A model validation framework based on parameter calibration under aleatory and epistemic uncertainty

Model validation methods have been widely used in engineering design to evaluate the accuracy and reliability of simulation models with uncertain inputs. Most of the existing validation methods for aleatory and epistemic uncertainty are based on the Bayesian theorem, which needs a vast number of data to update the posterior distribution of the model parameter. However, when a single simulation is time-consuming, the required simulation cost for the validation of a simulation model may be unaffordable. To overcome this difficulty, a new model validation framework based on parameter calibration under aleatory and epistemic uncertainty is proposed. In the proposed method, a stochastic kriging model is constructed to predict the validity of the candidate simulation model under different uncertainty input parameters. Then, an optimization problem is defined to calibrate the epistemic uncertainty parameters to minimize the discrepancy between the simulation model and the experimental model. K-S test finally decides whether to accept or reject the calibrated simulation model. The performance of the proposed approach is illustrated through a cantilever beam example and a turbine blade validation problem. Results show that the proposed framework can identify the most appropriate parameters to calibrate the simulation model and provide a correct judgment about the validity of the candidate model, which is useful for the validation of simulation models in practical engineering design.

Automated summary

A new model validation framework based on parameter calibration under uncertainty is proposed, using a stochastic kriging model to predict the validity of candidate simulation models and using K-S test to decide the acceptance or rejection of the calibrated simulation model. The framework is illustrated through examples and shows its effectiveness in identifying appropriate parameters for calibration and providing correct judgments about the validity of candidate models in practical engineering design.