基于 SPN 方法的快堆 NCLFR-Oil 功率分配智能优化

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Shaoning Shen, Wenshun Duan, Weixiang Wang, Aoguang Wu, Kefan Zhang, Hongli Chen
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引用次数: 0

摘要

在 COMSOL 有限元求解器的基础上定制开发了中子输运模块,以便在堆芯设计中进行高效的优化和参数评估,该模块可与其他内置模块集成以增强功能。这项工作首先为使用 PDE 求解器的多维 SPN 方法奠定了实用基础,该方法能够模拟稳态(k 特征值)和随时间变化的传输问题。稳态求解器与三维 TAKEDA 和二维 C5G7 基准显示了良好的一致性,而瞬态求解器则与 TWIGL 和 LMW 基准进行了良好的验证。在对自行设计的快堆 NCLFR-Oil 进行建模时,使用 OpenMC 生成少组常数,然后将其作为方程系数导入 COMSOL 的 SP3 中子输运模块。通过测试特征值、控制棒价值、功率和中子通量分布,验证了 SP3 模型模拟堆芯物理场的能力。利用 COMSOL 的不确定性量化模块进行了敏感性分析,以评估控制棒位置对堆芯特征值和功率分布的影响,从而完善优化参数空间并提高效率。为进一步提高优化效率,采用了基于 "多项式混沌展开 "的代用模型来逼近岩心物理模型,预测输入参数与优化目标之间的关系。事实证明,该模型比无梯度 "坐标搜索 "方法更有效,减少了计算资源消耗。优化结果表明,定制功率平坦化系数显著降低,使更多功率因数更接近目标值 1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent optimization of power distribution for fast reactor NCLFR-Oil based on SPN method

A custom-developed neutron transport module based on the COMSOL finite element solver was created to enable efficient optimization and parameter evaluation in core design, and it can be integrated with other built-in modules for enhanced capabilities. This work began by establishing a practical foundation for a multi-dimensional SPN method using the PDE solver, capable of simulating both steady-state (k-eigenvalue) and time-dependent transport problems. The steady-state solver showed good agreement with 3D TAKEDA and 2D C5G7 benchmarks, while the transient solver was well-validated with TWIGL and LMW benchmarks. For modeling the self-designed fast reactor NCLFR-Oil, OpenMC was used to generate few-group constants, which were then imported into COMSOL’s SP3 neutron transport module as equation coefficients. The SP3 model’s capability to simulate the core’s physical field was validated by testing eigenvalues, control rod worth, the power and neutron flux distribution. Sensitivity analysis was performed using COMSOL’s uncertainty quantification module to assess the impact of control rod positions on core eigenvalues and power distribution, refining the parameter space for optimization and enhancing efficiency. To further improve optimization efficiency, a surrogate model based on “Polynomial Chaos Expansion” was employed to approximate the core’s physical model, predicting relationships between input parameters and optimization objectives. This model proved more efficient than the gradient-free “Coordinate Search” method, reducing computational resource consumption. The optimization results showed a significant reduction in the custom power flattening factor, bringing more power factors closer to the target value of 1.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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