Calibrating Transient Models With Multiple Responses Using Bayesian Inverse Techniques

Abstract

Several engineering applications of high interest to turbomachinery involve transient models with multiple outputs. Thus, the ability to calibrate transient models with multiple correlated outputs is critical for enabling predictive models for design and analysis of turbomachinery. When the number of calibration parameters becomes large along with limited knowledge about those parameters (large uncertainty), traditional deterministic methods like least squares don’t yield reasonable parameter estimates. We employ the Bayesian calibration framework, proposed by Kennedy and O’Hagan [1], to perform calibration of industrial scale transient problems. The focus of this article is on Bayesian calibration of models with multiple transient outputs. The methodology is demonstrated with two problems with transient outputs. The advantages of using a Bayesian framework are highlighted. Specific challenges related to Bayesian calibration of transient responses are discussed along with potential solutions.

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