| 杨雁飞,薄凯,赵乾凯,黄道春,陈俊全.考虑改进LBL模型与NSGA-II算法的多层绝热多目标优化[J].电子测量与仪器学报,2026,40(1):215-227 |
| 考虑改进LBL模型与NSGA-II算法的多层绝热多目标优化 |
| Multi objective optimization of multi-layer insulation materials consideringimproved LBL model and NSGA-II algorithm |
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| DOI： |
| 中文关键词: 超导电机 多层绝热 非支配排序遗传算法 层与层模型 变密度 |
| 英文关键词:superconducting motor multi-layer insulation non-dominated sorting genetic algorithm layer to layer model variable density |
| 基金项目:湖北省自然科学基金（2025AFC075）、电磁能技术全国重点实验室基金（6142217242050201，614221725020502）项目资助 |
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| Author | Institution |
| Yang Yanfei | School of Electrical Engineering and Automation， Wuhan University, Wuhan 430072,China |
| Bo Kai | 2.National Key Laboratory of
Electromagnetic Energy, Naval University of Engineering, Wuhan 430033,China; 3.East Lake Laboratory, Wuhan 430073,China |
| Zhao Qiankai | School of Electrical Engineering, Southeast University, Nanjing 210096,China |
| Huang Daochun | School of Electrical Engineering and Automation， Wuhan University, Wuhan 430072,China |
| Chen Junquan | 2.National Key Laboratory of
Electromagnetic Energy, Naval University of Engineering, Wuhan 430033,China; 3.East Lake Laboratory, Wuhan 430073,China |
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| 中文摘要: |
| 针对超导电机多层绝热（multi-layer insulation, MLI）材料低热流密度需求与紧凑型整机要求的设计矛盾，提出一种基于改进逐层（layer by layer，LBL）模型与二代非支配排序遗传算法的多层绝热多目标协同优化方法。首先，基于辐射传热、气体导热与固体导热方程，通过引入间隔层光学性质、反射屏开孔率及动态自适应系数等关键参数，提升常规LBL模型的计算精度。其次，结合各项传热占比特点，构建最大为四密度区的变密度MLI模型。然后，采用非支配排序遗传算法，以各密度区层数为优化变量，以改进LBL模型作为算法适应度计算函数，约束各密度区层数以及各密度区层数之和，经种群进化得到Pareto前沿。在此基础上，进一步分析了绝热材料热流密度与密度区数量、各密度区层数、层密度的关系以及变密度敷设对热流分布的调控能力。研究结果表明，优化方案中热流密度覆盖0.42~3.11 W/m2、厚度覆盖5.5~43.0 mm，敷设方式覆盖不同层密度的定密度方案、两密度区变密度、三密度区变密度以及四密度区变密度方案。通过调控密度区数量以及密度区的层数、层密度可以实现多层绝热材料优化，降低后续施工难度。 |
| 英文摘要: |
| To resolve the design conflict between low heat flux density requirements in superconducting motor multi-layer insulation material and structural compactness demands, this study proposes a multi-objective optimization method for multilayer insulation based on an improved layer by layer model (LBL) and non-dominated sorting genetic algorithm. First, we enhanced the conventional LBL model’s computational accuracy by incorporating key parameters—spacer optical properties, reflective screen aperture ratio, and a dynamic adaptive coefficient—derived from fundamental radiative, gaseous, and solid conductive heat transfer equations. Second, we constructed a variable-density MLI model with up to four distinct density zones, accounting for relative heat transfer contributions. Finally, employing a non-dominated sorting genetic algorithm with layer counts per density zone as design variables and the improved LBL model as fitness function, we optimized the system under layer-count constraints per zone and total layer count, yielding the Pareto frontier through population evolution. Based on this, we further analyzed the relationships governing MLI heat flux density in relation to three key design parameters:the number of density zones, layer count per density zone, and layer density. Concurrently, we assessed the regulatory effects of variable-density configurations on heat flux distribution. Results demonstrate that the optimized solutions span heat flux densities of 0.42 to 3.11 W/m2 and thicknesses of 5.5 to 43.0 mm, encompassing four configuration types:uniform-density layouts, and variable-density configurations with two, three, or four distinct density zones. By adjusting the number of density zones and the layer density and number of layers in the density zones, multi-layer insulation material optimization can be achieved, reducing the difficulty of subsequent construction. |
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