| Summary: | Experiments used for model validation constitute a new type of experiments, whose primary goal is to determine the ability of a high-fidelity mathematical model in simulating a well-characterized physical process. Considering the importance of the credibility of the validation experiment, in this paper, a methodology for the optimal design of validation experiments based on the area metric factor and the fuzzy expert system is presented to obtain the test scheme with low cost and satisfactory credibility. First, the concept of area metric factor is proposed, which is a dimensionless validation metric. Then, a criterion for qualitative assessment of the predictive model accuracy is developed based on the fuzzy expert system, in which the inconsistency among the different expert groups is considered. Subsequently, an optimization model of the validation experiment design is constructed, in which the sample size of the experiments is designed variable, while the credibility of the validation experiment is a constraint. Meanwhile, a novel method is developed to solve the optimization model, in which the Latin hypercube sampling is used to obtain the location of each validation experiment. Finally, two simulation examples are adopted to validate the proposed methods. The simulation results show that the randomness of the experimental scheme significantly affects the credibility of the evaluation results in the case of small sample sizes. The proposed method for the validation experiment design can reduce the impact of the selection of the testing programs on the credibility of model validation.
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