Statistical validation of simulation models

Ramesh Rebba; Shuping Huang; Yongming Liu; Sankaran Mahadevan

doi:10.1504/ijmpt.2006.008280

Statistical validation of simulation models

Ramesh Rebba, Shuping Huang, Yongming Liu, Sankaran Mahadevan

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

This paper investigates various statistical methodologies for validating simulation models in automotive design. Validation metrics to compare model prediction with experimental observation, when there is uncertainty in both, are developed. Two types of metrics based on Bayesian analysis and principal components analysis are proposed. The validation results are also compared with those obtained from classical hypothesis testing. A fatigue life prediction model for composite materials and a residual stress prediction model for a spot-welded joint are validated, using the proposed methodology.

Original language	English (US)
Pages (from-to)	164-181
Number of pages	18
Journal	International Journal of Materials and Product Technology
Volume	25
Issue number	1-3
DOIs	https://doi.org/10.1504/ijmpt.2006.008280
State	Published - 2006
Externally published	Yes

Keywords

Bayesian statistics
Fatigue life
Hypothesis testing
PCA
Validation

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Mechanics of Materials
Mechanical Engineering
Industrial and Manufacturing Engineering

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10.1504/ijmpt.2006.008280

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abstract = "This paper investigates various statistical methodologies for validating simulation models in automotive design. Validation metrics to compare model prediction with experimental observation, when there is uncertainty in both, are developed. Two types of metrics based on Bayesian analysis and principal components analysis are proposed. The validation results are also compared with those obtained from classical hypothesis testing. A fatigue life prediction model for composite materials and a residual stress prediction model for a spot-welded joint are validated, using the proposed methodology.",

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author = "Ramesh Rebba and Shuping Huang and Yongming Liu and Sankaran Mahadevan",

year = "2006",

doi = "10.1504/ijmpt.2006.008280",

language = "English (US)",

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AU - Rebba, Ramesh

AU - Huang, Shuping

AU - Liu, Yongming

AU - Mahadevan, Sankaran

PY - 2006

Y1 - 2006

N2 - This paper investigates various statistical methodologies for validating simulation models in automotive design. Validation metrics to compare model prediction with experimental observation, when there is uncertainty in both, are developed. Two types of metrics based on Bayesian analysis and principal components analysis are proposed. The validation results are also compared with those obtained from classical hypothesis testing. A fatigue life prediction model for composite materials and a residual stress prediction model for a spot-welded joint are validated, using the proposed methodology.

AB - This paper investigates various statistical methodologies for validating simulation models in automotive design. Validation metrics to compare model prediction with experimental observation, when there is uncertainty in both, are developed. Two types of metrics based on Bayesian analysis and principal components analysis are proposed. The validation results are also compared with those obtained from classical hypothesis testing. A fatigue life prediction model for composite materials and a residual stress prediction model for a spot-welded joint are validated, using the proposed methodology.

KW - Bayesian statistics

KW - Fatigue life

KW - Hypothesis testing

KW - PCA

KW - Validation

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EP - 181

JO - International Journal of Materials and Product Technology

JF - International Journal of Materials and Product Technology

IS - 1-3

ER -

Statistical validation of simulation models

Abstract

Keywords

ASJC Scopus subject areas

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