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Journal of Mechanical Design | Volume 127 | Issue 5 | TECHNICAL PAPERS
TECHNICAL PAPERS

Metamodeling Development for Vehicle Frontal Impact Simulation

[+] Author and Article Information
R. J. Yang

 Ford Motor Company, 2101 Village Road, MD2115, SRL Dearborn, MI 48124ryang@ford.com

N. Wang, C. H. Tho, J. P. Bobineau

 Ford Motor Company, 2101 Village Road, MD2115, SRL Dearborn, MI 48124

B. P. Wang

Department of Mechanical and Aerospace Engineering,  University of Texas at Arlington, Box 19023 Arlington, TX 76019bpwang@mae.uta.edu

J. Mech. Des 127(5), 1014-1020 (Jan 14, 2005) (7 pages) doi:10.1115/1.1906264 History: Received March 16, 2004; Revised January 14, 2005
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Response surface methods or metamodels are commonly used to approximate large computationally expensive engineering systems. Five response surface methods are studied: Stepwise Regression, Moving Least Square, Kriging, Multiquadric, and Adaptive and Interactive Modeling System. A real-world frontal impact design problem is used as an example, which is a complex, highly nonlinear, transient, dynamic, large deformation finite element model. To study the accuracy of the metamodel, the optimal Latin Hypercube Sampling method is used to distribute the sampling points uniformly over the entire design space. The Root Mean Square Error (RMSE) is used as the error indicator. Convergence rate, widely used in the arena of the finite element method for evaluating new element’s performance, was exploited in this vehicle impact example.
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Copyright © 2005 by American Society of Mechanical Engineers
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References

Figures

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+AIMS architecture
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AIMS architecture
Figure 1

AIMS architecture

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+AIMS modeling process
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AIMS modeling process
Figure 2

AIMS modeling process

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+Frontal crash finite element model
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Frontal crash finite element model
Figure 3

Frontal crash finite element model

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+Selected design variables
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Selected design variables
Figure 4

Selected design variables

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+Frontal crash CAE procedure
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Frontal crash CAE procedure
Figure 5

Frontal crash CAE procedure

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+RMSE versus normalized sample size for intrusion
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RMSE versus normalized sample size for intrusion
Figure 6

RMSE versus normalized sample size for intrusion

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+RMSE versus normalized sample size for HIC
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RMSE versus normalized sample size for HIC
Figure 7

RMSE versus normalized sample size for HIC

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+The LHS configurations (6N)
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The LHS configurations (6N)
Figure 8

The LHS configurations (6N)

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+Surface and contour plots for normalized intrusion (36N)
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Surface and contour plots for normalized intrusion (36N)
Figure 9

Surface and contour plots for normalized intrusion (36N)

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+Surface and contour plots for normalized HIC (36N)
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Surface and contour plots for normalized HIC (36N)
Figure 10

Surface and contour plots for normalized HIC (36N)

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