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Improving High-Dimensional Physics Models Through Bayesian Calibration With Uncertain Data

[+] Author Affiliations
Natarajan Chennimalai Kumar, Arun K. Subramaniyan, Liping Wang

GE Global Research Center, Niskayuna, NY

Paper No. GT2012-69058, pp. 407-416; 10 pages
doi:10.1115/GT2012-69058
From:
  • ASME Turbo Expo 2012: Turbine Technical Conference and Exposition
  • Volume 7: Structures and Dynamics, Parts A and B
  • Copenhagen, Denmark, June 11–15, 2012
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4473-1
  • Copyright © 2012 by ASME

abstract

We address the problem of calibrating model parameters in computational models to match uncertain and limited experimental data using a Bayesian framework. We employ a modified version of the Bayesian calibration framework proposed by Kennedy and O’Hagan [15], to perform calibration of large dimensional industrial problems. Results for two nonlinear industrial problems with 15 and 100 calibration parameters are presented. The unique advantages of the Bayesian framework are presented along with a discussion on the challenges in calibrating large number of parameters with uncertain and limited data.

Copyright © 2012 by ASME
Topics: Physics , Calibration

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