Model-based thermal system design optimization for the James Webb Space Telescope

被引:13
作者
Cataldo, Giuseppe [1 ]
Niedner, Malcolm B. [1 ]
Fixsen, Dale J. [1 ]
Moseley, Samuel H. [1 ]
机构
[1] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA
基金
美国国家航空航天局;
关键词
thermal model correlation; model-based systems engineering; multidisciplinary design optimization; James Webb Space Telescope;
D O I
10.1117/1.JATIS.3.4.044002
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:7
相关论文
共 38 条
[1]  
Allaire D. L., 2009, THESIS
[2]  
[Anonymous], THESIS
[3]  
[Anonymous], 1939, THESIS U CHICAGO CHI
[4]  
Atkinson A. C., 2008, OPTIMUM EXPT DESIGNS
[5]   Numerical thermal mathematical model correlation to thermal balance test using adaptive particle swarm optimization (APSO) [J].
Beck, T. ;
Bieler, A. ;
Thomas, N. .
APPLIED THERMAL ENGINEERING, 2012, 38 :168-174
[6]   Learning about physical parameters: the importance of model discrepancy [J].
Brynjarsdottir, Jenny ;
O'Hagan, Anthony .
INVERSE PROBLEMS, 2014, 30 (11)
[7]  
Canavan ER, 2006, AIP CONF PROC, V824, P233
[8]  
Cataldo G., 2016, 67 INT ASTR C GUAD M, P1
[9]  
Cataldo G., 2017, 68 INT ASTR C AD AUS
[10]  
Cataldo G., 2015, 66 INT ASTR C JER IS, P1