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이규석(Guesuk Lee),손혜정(Hye-jeong Son),김원곤(Wongon Kim),오현석(Hyunseok Oh),윤병동(Byeng D. Youn) 대한기계학회 2016 대한기계학회 춘추학술대회 Vol.2016 No.12
Computational models have reached a high level of resolution and sophistication that they can provide various knowledge for many engineering fields. A scientific discipline concerned with assessing the credibility of computational models of physical system in the presence of uncertainties is called verification and Validation (V&V). As an important element of V&V, model calibration is the process of adjusting the input variables of a model to improve or force the agreement of model predictions with experimental observations. In statistical sense, an input variable of the model has a degree of uncertainty which can be represented by a probability distribution with statistical parameters. Optimization-based model calibration is a straightforward method to estimate those statistical parameters with experimental observations. Accuracy and efficiency of optimization-based model calibration depend on the selection of a calibration metric which quantitatively measure the difference between two statistical distributions of the system response in interest from computational prediction and experimental observation. In this paper, three delegates for calibration metrics are introduced. The comparison analysis is conducted in aspects of accuracy, efficiency, and practical usage.