Research Papers

A Decomposition Method for Exploiting Parallel Computing Including the Determination of an Optimal Number of Subsystems

[+] Author and Article Information
Sangjin Jung

Senior Research Engineer
Production Engineering Research Institute,
LG Electronics, Inc.,
19-1, Cheongho-ri, Jinwi-myeon, Pyeongtaek-si,
Gyeonggi-do 451-713, Republic of Korea

Gyu-Byung Park

Ph.D. Candidate
Graduate School of Mechanical Engineering,
Hanyang University,
17 Haengdang-dong,
Seongdong-gu, Seoul 133-791, Republic of Korea

Dong-Hoon Choi

Department of Mechanical Engineering,
Hanyang University,
17 Haengdang-dong,
Seongdong-gu, Seoul 133-791, Republic of Korea

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 8, 2012; final manuscript received January 15, 2013; published online March 26, 2013. Assoc. Editor: Olivier de Weck.

J. Mech. Des 135(4), 041005 (Mar 26, 2013) (9 pages) Paper No: MD-12-1099; doi: 10.1115/1.4023554 History: Received February 08, 2012; Revised January 15, 2013

Many practical design problems are multidisciplinary and typically involve the transfer of complex information between analysis modules. In solving such problems, the method for performing multidisciplinary analyses greatly affects the speed of the total design time. Thus, it is very important to group and order a multidisciplinary analysis (MDA) process so as to minimize the total computational time and cost by decomposing a large multidisciplinary problem into several subsystems and then processing them in parallel. This study proposes a decomposition method that exploits parallel computing, including the determination of an optimal number of subsystems by using a multi-objective optimization formulation and a messy genetic algorithm (GA) modified to handle discrete design variables. In the suggested method, an MDA process is decomposed and sequenced for simultaneously minimizing the feedback couplings within each subsystem, the total couplings between subsystems, the variation of computation times among subsystems, and the computation time of each subsystem. The proposed method is applied to the decomposition of an artificial complex system example and a multidisciplinary design problem of a rotorcraft with 17 analysis modules; promising results are presented using this proposed method.

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Fig. 1

MDA process represented by using DSM

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Fig. 2

Analysis time and coupling strength values within the DSM

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Fig. 3

Variables defined for decomposition in DSM

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Fig. 4

Dividing points between subsystems in the DSM

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Fig. 5

Extended chromosome

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Fig. 6

A suggested crossover method

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Fig. 7

A suggested mutation method

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Fig. 8

Flow chart of the proposed decomposition method

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Fig. 9

Initial DSM of example 1

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Fig. 10

Decomposed DSM for case 1

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Fig. 11

The trend of optimal values for the function F according to changing the fixed number of subsystems

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Fig. 12

Data flow of the rotorcraft analysis process

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Fig. 13

Initial DSM of the rotorcraft analysis process

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Fig. 14

Decomposed DSM for case 1

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Fig. 15

Convergence history of the proposed GA in case 1

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Fig. 16

Decomposed DSM for case 8

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Fig. 17

Comparison between the decomposed DSM in Ref. [26] and the DSM decomposed by the proposed method




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