhang Kui , Wu Yingwei , Tian Wenxi , Qiu Suizheng

The release of fission gas in nuclear fuel significantly impacts fuel performance. Currently, many engineering models for fission gas release (FGR) rely on empirical corrections of simplified processes, introducing considerable uncertainty. Therefore, implementing mechanism-based FGR models grounded in physical behavior is crucial for improving the reliability of fuel performance codes. In this study, an established mechanism-based FGR model (incorporating atomic diffusion, intra-granular bubble re-solution, grain-boundary sweeping, and inter-granular bubble dynamics) was integrated into the fuel performance analysis code FROBA, along with a non-thermal release model. The implementation couples grain-boundary gas release with swelling equations. Model validation against literature benchmarks under steady-state conditions demonstrates excellent agreement with experimental data and other codes for both FGR fraction and swelling rate. Uncertainty analysis confirms the model’s effectiveness within the implemented scope.

核燃料中裂变气体的释放严重影响核燃料的性能。目前，许多裂变气体释放（FGR）的工程模型依赖于简化过程的经验修正，引入了相当大的不确定性。因此，实现基于物理行为的FGR模型对于提高燃料性能代码的可靠性至关重要。在这项研究中，建立了一个基于机制的FGR模型（包括原子扩散、颗粒内气泡再溶解、晶界扫描和颗粒间气泡动力学），并将其与非热释放模型集成到燃料性能分析代码FROBA中。该实现将晶界气体释放与膨胀方程耦合。在稳态条件下对文献基准的模型验证表明，FGR分数和膨胀率与实验数据和其他代码非常吻合。不确定性分析证实了模型在实施范围内的有效性。

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引用次数: 0

Prediction method for Safety-Related parameters of Lead-Bismuth cooled fast reactor using Attention-CNN-LSTM fusion model 基于Attention-CNN-LSTM聚变模型的铅铋冷快堆安全相关参数预测方法

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

Annals of Nuclear Energy

Pub Date : 2025-12-26 DOI: 10.1016/j.anucene.2025.112093

Hanwen Xu , Rui Pan , Shuai Chen , Qiusun Zeng , Yanan Ma , Zhen Wang

In the context of rising global energy demand and the shift towards low-carbon energy, lead–bismuth cooled fast reactors (LFRs) have emerged as a key technology in fourth-generation nuclear reactor development. The advantages of LFRs—low neutron absorption cross-section, atmospheric-pressure operation, excellent heat transfer performance, and strong chemical inertness—endow them with significant potential in specialized energy supply scenarios. However, the compact structure of LFRs and complex system parameter changes during failures present challenges, and existing prediction methods fail to meet practical requirements. This study proposes a novel model integrating the attention mechanism, convolutional neural networks (CNN), and long short-term memory networks (LSTM). CNNs extract local spatiotemporal features, the attention mechanism highlights critical information, and LSTMs capture both long- and short-term dependencies. Combined with a multi-input, multi-output (MIMO) prediction strategy, the model enables multi-step prediction of LFR safety–critical parameters under fault conditions. Experimental results based on simulation data from various operating scenarios of China’s Lead-based Research Reactor (CLEAR-I) demonstrate that the proposed model outperforms advanced alternative models. Compared with RNN, Attention-GRU, and TCN, it reduces RMSE by an average of 35%, RRMSE by 32%, MAE by 29%, and MAPE by 33% across 6-step, 12-step, and 24-step predictions. Its single-sample inference time ranges from 1.99 to 2.12 ms, with a 95% confidence interval coverage rate of 96.84%. This model effectively predicts safety-related parameters under LFR fault conditions, providing crucial support for reactor safety and stability, and demonstrating significant application value in fault parameter prediction for lead–bismuth cooled reactors.

在全球能源需求不断增长和向低碳能源转变的背景下，铅铋冷却快堆（LFRs）已成为第四代核反应堆发展的关键技术。lfrs具有中子吸收截面小、常压运行、传热性能好、化学惰性强等优点，在特殊的能源供应场景中具有巨大的潜力。然而，由于LFRs结构紧凑，故障过程中系统参数变化复杂，现有的预测方法不能满足实际要求。本研究提出了一种将注意机制、卷积神经网络（CNN）和长短期记忆网络（LSTM）相结合的新模型。cnn提取局部时空特征，注意机制突出关键信息，lstm捕获长期和短期依赖关系。该模型结合多输入多输出（MIMO）预测策略，实现了故障条件下LFR安全关键参数的多步预测。基于中国铅基研究堆（CLEAR-I）各种运行场景的模拟数据的实验结果表明，所提出的模型优于先进的替代模型。与RNN、Attention-GRU和TCN相比，它在6步、12步和24步预测中平均降低了35%的RMSE， 32%的RRMSE， 29%的MAE和33%的MAPE。其单样本推断时间范围为1.99 ~ 2.12 ms， 95%置信区间覆盖率为96.84%。该模型能有效预测LFR故障条件下的安全相关参数，为反应堆安全稳定提供了重要支撑，在铅铋冷却堆故障参数预测中具有重要应用价值。

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引用次数: 0

Physics-informed fault diagnosis through online efficiency monitoring of PWR type nuclear power plants 基于压水式核电站在线效率监测的物理信息故障诊断

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

Annals of Nuclear Energy

Pub Date : 2025-12-26 DOI: 10.1016/j.anucene.2025.112071

Furqan Arshad , Minjun Peng , Wasiq Ali , Zikang Li , Fazle Haseeb , Awais Khan

This study proposes a framework which integrates the machine learning based fault diagnosis with the efficiency monitoring of a pressurized water reactor (PWR) nuclear power plant. The purpose of the efficiency monitoring is to detect the operational deviations from the optimum conditions, while the fault diagnosis part identifies the faulty equipment along with the extent estimation. The fault diagnosis has been performed through the use of feed forward back propagation (FFBP) and long short term memory (LSTM) neural networks, and its performance has further been improved through the incorporation of physics augmented feature space. In total, thirty three fault conditions related to the internal leakages in steam generators and feed water heaters have been studied in this work. It has been demonstrated that through the augmentation of physics-based features, the overall performance of the fault diagnosis is significantly improved. This improved performance has further been verified through the application of SHapley Additive exPlanations (SHAP) analysis, and also the model robustness has been demonstrated through testing against the noisy data.

本文提出了一种将基于机器学习的故障诊断与压水堆（PWR）核电站效率监测相结合的框架。效率监测的目的是检测运行偏离最佳状态，故障诊断部分是识别故障设备并进行程度估计。利用前馈反馈传播（FFBP）和长短期记忆（LSTM）神经网络进行故障诊断，并通过物理增强特征空间的结合进一步提高其性能。本文共研究了33种与蒸汽发生器和给水加热器内泄漏有关的故障工况。研究表明，通过增强基于物理的特征，故障诊断的整体性能得到了显著提高。通过应用SHapley加性解释（SHAP）分析进一步验证了这种改进的性能，并且通过对噪声数据的测试证明了模型的鲁棒性。

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引用次数: 0

Neutronic analysis of U-7Mo-xTi/Al fuel elements as replacement candidates for Indonesia’s RSG-GAS research reactor fuel: Enrichment optimisation, burnup behaviour, and temperature coefficients U-7Mo-xTi/Al燃料元件替代印尼RSG-GAS研究堆燃料的中子分析：浓缩优化、燃耗行为和温度系数

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

Annals of Nuclear Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.anucene.2025.112091

Fikri A. Furqan, Deni Mustika, Saga Octadamailah, Mu’nisatun Sholikhah, G.K. Suryaman, Ade Saputra, Supardjo

This study presents an initial neutronic analysis of the new nuclear fuel U-7Mo-xTi/Al (x = 1, 2, 3) proposed as a replacement for U₃Si₂/Al in the GA Siwabessy Multipurpose Reactor (RSG-GAS). Ti is added to the U-7Mo alloy to stabilise the γ-U phase, improving powder fabrication, and contributes to enhanced corrosion resistance of the fuel. Evaluations were conducted on enrichment optimisation, burnup, and temperature coefficients using OpenMC with the RSG-GAS fuel element geometry model without considering neutron leakage. Simulation results show that the optimal enrichment for each composition is 13.715 % (U-7Mo-1Ti/Al), 14.140 % (U-7Mo-2Ti/Al), and 14.5 % (U-7Mo-3Ti/Al) to achieve a k-infinity value comparable to U₃Si₂/Al at 19.75 % enrichment. Burnup behaviour indicates an extension of fuel lifetimes from 25 days to 45.909 days (U-7Mo-1Ti/Al); 45.723 days (U-7Mo-2Ti/Al); 45.572 days (U-7Mo-2Ti/Al), indicating improved fuel cycle efficiency. Safety margins are strengthened by strongly negative temperature coefficients: FTC (−1.94700 to −2.72296 pcm/K) and MTC (−0.64651 to −4.18274 pcm/K), which support the inherent safety characteristics of the reactor. Overall, U-7Mo-xTi/Al has higher fuel efficiency and safety margins than U₃Si₂/Al.

本研究提出了一种新的核燃料U-7Mo-xTi/Al （x = 1,2,3）作为替代U3Si2/Al在GA核聚变多用途反应堆（RSG-GAS）的初步中子分析。Ti被添加到U-7Mo合金中以稳定γ-U相，改善粉末制造，并有助于增强燃料的耐腐蚀性。在不考虑中子泄漏的情况下，利用OpenMC和RSG-GAS燃料元件几何模型对浓缩优化、燃耗和温度系数进行了评估。模拟结果表明，每种成分的最佳富集度分别为13.715% （U-7Mo-1Ti/Al）、14.140% （U-7Mo-2Ti/Al）和14.5% (U-7Mo-3Ti/Al)，可获得与U3Si2/Al富集度为19.75%时相当的k无穷大值。燃耗行为表明燃料寿命从25天延长到45.909天（U-7Mo-1Ti/Al）；45.723天（U-7Mo-2Ti/Al）；45.572天（U-7Mo-2Ti/Al），表明燃料循环效率提高。安全边际通过强烈的负温度系数得到加强：FTC（- 1.94700至- 2.72296 pcm/K）和MTC(- 0.64651至- 4.18274 pcm/K)，这支持了反应堆的固有安全特性。总体而言，U-7Mo-xTi/Al具有比U3Si2/Al更高的燃油效率和安全边际。

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引用次数: 0