Structure-Performance-Cost Integration Multi-Objective Optimization Design for HTR Fuel Storage Canister
-
摘要: 燃料贮罐是高温堆新燃料供应系统关键设备。为探索最佳设计方案，提出燃料贮罐结构-性能-成本一体化多目标优化设计方法：选取燃料贮罐结构板厚作为设计变量，采用拉丁超立方采样（LHS）生成均匀采样点，通过数值计算获取跌落响应，通过混合径向基函数神经网络（RBFNN）-前馈神经网络（FFNN）构造代理模型；以最大塑性变形最小、成本最低、质量最小作为优化设计目标，同时约束球床作用下的径向位移膨胀，利用强度Pareto进化算法（SPEA-Ⅱ）求解优化问题。结果表明：燃料贮罐安全性明显提高，最大塑性变形可降低20.17%；经济性与轻量化效果较好，单罐成本可降低2128元，质量可降低12.54%。本文一体化优化方法能够为燃料贮罐设计提供参考。
-
关键词:
- 高温堆 /
- 燃料贮罐 /
- 结构-性能-成本一体化 /
- 多目标优化
Abstract: Fuel storage canister is a key equipment in the fuel supply system for high temperature reactor (HTR). In order to explore the optimal design scheme, a structure-performance-cost integration multi-objective optimization design method for fuel storage canister is proposed as follows: select the structural plate thickness of fuel storage canister as the design variable, use the Latin hypercube sampling (LHS) method to generate uniform sampling points, obtain the drop response through numerical calculation, and employ the hybrid radial basis function neural network (RBFNN)-feedforward neural network (FFNN) to construct a surrogate model. With the optimization design objectives of minimizing the maximum plastic deformation, the cost and the mass, constrain the radial displacement expansion under the action of pebble bed, and solve the optimization problem by using the strength Pareto evolutionary algorithm (SPEA-Ⅱ). The results show that the safety of the fuel storage canister is significantly improved, and the maximum plastic deformation can be reduced by 20.17%; it has good economical efficiency and lightweight effect, the cost of a single canister can be reduced by 2,128 yuan, and the mass can be reduced by 12.54%. The integration optimization method proposed in this paper can provide reference for the fuel storage canister design. -
图 3 多目标优化设计流程
RBFNN—径向基函数神经网络；FFNN—前馈神经网络；SPEA-Ⅱ—强度Pareto进化算法
Figure 3. Flow Chart of the Multi-objective Optimization
图 5 非线性塑性变形响应代理模型与数值计算模型结果对比
Figure 5. Result Comparison Between Surrogate Model and Numerical Calculation Model of Nonlinear Plastic Deformation Response
表 2 设计变量取值
Table 2. Values of Design Variables
设计变量 初始值 下限值 上限值 $ {t}_{1} $/mm 3 2 5 $ {t}_{2} $/mm 4 2 5 $ {t}_{3} $/mm 3 2 5 $ {t}_{4} $/mm 5 2 5 
下载: 导出CSV
表 3 优化设计与最初设计对比
Table 3. Comparison between Optimized Design and Initial Design
响应 最初设计值 优化设计值 变化量 总成本/元 7564+X 5436+X 2128/(7564+X) 质量/kg 566 495 12.54% 最大塑性变形/% 23.72 18.93 20.17% 径向位移膨胀/mm 32.5 34.9 −7.38%
下载: 导出CSV
-
[1] 刘翔. HTR-PM乏燃料贮罐跌落工况的安全性分析与计算[D]. 北京: 清华大学, 2015. [2] 林木森. 球床堆燃料贮存容器冲击动力学特性及计算方法的研究[D]. 北京: 清华大学, 2020. [3] HAO Y C, LI Y, LIN M S, et al. Containment integrity and sealing assessment for HTR-PM600 fresh fuel transport package under impact loading[J]. Frontiers in Energy Research, 2022, 10: 914090. doi: 10.3389/fenrg.2022.914090 [4] 郝予琛,王金华,王海涛,等. HTR-PM600新燃料贮存容器跌落冲击安全性能[J]. 清华大学学报:自然科学版,2022, 62(10): 1668-1674. [5] KIM K S, KIM J S, CHOI K S, et al. Dynamic impact characteristics of KN-18 SNF transport cask – Part 2: sensitivity analysis of modeling and design parameters[J]. Annals of Nuclear Energy, 2010, 37(4): 560-571. doi: 10.1016/j.anucene.2009.12.024 [6] HAO Y C, LI Y, WANG J H, et al. Study on the impact limiter design in spent fuel transfer cask in nuclear power plants [C]//Proceedings of 2020 International Conference on Nuclear Engineering Collocated with the ASME 2020 Power Conference. Anaheim, California, USA: ASME, 2020. [7] HAO Y C, LI Y, WU B, et al. Optimization analysis of HTR-PM600 fresh fuel transport container under multiple impact loading conditions[J]. Annals of Nuclear Energy, 2022, 175: 109216. doi: 10.1016/j.anucene.2022.109216 [8] American Society of Mechanical Engineers. 2015 BPVC section II-materials - Part D-properties-(customary): ASME BPVC.II.D.M-2015[R]. New York: American Society of Mechanical Engineers, 2015. [9] LIN M S, WANG J H, WU B, et al. Dynamic analysis of dry storage canister and the spent fuels inside under vertical drop in HTR-PM[J]. Annals of Nuclear Energy, 2021, 154: 108030. doi: 10.1016/j.anucene.2020.108030 [10] 孙其诚, 王光谦. 颗粒物质力学导论[M]. 北京: 科学出版社, 2009: 31-35. [11] 王传青. 白车身前端结构—材料—性能一体化轻量化多目标协同优化设计[D]. 长春: 吉林大学, 2016. [12] SHARMA K, PAWASKAR D N, GUHA A, et al. Optimization of cask for transport of radioactive material under impact loading[J]. Nuclear Engineering and Design, 2014, 273: 190-201. doi: 10.1016/j.nucengdes.2014.03.021 [13] 熊锋. 车身结构轻量化与抗撞性多目标协同优化设计方法研究[D]. 长春: 吉林大学, 2018. [14] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 108. [15] STANDER N, ROUX W, GOEL T, et al. LS-OPT® user's manual [M]. Livermore, California: Livermore Software Technology Corporation, 2022: 584. [16] 王康,张树生,何卫平,等. 基于SPEA2的复杂机械产品选择装配方法[J]. 上海交通大学学报,2016, 50(7): 1047-1053. doi: 10.16183/j.cnki.jsjtu.2016.07.011 [17] 谭琦. 多目标优化算法在多客户批处理机环境下的应用研究[D]. 合肥: 中国科学技术大学, 2012. -
图(6) / 表(3)
计量
- 文章访问数: 170
- HTML全文浏览量: 40
- PDF下载量: 45
- 被引次数: 0
