Identifying Parameter Uncertainties in Model Calibration of Complex Systems
Date Issued
2016
Date
2016
Author(s)
Lin, Yueh-I
Abstract
Effective physical models play important roles in efficient product development cycle. This research focuses on parameter uncertainty to improve precision between model predictions and measured system performances. The state-of-the-art methods use model calibration with Bayesian Inference to identify parameter uncertainties; however potential risks might exist in complex system analysis, namely (1) analyzing multiple parameters, resulting in high computational costs, (2) the predicted confidence levels are low, and (3) unable to infer each individual uncertainty in complex systems. This research adopts main effect analysis from Taguchi''s framework of design of experiments to select important parameters from a complex system. The uncertainty analysis is then narrowed down to those on important parameters. Bayesian updating loop is then reinforced and joint inference of multiple testing functions are used to improve the performance of model calibration. The method is demonstrated in two engineering cases: one is a steady-state test of a simple-supported beam, and the identifying error turns out to be 1.5%; while the other vehicle dynamic test under CarSim® has 17% of identifying error.
Subjects
complex system analysis
parameter uncertainty
parameter estimation
model calibration
Bayesian inference
main effect analysis
parameter selection
Type
thesis
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