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Multi-Fidelity Modeling and Adaptive Co-Kriging Based Optimization for All-Electric GEO Satellite Systems

[+] Author Affiliations
Renhe Shi, Li Liu, Teng Long, Yufei Wu

Beijing Institute of Technology, Beijing, China

G. Gary Wang

Simon Fraser University, Surrey, BC, Canada

Paper No. DETC2018-85335, pp. V02BT03A033; 12 pages
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2B: 44th Design Automation Conference
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5176-0
  • Copyright © 2018 by ASME


All-electric GEO satellite systems design is a challenging multidisciplinary design optimization (MDO) problem, which is computation-intensive due to the employment of expensive simulations. In this paper, the all-electric GEO satellite MDO problem with multi-fidelity models is investigated. The MDO problem involving six inter-coupled disciplines is formulated to minimize the total mass of the satellite system subject to a number of engineering constraints. To reduce the computational cost of the multidisciplinary analysis (MDA) process, multi-fidelity transfer dynamics models and finite element analysis (FEA) models are developed for the geosynchronous transfer orbit (GTO) and structure disciplines respectively. To effectively solve the all-electric GEO satellite MDO problem using multi-fidelity models, an adaptive Co-Kriging based optimization framework is proposed. In this framework, the samples from a high-fidelity MDA process are integrated with those from a low-fidelity MDA process to create a Co-Kriging metamodel with moderate computational cost for optimization. Besides, for refining the Co-Kriging metamodels, a multi-objective adaptive infill sampling approach is developed to produce the infill sample points in terms of expected improvement (EI) and probability of feasibility (PF) functions. Optimization results show that the proposed optimization framework can significantly reduce the total mass of satellite system with limited computational budget, which demonstrates the effectiveness and practicality of the multi-fidelity modeling and adaptive Co-Kriging based optimization framework for all-electric GEO satellite systems design.

Copyright © 2018 by ASME



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