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Research on parameters correction method for thermal model of satellite optomechanical load
Received date: 2023-04-04
Revised date: 2023-04-21
Accepted date: 2023-07-18
Online published: 2023-07-24
Supported by
National Key Research and Development Program of China(2018YFB0504700)
The optical efficiency of satellite optomechanical loads is closely related to thermal design, and model correction of their thermal control system is an essential part of thermal design. In recent years, many reference methods for improving the efficiency and accuracy of thermal model correction based on deep learning and optimization algorithms have emerged both domestically and internationally. However, there is currently no systematic induction. This paper summarizes the new correction methods and focuses on the analysis of several means to improve the correction efficiency in the special problem of satellite optomechanical load thermal control model correction. The means include appropriate optimization algorithm, surrogate model construction and development of automatic correction tools. A specific analysis was conducted on the research progress, applicable conditions, and limitations of these three means, and suggestions were put forward for the development of correction tools. Finally, prospects were made for the field of satellite optomechanical load thermal control model correction, providing direction for improving the accuracy and correction efficiency of thermal models in the future.
Yuhan LI , Baoyu YANG , Yinong WU , Qiang ZHANG , Xiao TANG . Research on parameters correction method for thermal model of satellite optomechanical load[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(6) : 628814 -628814 . DOI: 10.7527/S1000-6893.2023.28814
1 | ROBSON A, HELLER C, SDUNNUS H. Space systems thermal analysis software - a user’s view[C]∥ Proceedings of the International Conference on Environmental Systems. Warrendale: SAE International, 2005. |
2 | HYE A, LIN C. Space station active thermal control system modeling[C]∥ Proceedings of the 26th Aerospace Sciences Meeting. Reston: AIAA, 1988. |
3 | LEE H P. Application of finite-element method in the computation of temperature with emphasis on radiative exchanges[C]∥ Proceedings of the 7th Thermophysics Conference. Reston: AIAA, 1972. |
4 | LEE H P, JACKSON C. Finite-element solution for a combined radiative-conductive analysiswith mixed diffuse-specular surface characteristics[C]∥ Proceedings of the 10th Thermophysics Conference. Reston: AIAA, 1975. |
5 | 苗建印, 钟奇, 赵啟伟. 航天器热控制技术[M]. 北京: 北京理工大学出版社, 2018. |
MIAO J Y, ZHONG Q, ZHAO Q W. Spacecraft thermal control technology[M]. Beijing: Beijing Insititute of Technology Press, 2018 (in Chinese). | |
6 | KIM J H, KIM B. Study on the reduction method of the satellite thermal mathematical model[J]. Advances in Engineering Software, 2017, 108: 37-47. |
7 | 锁斌, 程永生, 曾超. 不确定性处理方法及其在可靠性工程中的应用[C]∥ 全国信息与电子工程第五届学术年会暨四川省电子学会曙光分会第十六届学术年会. 成都:四川省电子学会, 2012. |
SUO B, CHEN Y S, ZENG C. Uncertainty processing methods and their application in reliability engineering[C]∥ The 5th Annual Academic Conference of National Information and Electronic engineering and the 16th Annual Academic Conference of Shuguang Branch of Sichuan Institute of Electronics. Chengdu: Sichuan Institute of Electronics, 2012.(in Chinese) | |
8 | 周海东. 含不确定性参数结构静动态特性的区间分析方法及其应用研究[D]. 哈尔滨: 哈尔滨工业大学, 2014. |
ZHOU H D. A study of interval analysis and application to static/dynamic characteristics of the structure with uncertain parameters[D]. Harbin: Harbin Institute of Technology, 2014 (in Chinese). | |
9 | 马羽. 考虑代理模型不确定性的结构统计灵敏度和可靠性分析方法研究[D]. 成都: 电子科技大学, 2017. |
MA Y. Structural statistical sensitivity analysis and reliability assessment considering metamodeling uncertainty[D]. Chengdu: University of Electronic Science and Technology of China, 2017 (in Chinese). | |
10 | 彭祖军. 复合材料热结构模型的不确定性分析和实验修正方法[D]. 哈尔滨: 哈尔滨工业大学, 2018. |
PENG Z J. Uncertainty analysis and experimental calibration methods of thermal structure model of composite materials[D]. Harbin: Harbin Institute of Technology, 2018 (in Chinese). | |
11 | 阮晓行. 考虑认知参数或多峰随机参数的不确定性分析方法[D]. 长沙: 湖南大学, 2018. |
RUAN X H. Uncertainty analysis methods considering epistemic parameters or multimodal aleatory parameters[D]. Changsha: Hunan University, 2018 (in Chinese). | |
12 | 王涵. 基于响应面的参数不确定性有限元模型修正研究[D]. 南京: 南京航空航天大学, 2021. |
WANG H. Research on finite element model updating of parameter uncertainty based on response surface method[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2021 (in Chinese). | |
13 | 程梅苏. 航天器瞬态热分析模型修正方法及应用研究[D]. 南京: 南京航空航天大学, 2016. |
CHENG M S. Research on correction method and application of the transient thermal model of spacraft[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2016 (in Chinese). | |
14 | 闵桂荣. 卫星热控制技术[M]. 北京: 中国宇航出版社, 1991. |
MIN G R. Satellite thermal control technology[M]. Beijng: China Astronautic Publishing House, 1991 (in Chinese). | |
15 | HERRERA F L, SEPúLVEDA A T. Stochastic approach to spacecraft thermal control subsystem[C]∥ Proceedings of the International Conference on Environmental Systems. Copenhagen: ICES, 2000. |
16 | TORRALBO I, PEREZ-GRANDE I, SANZ-ANDRES A, et al. Correlation of spacecraft thermal mathematical models to reference data[J]. Acta Astronautica, 2018, 144: 305-319. |
17 | TORRES A, MISHKINIS D, KAYA T. Mathematical modeling of a new satellite thermal architecture system connecting the east and west radiator panels and flight performance prediction[J]. Applied Thermal Engineering, 2014, 65(1-2): 623-632. |
18 | BECK T, BIELER A, THOMAS N. Numerical thermal mathematical model correlation to thermal balance test using adaptive particle swarm optimization (APSO)[J]. Applied Thermal Engineering, 2012, 38: 168-174. |
19 | DOENECKE J. Adjustment of a thermal mathematical model to test data[J]. Journal of Spacecraft and Rockets, 1970, 7(6): 720-726. |
20 | TOUSSAINT M. Verification of the thermal mathematical model for artificial satellites - a new test philosophy[C]∥ Proceedings of the 2nd Thermophysics Specialist Conference. Reston: AIAA, 1967. |
21 | SHIMOJI S. A comparison of thermal network correction methods[C]∥ Proceedings of the 16th Thermophysics Conference. Reston: AIAA, 1981. |
22 | 王家映. 地球物理资料非线性反演方法讲座(二) 蒙特卡洛法[J]. 工程地球物理学报, 2007, 4(2): 81-85. |
WANG J Y. Lecture on non-linear inverse methods in geophysics (Ⅱ) Monte Carlo method[J]. Chinese Journal of Engineering Geophysics, 2007, 4(2): 81-85 (in Chinese). | |
23 | TOUSSAINT M. Verification of the thermal mathematical model for artificial satellites: A new test philosophy[M]∥ Thermophysics of Spacecraft and Planetary Bodies. Amsterdam: Elsevier, 1967: 611-629. |
24 | ISHIMOTO T, GASKI J D, FINK L C. Development of digital computer program for thermal network correction: NASA-CR-108681[R]. Washington, D.C.: NASA, 1970. |
25 | 闵桂荣, 姜贵庆, 侯增祺, 等. 美国空间热物理研究近况[J]. 国外空间技术, 1979(3): 1-23. |
MIN G R, JIANG G Q, HOU Z Q, et al. Recent status of space thermophysics research in the United States[J]. Foreign Space Technology, 1979(3): 1-23 (in Chinese). | |
26 | 翁建华, 闵桂荣, 潘增富. 利用稳态数据修正航天器热网络方程及其系数[J]. 工程热物理学报, 1998, 19(2): 218-223. |
WENG J H, MIN G R, PAN Z F. Correcting spacecraft thermal network and its coefficients with ultimate thermal vacuum test data[J]. Journal of Engineering Thermophysics, 1998, 19(2): 218-223 (in Chinese). | |
27 | HARVEY S, LEBRU A, KERNER R, et al. Thermal design of the Envisat-1 ASAR active antenna[C]∥ Proceedings of the Sixth European Symposium on Space Environmental Control Systems. Paris: ESA, 1997. |
28 | MAURO J C. Monte Carlo techniques[M]∥ Materials Kinetics. Amsterdam: Elsevier, 2021: 443-466. |
29 | DUVIGNACQ C, HESPEL L, ROZé C, et al. Modelling of white paints optical degradation using Mie’s theory and Monte Carlo method[C]∥ Proceedings of the 9th International Symposium on Materials in a Space Environment. Paris: ESA, 2003: 399-406. |
30 | ANGLADA E, MARTINEZ-JIMENEZ L, GARMENDIA I. Performance of gradient-based solutions versus genetic algorithms in the correlation of thermal mathematical models of spacecrafts[J]. International Journal of Aerospace Engineering, 2017, 2017: 1-12. |
31 | GóMEZ-SAN-JUAN A, PéREZ-GRANDE I, SANZ-ANDRéS A. Uncertainty calculation for spacecraft thermal models using a generalized SEA method[J]. Acta Astronautica, 2018, 151: 691-702. |
32 | GARMENDIA I, ANGLADA E. Thermal parameters identification in the correlation of spacecraft thermal models against thermal test results[J]. Acta Astronautica, 2022, 191: 270-278. |
33 | GARMENDIA I, ANGLADA E. Transient thermal parameters correlation of spacecraft thermal models against test results[J]. Acta Astronautica, 2022, 199: 49-57. |
34 | 钟奇, 潘维, 王玉莹, 等. 航天器热模型修正技术进展研究[J]. 航天器工程, 2021, 30(1): 64-71. |
ZHONG Q, PAN W, WANG Y Y, et al. Survey of spacecraft thermal model correlation technology development[J]. Spacecraft Engineering, 2021, 30(1): 64-71 (in Chinese). | |
35 | 樊越. 航空相机光机热分析与热控技术研究[D]. 成都: 中国科学院研究生院(光电技术研究所), 2013. |
FAN Y. Thermal/structural/optical analysis and thermal control technique of aerial camera[D]. Chengdu: Institute of Optics and Electronics, Chinese Academy of Sciences, 2013 (in Chinese). | |
36 | 李强. CO2探测仪热设计及热分析模型修正技术研究[D]. 长春: 中国科学院长春光学精密机械与物理研究所, 2017. |
LI Q. Study on the thermal design for carbon dioxide and technique of thermal analysis model correction[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2017 (in Chinese). | |
37 | 吴愉华. 地球静止轨道太阳X-EUV成像仪探测器组件热控技术研究[D]. 长春: 中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2019. |
WU Y H. Study on the thermal control technique for detector components in geostationary solar X-EUV imager[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2019 (in Chinese). | |
38 | 杨雨霆. 高空气球平台地—月成像光谱仪载荷系统热控技术研究[D]. 长春: 中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2020. |
YANG Y T. Study on the thermal control technique for Earth-Moon imaging spectrometer load system for high-altitude balloon[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2020 (in Chinese). | |
39 | 李世俊. 太阳X-EUV成像仪热控关键技术研究[D].长春:中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2020. |
LI S J. Study on the key technique of thermal control for solar X-ray and extreme ultraviolet imager[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2020.(in Chinese) | |
40 | 杨沪宁, 钟奇. 航天器热模型蒙特卡罗法修正论述[J]. 航天器工程, 2009, 18(3): 53-58. |
YANG H N, ZHONG Q. Monte-carlo method for thermal model correction of spacecraft[J]. Spacecraft Engineering, 2009, 18(3): 53-58 (in Chinese). | |
41 | 张镜洋, 常海萍, 王立国. 小卫星瞬态热分析模型修正方法[J]. 中国空间科学技术, 2013, 33(4): 24-30. |
ZHANG J Y, CHANG H P, WANG L G. Correction method for transient thermal analysis model of small satellite[J]. Chinese Space Science and Technology, 2013, 33(4): 24-30 (in Chinese). | |
42 | KIM K W, BAEK S W, KIM M Y, et al. Estimation of emissivities in a two-dimensional irregular geometry by inverse radiation analysis using hybrid genetic algorithm[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2004, 87(1): 1-14. |
43 | KLEMENT J. On using quasi-newton algorithms of the broyden class for model-to-test correlation[J]. Journal of Aerospace Technology and Management, 2014, 6(4): 407-414. |
44 | ANGLADA E, GARMENDIA I. Correlation of thermal mathematical models for thermal control of space vehicles by means of genetic algorithms[J]. Acta Astronautica, 2015, 110: 355. |
45 | 黄芳, 樊晓平. 基于岛屿群体模型的并行粒子群优化算法[J]. 控制与决策, 2006, 21(2): 175-179, 188. |
HUANG F, FAN X P. Parallel particle swarm optimization algorithm with island population model[J]. Control and Decision, 2006, 21(2): 175-179, 188 (in Chinese). | |
46 | HU X Z, CHEN X Q, ZHAO Y, et al. Optimization design of satellite separation systems based on multi-island genetic algorithm[J]. Advances in Space Research, 2014, 53(5): 870-876. |
47 | YUAN C, LI L, LUO X B. Heat conduction optimization of anisotropic composite material using simulated annealing algorithm[C]∥ Proceedings of the 15th International Heat Transfer Conference. London: AIHTC, 2014. |
48 | 李楠. 基于遗传算法的瞬态非线性热传导反问题研究[D]. 大连: 大连理工大学, 2014. |
LI N. Research on transient non-linear inverse heat conduction problems based on a genetic algorithm[D]. Dalian: Dalian University of Technology, 2014 (in Chinese). | |
49 | 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012. |
LI H. Statistical learning method[M]. Beijing: Tsinghua University Press, 2012 (in Chinese). | |
50 | TALHAMAINUDDIN ANSARY M ABU. A Newton-type proximal gradient method for nonlinear multi-objective optimization problems[J]. Optimization Methods and Software, 2023, 38(3): 570-590. |
51 | CARTIS C, GOULD N I M, TOINT P L. On the complexity of steepest descent, Newton’s and regularized Newton’s methods for nonconvex unconstrained optimization problems[J]. SIAM Journal on Optimization, 2010, 20(6): 2833-2852. |
52 | 李守巨. 基于计算智能的岩土力学模型参数反演方法及其工程应用[D]. 大连: 大连理工大学, 2004. |
LI S J. Parameter identification procedures in geotechnical engineering with computational intelligences and their applications[D]. Dalian: Dalian University of Technology, 2004 (in Chinese). | |
53 | KLEMENT J, ANGLADA E, GARMENDIA I. Advances in automatic thermal model to test correlation in space industry[C]∥ Proceedings of the 46th International Conference on Environmental Systems.Copenhagen: ICES, 2016. |
54 | 徐萃薇, 孙绳武. 计算方法引论[M].3版. 北京: 高等教育出版社, 2007. |
XU C W, SUN S W. Introduction to numerical calculation methods[M].3rd ed. Beijing: Higher Education Press, 2007 (in Chinese). | |
55 | 陈文. 基于灵敏度与模拟退火方法的模型修正及软件二次开发[D]. 南京: 南京航空航天大学, 2008. |
CHEN W. Model updating and redevelopment based on sensitivity and simulated annealing method[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008 (in Chinese). | |
56 | 王晓军, 倪博文, 王磊, 等. 一种基于超体积迭代策略的全局寻优算法[C]∥ 中国力学大会.北京:中国力学学会, 2017: 1164-1175. |
WANG X J, NI B W, WANG L, et al. A global optimization algorithm based on Hyper-volume Iteration (HVI)[C]∥ Proceedings of the Chinese Congress of Theoretical and Applied Mechanics. Beijing: CSTAM, 2017: 1164-1175 (in Chinese). | |
57 | GHOSH S, MONDAL S, KAPAT J S, et al. Shape optimization of pin fin arrays using Gaussian process surrogate models under design constraints[C]∥ Proceedings of ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. London: ASME, 2020. |
58 | XIONG Y, GUO L, TIAN D F, et al. Intelligent optimization strategy based on statistical machine learning for spacecraft thermal design[J]. IEEE Access, 2020, 8: 204268-204282. |
59 | REZK H, BABU T S, AL-DHAIFALLAH M, et al. A robust parameter estimation approach based on stochastic fractal search optimization algorithm applied to solar PV parameters[J]. Energy Reports, 2021, 7: 620-640. |
60 | YANG C, HOU X B. Iterative two-layer thermal design strategy for step sandwich antenna of space solar power satellite using modified constrained multi-objective optimization[J]. Aerospace Science and Technology, 2021, 118: 106987. |
61 | OTAKI D, NONAKA H, YAMADA N. Thermal design optimization of electronic circuit board layout with transient heating chips by using Bayesian optimization and thermal network model[J]. International Journal of Heat and Mass Transfer, 2022, 184: 122263. |
62 | CHANG R L, HAN J, DUAN R X, et al. Optimization of geometric parameters of gear shaper cutter based on multi-island genetic algorithm[C]∥ Proceedings of the 2021 7th International Conference on Computing and Artificial Intelligence. New York: ACM, 2021: 446-451. |
63 | BABAL?K A, ??CAN H, BABAO?LU ?, et al. An improvement in fruit fly optimization algorithm by using sign parameters[J]. Soft Computing, 2018, 22(22): 7587-7603. |
64 | VENKATESHWAR RAO B, PANDA S. Spider monkey optimization algorithm-based optimal design of wideband EBG structure for certain uncertainty parameters[J]. Journal of Uncertain Systems, 2023, 16(1): 2242004. |
65 | SHAHSAVANI D, GRIMVALL A. Variance-based sensitivity analysis of model outputs using surrogate models[J]. Environmental Modelling & Software, 2011, 26(6): 723-730. |
66 | YANG Y T, CHEN L H, XIONG Y, et al. Global sensitivity analysis based on BP neural network for thermal design parameters[J]. Journal of Thermophysics and Heat Transfer, 2021, 35(1): 187-199. |
67 | 员婉莹. 结构可靠性及全局灵敏度分析算法研究[D]. 西安: 西北工业大学, 2019. |
YUN W Y. Research on algorithms of reliability analysis and global sensitivity analysis of the structures[D]. Xi’an: Northwestern Polytechnical University, 2019 (in Chinese). | |
68 | 熊琰. 基于深度学习的空间望远镜智能自主热控关键技术研究[D]. 长春: 中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2022. |
XIONG Y. Research on the key technology of intelligent thermal control for space telescope based on deep learning[D]. Changchun: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 2022 (in Chinese). | |
69 | 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016. |
ZHOU Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016 (in Chinese). | |
70 | HERRERA L J, POMARES H, ROJAS I, et al. Global and local modelling in RBF networks[J]. Neurocomputing, 2011, 74(16): 2594-2602. |
71 | 陆强华. 基于径向基函数神经网络的结构可靠性分析[J]. 中国科技信息, 2008(5): 234-235. |
LU Q H. Structural reliability analysis based on radial basis function neural network[J]. China Science and Technology Information, 2008(5): 234-235 (in Chinese). | |
72 | PALAR P S, PARUSSINI L, BREGANT L, et al. Composite kernel functions for surrogate modeling using recursive multi-fidelity kriging[C]∥ Proceedings of the AIAA SCITECH 2022 Forum. Reston: AIAA, 2022. |
73 | 张崎. 基于Kriging方法的结构可靠性分析及优化设计[D]. 大连: 大连理工大学, 2005. |
ZHANG Q. Structural reliability analysis and optimization based on kriging technique[D]. Dalian: Dalian University of Technology, 2005 (in Chinese). | |
74 | WELCH W J, BUCK R J, SACKS J, et al. Screening, predicting, and computer experiments[J]. Technometrics, 1992, 34(1): 15. |
75 | 窦毅芳, 刘飞, 张为华. 响应面建模方法的比较分析[J]. 工程设计学报, 2007, 14(5): 359-363. |
DOU Y F, LIU F, ZHANG W H. Research on comparative analysis of response surface methods[J]. Journal of Engineering Design, 2007, 14(5): 359-363 (in Chinese). | |
76 | 韩彦彬, 白广忱, 李晓颖, 等. 基于支持向量机柔性机构动态可靠性分析[J]. 机械工程学报, 2014, 50(11): 86-92. |
HAN Y B, BAI G C, LI X Y, et al. Dynamic reliability analysis of flexible mechanism based on support vector machine[J]. Journal of Mechanical Engineering, 2014, 50(11): 86-92 (in Chinese). | |
77 | ZHANG K S, HE S J, HAN Z H. Comparative studies of support vector regression and kriging - theory and applications[C]∥ Proceedings of the 2018 Multidisciplinary Analysis and Optimization Conference. Reston: AIAA, 2018. |
78 | ZHAO L A, CHOI K K, LEE I. Metamodeling method using dynamic kriging for design optimization[J]. AIAA Journal, 2011, 49(9): 2034-2046. |
79 | ZHENG J, SHAO X Y, GAO L A, et al. A hybrid variable-fidelity global approximation modelling method combining tuned radial basis function base and kriging correction[J]. Journal of Engineering Design, 2013, 24(8): 604-622. |
80 | TAO J, SUN G. Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization[J]. Aerospace Science and Technology, 2019, 92: 722-737. |
81 | BARTZ-BEIELSTEIN T, REHBACH F, SEN A, et al. Surrogate model based hyperparameter tuning for deep learning with SPOT[EB/OL]. 2021: arXiv: 2105.14625. |
82 | JIN R, CHEN W, SIMPSON T W. Comparative studies of metamodelling techniques under multiple modelling criteria[J]. Structural and Multidisciplinary Optimization, 2001, 23(1): 1-13. |
83 | BUCHER C G, BOURGUND U. A fast and efficient response surface approach for structural reliability problems[J]. Structural Safety, 1990, 7(1): 57-66. |
84 | DENG J. Structural reliability analysis for implicit performance function using radial basis function network[J]. International Journal of Solids and Structures, 2006, 43(11-12): 3255-3291. |
85 | RAJASHEKHAR M R, ELLINGWOOD B R. A new look at the response surface approach for reliability analysis[J]. Structural Safety, 1993, 12(3): 205-220. |
86 | BAESENS B, VIAENE S, GESTEL T V, et al. Least squares support vector machine classifiers: An empirical evaluation[J]. Access & Download Statistics, 2000, 3: 2376. |
87 | FREY B, BOHNE N, BRUNO M. Development, benchmarking and validation of an automated thermal model correlation tool[C]∥ Proceedings of the 46th International Conference on Environmental Systems. Copenhagen: ICES, 2016: 303. |
88 | FREY B, TRINOGA M, HOPPE M, et al. Development of an automated thermal model correlation method and tool[C]∥ Proceedings of the 45th International Conference on Environmental Systems. Copenhagen: ICES, 2015: 12-16. |
89 | 李欢欢. 星载天线热分析平台的研究与开发[D]. 西安: 西安电子科技大学, 2008. |
LI H H. Research and development of thermal analysis platform for satellite antenna[D]. Xi’an: Xidian University, 2008 (in Chinese). | |
90 | 李欢欢, 朱敏波, 张庞岭. I-DEAS二次开发技术在星载天线热分析中的应用[J]. 机械设计与制造, 2008(7): 92-94. |
LI H H, ZHU M B, ZHANG P L. Application of secondary development technology based-on I-DEAS in the thermal analysis for satellite antenna[J]. Machinery Design & Manufacture, 2008(7): 92-94 (in Chinese). | |
91 | 施道云, 杨光, 张卫国, 等. 基于Isight/Fluent联合仿真的热模型修正方法研究[J]. 科学技术与工程, 2016, 16(4): 205-209, 220. |
SHI D Y, YANG G, ZHANG W G, et al. Thermal model modifying based on the combination of isight and fluent[J]. Science Technology and Engineering, 2016, 16(4): 205-209, 220 (in Chinese). |
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