.png){kind=icon}
.png){kind=icon}
Return
Multi-model management for time-dependent multidisciplinary design optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-020-02572-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In previous work, we presented a relative adequacy framework (RAF) for multi-model management in single- and multi-disciplinary design optimization to select the most efficient model in different areas of the design space as the latter is being explored during the optimization process. In this work, we extend our framework to allow the consideration of time-dependent multidisciplinary design optimization (MDO) problems with particular emphasis on strongly coupled transient fluid-structure interaction (FSI). The computational models used for analysis in the problems under consideration are typically not amenable to reliable and cost-effective approximation of gradients. Therefore, we use the mesh adaptive direct search optimization algorithm in conjunction with a trust-region approach for multi-model management. The algorithm is modified to automate the search for adequate modeling parameters during the time-dependent multidisciplinary analysis. We show that multidisciplinary temporal interactions may have a significant impact on multi-model management; models that could have been selected in a time-invariant MDO environment may be inadequate in certain areas of the design space in a time-dependent MDO context. At the same time, the relative adequacy framework may improve multidisciplinary temporal analysis convergence and/or MDO coordination. We demonstrate the proposed RAF for multi-model management in time-dependent MDO using a flexible plate FSI example.
Keywords:
Multidisciplinary analysis
Multidisciplinary design optimization
Relative adequacy framework
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
4.8K
Citations:
1.7W