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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February 2019
Research-Article
Discrepancy Prediction in Dynamical System Models Under Untested Input Histories Available to Purchase
Kyle Neal,
Kyle Neal
Department of Civil and
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
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Zhen Hu,
Zhen Hu
Department of Industrial and Manufacturing
Systems Engineering,
University of Michigan-Dearborn,
2340 Heinz Prechter Engineering
Complex (HPEC)
Dearborn, MI 48128
e-mail: [email protected]
Systems Engineering,
University of Michigan-Dearborn,
2340 Heinz Prechter Engineering
Complex (HPEC)
Dearborn, MI 48128
e-mail: [email protected]
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Sankaran Mahadevan,
Sankaran Mahadevan
Department of Civil and
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Search for other works by this author on:
Jon Zumberge
Jon Zumberge
Energy/Power/Thermal Division,
Air Force Research Laboratory,
Wright-Patterson AFB,
Dayton, OH 45433
e-mail: [email protected]
Air Force Research Laboratory,
Wright-Patterson AFB,
Dayton, OH 45433
e-mail: [email protected]
Search for other works by this author on:
Kyle Neal
Department of Civil and
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Zhen Hu
Department of Industrial and Manufacturing
Systems Engineering,
University of Michigan-Dearborn,
2340 Heinz Prechter Engineering
Complex (HPEC)
Dearborn, MI 48128
e-mail: [email protected]
Systems Engineering,
University of Michigan-Dearborn,
2340 Heinz Prechter Engineering
Complex (HPEC)
Dearborn, MI 48128
e-mail: [email protected]
Sankaran Mahadevan
Department of Civil and
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Environmental Engineering,
Vanderbilt University,
2201 West End Avenue,
Box 1831, Station B,
Nashville, TN 37235
e-mail: [email protected]
Jon Zumberge
Energy/Power/Thermal Division,
Air Force Research Laboratory,
Wright-Patterson AFB,
Dayton, OH 45433
e-mail: [email protected]
Air Force Research Laboratory,
Wright-Patterson AFB,
Dayton, OH 45433
e-mail: [email protected]
1Corresponding author.
Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received May 31, 2018; final manuscript received August 14, 2018; published online January 7, 2019. Assoc. Editor: Kyung Choi.This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.
J. Comput. Nonlinear Dynam. Feb 2019, 14(2): 021009 (13 pages)
Published Online: January 7, 2019
Article history
Received:
May 31, 2018
Revised:
August 14, 2018
Citation
Neal, K., Hu, Z., Mahadevan, S., and Zumberge, J. (January 7, 2019). "Discrepancy Prediction in Dynamical System Models Under Untested Input Histories." ASME. J. Comput. Nonlinear Dynam. February 2019; 14(2): 021009. https://doi.org/10.1115/1.4041238
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