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Structural and Multidisciplinary Optimization

, Volume 51, Issue 3, pp 573–583 | Cite as

A framework of model validation and virtual product qualification with limited experimental data based on statistical inference

  • Byung C. Jung
  • Jungho Park
  • Hyunseok Oh
  • Jisun Kim
  • Byeng D. Youn
RESEARCH PAPER

Abstract

Virtual testing is a recent engineering development trend to design, evaluate, and test new engineered products. This research proposes a framework of virtual testing based on statistical inference for new product development comprising of three successive steps: (i) statistical model calibration, (ii) hypothesis test for validity check and (iii) virtual qualification. Statistical model calibration first improves the predictive capability of a computational model in a calibration domain. Next, the hypothesis test is performed with limited observed data to see if a calibrated model is sufficiently predictive for virtual testing of a new product design. An area metric and the u-pooling method are employed for the hypothesis test to measure the degree of mismatch between predicted and observed results while considering statistical uncertainty in the area metric due to the lack of experimental data. Once the calibrated model becomes valid, the virtual qualification process can be executed with a qualified model for new product developments. The qualification process builds a design decision matrix to aid in rational decision-making for product design alternatives. The effectiveness of the proposed framework is demonstrated through the case study of a tire tread block.

Keywords

Model validation Virtual qualification Model calibration Validity check 

Nomenclature

D

critical value of area metric

e

observation error

L

likelihood function

l

number of the known variables

p

number of the unknown variables

q

number of the controllable variables

Um

area metric

y

observed model

α

significance level for a hypothesis test

β

known model variable vector

δ

prediction error

ζ

operation variable vector

Θ

hyper-parameter vector

θ

unknown model variable vector

μ

friction coefficient

υ

pressure exponential parameter

φ

contact pressure

ψ

predicted model

Notes

Acknowledgments

This work was partially supported by a grant from the Energy Technology Development Program (2010101010027B) and International Collaborative R&D Program (0420-2011-0161) of Korea Institute of Energy Technology Evaluation and Planning (KETEP), funded by the Korean government’s Ministry of Knowledge Economy, the National Research Foundation of Korea (NRF) grant (No. 2011–0022051) funded by the Korea government, the Basic Research Project of Korea Institute of Machinery and Materials (Project Code : SC1000) supported by a grant from Korea Research Council for Industrial Science & Technology, and the Institute of Advanced Machinery and Design at Seoul National University (SNU-IAMD).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Byung C. Jung
    • 1
  • Jungho Park
    • 2
  • Hyunseok Oh
    • 2
  • Jisun Kim
    • 2
  • Byeng D. Youn
    • 2
  1. 1.Department of System Dynamics, Korea Institute of Machinery and MaterialsDaejeonSouth Korea
  2. 2.Department of Mechanical and Aerospace Engineering, Seoul National UniversitySeoulSouth Korea

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