Cost-Aware Adaptive Design of Experiment with Nonstationary Surrogate Model for Wind Tunnel Testing

This paper proposes a novel adaptive design of experiment (ADoE) framework with cost-aware sampling strategy and nonstationary surrogate model for efficient wind tunnel testing. The ADoE framework, which is based on the Gaussian process, can effectively reduce the required number of an experiment while maintaining its accuracy. The proposed cost-aware sampling strategy augments the framework by selecting cost-efficient experiment points and the nonstationary surrogate model effectively reflects the nonlinearity of the system on the response surface model. The efficacy of the proposed framework has been validated through a virtual experiment using an actual high angle-of-attack wind tunnel test dataset, which is highly nonlinear.

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