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Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization

Research output: Contribution to journalArticleScientificpeer-review

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Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization. / Wu, Di; Coatanea, Eric; Wang, G. Gary.

In: Journal of Mechanical Design, Transactions of the ASME, Vol. 141, No. 4, 041402, 04.2019.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Wu, D, Coatanea, E & Wang, GG 2019, 'Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization', Journal of Mechanical Design, Transactions of the ASME, vol. 141, no. 4, 041402. https://doi.org/10.1115/1.4042342

APA

Wu, D., Coatanea, E., & Wang, G. G. (2019). Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization. Journal of Mechanical Design, Transactions of the ASME, 141(4), [041402]. https://doi.org/10.1115/1.4042342

Vancouver

Wu D, Coatanea E, Wang GG. Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization. Journal of Mechanical Design, Transactions of the ASME. 2019 Apr;141(4). 041402. https://doi.org/10.1115/1.4042342

Author

Wu, Di ; Coatanea, Eric ; Wang, G. Gary. / Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization. In: Journal of Mechanical Design, Transactions of the ASME. 2019 ; Vol. 141, No. 4.

Bibtex - Download

@article{7d10e1654c42458bac1923ab35613a1f,
title = "Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization",
abstract = "With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input-output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.",
keywords = "causal graph, dimension reduction, dimensional analysis, multidisciplinary design optimization",
author = "Di Wu and Eric Coatanea and Wang, {G. Gary}",
year = "2019",
month = "4",
doi = "10.1115/1.4042342",
language = "English",
volume = "141",
journal = "Journal of Mechanical Design",
issn = "1050-0472",
publisher = "American Society of Mechanical Engineers",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization

AU - Wu, Di

AU - Coatanea, Eric

AU - Wang, G. Gary

PY - 2019/4

Y1 - 2019/4

N2 - With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input-output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.

AB - With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input-output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.

KW - causal graph

KW - dimension reduction

KW - dimensional analysis

KW - multidisciplinary design optimization

U2 - 10.1115/1.4042342

DO - 10.1115/1.4042342

M3 - Article

VL - 141

JO - Journal of Mechanical Design

JF - Journal of Mechanical Design

SN - 1050-0472

IS - 4

M1 - 041402

ER -