Two-stage nested optimization-based uncertainty propagation method for model calibration

Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability. However, it is difficult to implement the procedure because of the aleatory uncertainty. In this paper, a new method of model calibration based on uncertainty propagation is investigated. The calibration process is described as an optimization problem. A two-stage nested uncertainty propagation method is proposed to resolve this problem. Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty. Optimization method is applied for the outer loop to propagate the epistemic uncertainty. The optimization objective function is the consistency between the result of the inner loop and the experimental data. Thus, different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function. Finally, the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.