Statistical validation of simulation models

Ramesh Rebba, Shuping Huang, Yongming Liu, Sankaran Mahadevan

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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 languageEnglish (US)
Pages (from-to)164-181
Number of pages18
JournalInternational Journal of Materials and Product Technology
Issue number1-3
StatePublished - 2006
Externally publishedYes


  • 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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