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Research Papers: Design Theory and Methodology

A Clustering-Based Surrogate Model Updating Approach to Simulation-Based Engineering Design

[+] Author and Article Information
Tiefu Shao

Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003

Sundar Krishnamurty

Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003skrishna@ecs.umass.edu

J. Mech. Des 130(4), 041101 (Feb 28, 2008) (13 pages) doi:10.1115/1.2838329 History: Received November 16, 2006; Revised September 10, 2007; Published February 28, 2008

This paper addresses the critical issue of effectiveness and efficiency in simulation-based optimization using surrogate models as predictive models in engineering design. Specifically, it presents a novel clustering-based multilocation search (CMLS) procedure to iteratively improve the fidelity and efficacy of Kriging models in the context of design decisions. The application of this approach will overcome the potential drawback in surrogate-model-based design optimization, namely, the use of surrogate models may result in suboptimal solutions due to the possible smoothing out of the global optimal point if the sampling scheme fails to capture the critical points of interest with enough fidelity or clarity. The paper details how the problem of smoothing out the best (SOB) can remain unsolved in multimodal systems, even if a sequential model updating strategy has been employed, and lead to erroneous outcomes. Alternatively, to overcome the problem of SOB defect, this paper presents the CMLS method that uses a novel clustering-based methodical procedure to screen out distinct potential optimal points for subsequent model validation and updating from a design decision perspective. It is embedded within a genetic algorithm setup to capture the buried, transient, yet inherent data pattern in the design evolution based on the principles of data mining, which are then used to improve the overall performance and effectiveness of surrogate-model-based design optimization. Four illustrative case studies, including a 21bar truss problem, are detailed to demonstrate the application of the CMLS methodology and the results are discussed.

Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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SOB in a Kriging-model-based global optimization of the six-basin function

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A system with four local maximum points

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Evolution in the simulation of water submerging process

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Clustering process using SLC method

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Using CMLS method in SMBDO

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Initial 21 sample points

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Improvement with using CMLS method

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Efficacy of CMLS method: Case Study 1

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Initial ten sample points

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Improvement with using CMLS method

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Efficacy of CMLS method: Case Study 2

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Beam design problem

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Actual deflection at right beam end

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Beam design problem: beam end deflection

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Efficacy of CMLS method: Case Study 3

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21bar truss problem

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Optimization results

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SOB in a Kriging-model-based global optimization of the mystery function

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