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research-article

Discrepancy Prediction in Dynamical System Models under Untested Input Histories

[+] Author and Article Information
Kyle Neal

Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee, 37235
kyle.d.neal@vanderbilt.edu

Zhen Hu

Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, Michigan, 48128
zhennhu@umich.edu

Sankaran Mahadevan

Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee, 37235
sankaran.mahadevan88@gmail.com

Jon Zumberge

Air Force Research Laboratory, Wright-Patterson AFB, Dayton, Ohio, 45433
jon.zumberge@us.af.mil

1Corresponding author.

ASME doi:10.1115/1.4041238 History: Received May 31, 2018; Revised August 14, 2018

Abstract

This paper presents a probabilistic framework for discrepancy prediction in dynamical system models under untested input time histories, based on information gained from validation experiments. Two surrogate modeling-based methods, namely observation surrogate and bias surrogate, are developed to predict the bias of a dynamical system simulation model under untested input time history. In the first method, a surrogate model is built for the observed experimental output, and the model bias for the untested input is obtained by comparing the output of the observation surrogate with the output of the physics-based model. The second method constructs a surrogate model for the bias in terms of the inputs in the conducted experiments. The bias surrogate model is then used to correct the simulation model prediction at each time step under a predictor-corrector scheme to predict the model bias under untested conditions. A neural network-based surrogate modeling technique is employed to implement the proposed methodology. The bias prediction result is reported in a probabilistic manner, in order to account for the uncertainty of the surrogate model prediction. An air cycle machine case study is used to demonstrate the effectiveness of the proposed bias prediction framework.

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