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Improved collaboration pursuing method for multidisciplinary robust design optimization

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Abstract

The collaboration pursuing method (CPM) is a computationally efficient approach for deterministic multidisciplinary design optimization (MDO). However, it has not been employed to handle problems with uncertainty. Moreover, obtaining actual uncertainty probability distributions is challenging. Compared to probability distributions, the interval information of uncertainties can be more easily obtained. Thus, multidisciplinary robust design optimization (MRDO) under interval uncertainty has been widely investigated. To overcome the inefficiency of existing methods for solving MRDO, this paper proposes an improved collaboration pursuing method (ICPM). In this method, the collaboration model (CM) is utilized to filter the samples that satisfy the coupled state equations in system analysis (SA) or multidisciplinary analysis (MDA). Then, a robustness discrepancy model (RDM) is developed to efficiently select candidate samples that meet the robustness requirements. Next, the mode pursuing sampling (MPS) method is utilized as a global optimizer to drive the optimization process and identify the robust optimum. Finally, a mathematical example and two engineering examples are utilized to evaluate the feasibility and effectiveness of the proposed method.

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Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 51675196 and 51721092] and the Program for HUST Academic Frontier Youth Team.

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Wei Li, Mi Xiao & Liang Gao

Authors
  1. Wei Li
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  2. Mi Xiao
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  3. Liang Gao
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Corresponding author

Correspondence to Liang Gao.

Additional information

Responsible editor: Pingfeng Wang

Appendix

Appendix

Table 13 CPM results of the mathematical example
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Table 14 ICPM results of the mathematical example
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Table 15 Initial robustness feasible samples for the bulk carrier design problem
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1.1 Aircraft sizing problem

The details of the model are given in Table 16. The empty weight includes four parts: (1) the aircraft structure, (2) the landing gear, (3) the propulsion system, and (4) the equipment.

Table 16 Specific expressions of the aircraft sizing problem
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Table 17 Fixed parameters of the aircraft sizing problem
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Table 18 Initial robustness feasible samples for the aircraft sizing problem
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Li, W., Xiao, M. & Gao, L. Improved collaboration pursuing method for multidisciplinary robust design optimization. Struct Multidisc Optim 59, 1949–1968 (2019). https://doi.org/10.1007/s00158-018-2165-2

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  • DOI: https://doi.org/10.1007/s00158-018-2165-2

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