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.

模型验证方法已广泛用于工程设计中，以评估具有不确定输入的仿真模型的准确性和可靠性。现有的针对不确定性和认知不确定性的大多数验证方法都是基于贝叶斯定理，该贝叶斯定理需要大量数据来更新模型参数的后验分布。但是，当单个仿真很耗时时，用于仿真模型验证所需的仿真成本可能无法承受。为了克服这一困难，提出了一种在不确定性和认知不确定性下基于参数校准的新模型验证框架。在所提出的方法中，构造了随机克里金模型来预测在不同不确定性输入参数下候选仿真模型的有效性。然后，定义了一个优化问题，以校准认知不确定性参数，以最大程度地减少仿真模型和实验模型之间的差异。KS测试最终决定接受还是拒绝校准的仿真模型。通过悬臂梁示例和涡轮叶片验证问题来说明所提出方法的性能。结果表明，所提出的框架可以识别出最合适的参数来校准仿真模型，并为候选模型的有效性提供正确的判断，这对于实际工程设计中的仿真模型验证是有用的。KS测试最终决定接受还是拒绝校准的仿真模型。通过悬臂梁示例和涡轮叶片验证问题来说明所提出方法的性能。结果表明，所提出的框架能够识别出最合适的参数来校准仿真模型，并为候选模型的有效性提供正确的判断，这对于实际工程设计中的仿真模型验证是有用的。KS测试最终决定接受还是拒绝校准的仿真模型。通过悬臂梁示例和涡轮叶片验证问题来说明所提出方法的性能。结果表明，所提出的框架能够识别出最合适的参数来校准仿真模型，并为候选模型的有效性提供正确的判断，这对于实际工程设计中的仿真模型验证是有用的。