Multidisciplinary Collaborative Optimization of General Parameters of Low Orbit Spacecraft

Article Preview

Abstract:

This article takes the optimization of general parameters of low-orbit spacecraft as object. Three subsystems of attitude and orbit control subsystem, power subsystem and structure subsystem were researched. Coupling relations between subsystems and system were described. Analysis models of the subsystems were provided. Meanwhile, optimization models of the researched problem were established based on collaborative optimization (CO). Sequential quadratic programming (NLPQL) was selected as search strategy of CO subsystem optimizer. While NLPQL, adaptive simulated annealing algorithm (ASA) and multi-island genetic algorithm (MIGA) were respectively selected as the search strategy of CO system-level optimizer to search. Furthermore, multidisciplinary feasible method (MDF) was used to optimize the same problem. NLPQL, ASA, MIGA were respectively selected as the search strategy of MDF system-level optimizer. The optimization result of CO was compared to the optimization result of MDF shows that using CO can get the result which close to the optimal result. That proves CO can be effectively applied to multidisciplinary design optimization of the general parameters of the low-orbit spacecraft.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1482-1489

Citation:

Online since:

December 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Huang Hai,Tan Chunlin. Discussion on Modeling and Multidisciplinary Design Optimization for the Satellite System Parameters. Spacecraft Engineering, 2007, 16(3):38-42. ( in Chinese)

Google Scholar

[2] Current State of the Art in Multidisciplinary Design Optimization. An AIAA White Paper. September 1991, Washington, D.C.

Google Scholar

[3] Braun R D .Use of the Collaborative Optimization Architecture for Launch Vehicle. AIAA 96-4018, 1996.

Google Scholar

[4] Sun Lefeng, Zhang Bainan. Multidisciplinary Design Optimization of Main Parameters of Low Orbit Manned Spacecraft. Manned Spaceflight Forum, September 2012, Beijing. ( in Chinese)

Google Scholar

[5] Wertz J R,I.arson W J.Space mission analysis and design[M].Third Edition.Torrance.California:Microcosm Press.1999.

Google Scholar

[6] I. Kroo. Decomposition and collaborative optimization for large-scale aerospace design. In Multidisciplinary Design Optimization: State of the Art, SIAM Publications, 1995.

Google Scholar

[7] Mark Aaron. APPLICATION OF SEQUENTIAL QUADRATIC PROGRAMMING TO LARGE-SCALE STRUCTURAL DESIGN PROBLEMS. THESIS, Captain, USAF, 1994.

Google Scholar

[8] TUSHAR G, NIELEN S. Adaptive simulated annealing for global optimization in. LS-OPT[EB/OL]. [2010-08-22]. http://www. Ingber.com

Google Scholar

[9] Xing Wenxun, Xie Jinxing. Modern optimization algorithm [M]. Beijing: Tsinghua University Press ,1999.140-191.( in Chinese)

Google Scholar

[10] Nathan P. Tedford , Joaquim R. R. A. Martins .On the Common Structure of MDO Problems: A Comparison of Architectures. 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, September 2006, Portsmouth, Virginia.

DOI: 10.2514/6.2006-7080

Google Scholar