基于NSGA-Ⅱ算法的固定式蜂窝板辐射器优化研究

王建鹏; 郭彤; 陈亮

doi:10.11728/cjss2025.06.2024-0177

基于NSGA-Ⅱ算法的固定式蜂窝板辐射器优化研究

doi: 10.11728/cjss2025.06.2024-0177 cstr: 32142.14.cjss.2024-0177

王建鹏^1, ,,
郭彤^{1, 2},
陈亮¹

1.
中国科学院微小卫星创新研究院　上海　200050
2.
上海微小卫星工程中心　上海　200120

详细信息

通讯作者:

王建鹏　男, 1998年7月出生于辽宁省辽阳市, 现为中国科学院微小卫星创新院工程师、主任设计师, 主要研究方向为航天器热控设计、卫星载荷散热优化, 至今已参与近百余颗卫星的批产研制工作. E-mail: wangjianpenghit@163.com

中图分类号: V476
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出版历程
- 收稿日期: 2024-12-03
- 修回日期: 2025-03-12
- 网络出版日期: 2025-03-19

Optimization of Fixed Honeycomb Panel Radiator Based on NSGA-II Algorithm

WANG Jianpeng^{1
, ,},
GUO Tong^{1, 2},
CHEN Liang¹

1.
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 200050
2.
Shanghai Micro Satellite Engineering Center, Shanghai 200120

摘要

摘要: 空间辐射器是航天热控系统的重要组成部分. 为了满足某低轨卫星散热与减重需求, 借助反设计理念提出一种固定式蜂窝板辐射器的优化策略, 由宏观与微观传热角度阐述了辐射器性能改善的根本原因. 以热管–管路布局参数作为设计变量, 采用Kriging构建代理模型, 基于NSGA-II算法迭代优化得到方案α与方案β. 仿真结果表明, 优化方案在降低辐射器质量约1/4的基础上, 表面温度均匀性分别提高3.09 K与4.98 K, 散热能力分别提高18.7%与28.8%. 对优化前后卫星绕轨运行温度水平进行对比分析, 结果表明辐射器的优化设计使航天器热控系统具有更大的温度控制余量, 并且具备显著的减重优势, 有助于航天器在轨任务的进行与拓展.
- 空间辐射器 /
- NSGA-II算法 /
- 代理模型 /
- 敏感性分析
Abstract: Space radiator is an important part of aerospace thermal control system. In order to meet the heat dissipation and weight reduction requirements of a low-orbit satellite, an optimization strategy of fixed honeycomb plate space radiator has been proposed with the help of inverse design concept, and the root cause of space radiator performance improvement has been expounded from the perspective of macro and micro heat transfer. Taking the layout parameters of heat pipes and fluid loop as the design variables, Kriging was used to construct the surrogate model, and schemes α and β were obtained by iterative optimization based on NSGA-II algorithm. The simulation results show that the optimization schemes improve the surface temperature uniformity by 3.09 K and 4.98 K respectively, and improve the heat dissipation capacity by 18.7% and 28.8% on the basis of reducing the mass ratio by about 1/4. The on-orbit temperature levels of satellite were compared and analyzed. The verification results show that the optimal design of the radiator makes the spacecraft thermal control system have greater temperature control margin and significant weight reduction advantages, which is more conducive to the development and expansion of spacecraft on-orbit tasks.
- Space radiator /
- NSGA-II algorithm /
- Surrogate model /
- Sensitivity analysis

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图 1 空间辐射器传热路径及原理

Figure 1. Heat transfer path and schematic diagram of space radiator

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图 2 空间辐射器及预埋管路

Figure 2. Space radiator and embedded pipeline

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图 3 热管数目N_h对辐射器散热能力的影响

Figure 3. Influence of the number of heat pipes (N_h) on the radiator heat dissipation capacity

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图 4 辐射器整体网格划分情况

Figure 4. Overall grid division of the space radiator

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图 5 设计参数对目标变量的影响

Figure 5. Influence of design parameters on the target variables

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图 6 设计参数敏感性分析

Figure 6. Sensitivity analysis of design parameters

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图 7 衍生点分布及优化方案选取

Figure 7. Derivative point distribution and optimization scheme selection

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图 8 优化方案前后特征截面热参数分布

Figure 8. Thermal parameter distribution of characteristic section before and after optimization

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图 9 空间辐射器特征位置

Figure 9. Characteristic position of the space radiator

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图 10 优化前后各方案纵向特征截面热参数分布

Figure 10. Thermal parameter distribution of longitudinal characteristic section before and after optimization

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图 11 各区域温差熵产及比例

Figure 11. Entropy generation and proportion of temperature difference in each region

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图 12 绕轨运行辐射器流体进出管口温度(n为绕轨周期数)

Figure 12. Inlet and outlet temperature of the fluid during the on-orbit operation (n indicates the number of orbital revolutions)

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图 13 舱内典型设备平均温度

Figure 13. Average temperature of typical equipment in the cabin

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表 1 材料热物性参数

Table 1. Thermal physical parameters of materials

物质名称	密度/(kg·m^–3)	等效热导率/(W·m^–1·K^–1)	比热/(J/kg^–1·K^–1)
乙二醇溶液	1111.4	0.252	2415
铝蒙皮	1430	115	880
蜂窝芯^[16]	27	面内1.2, 法向2.0	891
外贴热管	1006	12000	910

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表 2 优化前后方案设计变量取值(单位: mm)

Table 2. Design variable values before and after optimization (Unit: mm)

	a	b	c	d	x	l₁	l₂	l₃	l₄	l₅	l₆
原方案	100	25	50	90	72	1 200	1 200	1 200	1 200	1 200	1 200
方案α	73.4	33.4	77.1	47.2	93.6	1 243	1 046	1 038	990	1 010	1 261
方案β	63.7	33.3	77.6	61.9	80.6	1 043	1 051	957	959	1 070	1 089

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