An automated selection algorithm for nonlinear solvers in MDO
| Autor: | John T. Hwang, Joaquim R. R. A. Martins, Shamsheer S. Chauhan |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Strongly coupled
Mathematical optimization Control and Optimization Computer science Multidisciplinary analysis 010103 numerical & computational mathematics Hybrid approach 01 natural sciences Computer Graphics and Computer-Aided Design Design Practice & Management Mathematical Sciences Computer Science Applications 010101 applied mathematics Nonlinear system Engineering Control and Systems Engineering Robustness (computer science) Convergence (routing) 0101 mathematics Engineering design process Selection algorithm Software |
| Zdroj: | Chauhan, SS; Hwang, JT; & Martins, JRRA. (2018). An automated selection algorithm for nonlinear solvers in MDO. Structural and Multidisciplinary Optimization. doi: 10.1007/s00158-018-2004-5. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/2wm5430h Structural and Multidisciplinary Optimization, vol 58, iss 2 |
| Popis: | There are two major types of approaches that are used for the multidisciplinary analysis (MDA) of coupled systems: fixed-point-iteration-based approaches and coupled Newton-based approaches. Fixed-point-iteration approaches are easier to implement, but can require a large number of iterations or diverge for strongly coupled problems. On the other hand, coupled-Newton approaches have superior convergence orders, but generally require more effort to implement and have more expensive iterations. Additionally, these two major approaches have many variations, including hybrid approaches where the MDA begins with a fixed-point iteration and then switches to a coupled-Newton approach after a certain number of iterations. However, there is a lack of criteria to govern how to select between these approaches, and when to switch between them in a hybrid approach. This paper compares these approaches and provides an algorithm that can be used to automatically select and switch between them. The proposed algorithm is implemented using OpenMDAO, NASA’s open-source framework for multidisciplinary analysis and optimization, and is tested using OpenAeroStruct, an open-source low-fidelity tool for aerostructural optimization. The results show that the proposed algorithm provides a balance of improved robustness and speed. |
| Databáze: | OpenAIRE |
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