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- Title
A modular Bayesian approach to model calibration of low‐fidelity simulation models.
- Authors
Li, Xiaopeng
- Abstract
Low‐fidelity computational models have been widely used for the computation of complex engineered systems to greatly save the computational time while sacrificing the computational precision. Model calibration can be implemented for low‐fidelity simulation models to remedy the errors of simulation results. This article proposes a modular Bayesian updating framework that considers epistemic uncertainty to achieve model calibration of low‐fidelity simulation models. The proposed framework mainly contains two steps: (1) A model calibration framework based on Bayesian theory is proposed that can simultaneously quantify the model form uncertainty, model parameter uncertainty, and the experimental measurement uncertainty, (2) the proposed method updates a low‐fidelity surrogate model via a Metropolis–Hastings (M–H) algorithm by using high‐fidelity experimental data. The proposed method greatly improves the prediction accuracy of the simulation model and enhances the computational efficiency. A mathematical example is used to testify the accuracy of the developed method, while the aerodynamics simulation of the NACA0012 airfoil is leveraged to demonstrate the engineering application. The results show that the posterior prediction mean is in better agreement with the experimental mean than the prior prediction mean, suggesting that the modular Bayesian updating method improves the prediction accuracy of low‐fidelity simulation models.
- Subjects
SIMULATION methods &; models; CALIBRATION; EPISTEMIC uncertainty; BAYESIAN analysis; COMPUTATIONAL fluid dynamics; EPISTEMIC logic
- Publication
Quality & Reliability Engineering International, 2024, Vol 40, Issue 1, p428
- ISSN
0748-8017
- Publication type
Article
- DOI
10.1002/qre.3428