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Multidisciplinary Design Optimization: A Survey of Architectures
Abstract
Multidisciplinary design optimization is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of multidisciplinary design optimization, various methods (architectures) have been developed and applied to solve multidisciplinary design-optimization problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitate the understanding of the various architectures, and how they relate to each other. A classification of the multidisciplinary design-optimization architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both theoretical and experimental perspectives. For each architecture, several applications to the solution of engineering-design problems are cited. The result is a comprehensive but straightforward introduction to multidisciplinary design optimization for nonspecialists and a reference detailing all current multidisciplinary design-optimization architectures for specialists.
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Cites
Zingg, D. W., Nemec, M., & Pulliam, T. H. (2008) · European Journal of Computational Mechanics · doi:10.3166/remn.17.103-126 · Cited by 186
Framework for Aircraft Conceptual Design and Environmental Performance Studies
Antoine, N. E., & Kroo, I. M. (2005) · AIAA Journal · doi:10.2514/1.13017 · Cited by 155
Cited by
Liu, H., Ong, Y.-S., & Cai, J. (2018) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-017-1739-8 · Cited by 444
Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles
Silvas, E., Hofman, T., Murgovski, N., Etman, P., & Steinbuch, M. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2547897 · Cited by 323
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., et al. (2021) · AIAA Journal · doi:10.2514/1.j060131 · Cited by 270
Metadata
- Authors
- Martins, J. R. R. A., & Lambe, A. B.
- Venue
- AIAA Journal
- Published
- 2013-09
- Volume
- 51
- Issue
- 9
- Pages
- 2049-2075
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W2021012767
- Cited by
- 1,111
- References
- 2
- FWCI
- 77.89
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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