Sensitivity-Based Parameter Calibration and Model Validation Under Model Error

Author:

Qiu Na1,Park Chanyoung2,Gao Yunkai3,Fang Jianguang4,Sun Guangyong5,Kim Nam H.6

Affiliation:

1. Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611 e-mail:

2. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611

3. School of Automotive Studies, Tongji University, Shanghai 201804, China

4. School of Civil and Environmental Engineering, University of Technology Sydney, Sydney 2007, NSW, Australia e-mail:

5. School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney 2006, NSW, Australia e-mail:

6. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611 e-mail:

Abstract

In calibrating model parameters, it is important to include the model discrepancy term in order to capture missing physics in simulation, which can result from numerical, measurement, and modeling errors. Ignoring the discrepancy may lead to biased calibration parameters and predictions, even with an increasing number of observations. In this paper, a simple yet efficient calibration method is proposed based on sensitivity information when the simulation model has a model error and/or numerical error but only a small number of observations are available. The sensitivity-based calibration method captures the trend of observation data by matching the slope of simulation predictions and observations at different designs and then utilizing a constant value to compensate for the model discrepancy. The sensitivity-based calibration is compared with the conventional least squares calibration method and Bayesian calibration method in terms of parameter estimation and model prediction accuracies. A cantilever beam example, as well as a honeycomb tube crush example, is used to illustrate the calibration process of these three methods. It turned out that the sensitivity-based method has a similar performance with the Bayesian calibration method and performs much better than the conventional method in parameter estimation and prediction accuracy.

Funder

National Nuclear Security Administration

Publisher

ASME International

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials

Reference22 articles.

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