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Zhao Xuan, Zhang Weiwei, Deng Zichen. Aerodynamic modeling method incorporating pressure distribution information. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2616-2626. DOI: 10.6052/0459-1879-22-170
 Citation: Zhao Xuan, Zhang Weiwei, Deng Zichen. Aerodynamic modeling method incorporating pressure distribution information. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2616-2626. DOI: 10.6052/0459-1879-22-170

AERODYNAMIC MODELING METHOD INCORPORATING PRESSURE DISTRIBUTION INFORMATION

• In aerodynamic shape optimization design and aircraft performance analysis, the cost of directly using numerical simulation or wind tunnel experiments to obtain aerodynamic forces is high. Building surrogate model is an important way to improve the efficiency of shape optimization and performance analysis. However, in the process of building the model, researchers only focus on the aerodynamic force and moment information after integration. In this paper, the accuracy and generalization of modeling are improved by making full use of the pressure distribution information generated in the sampling process, thereby reducing the cost of sample acquisition. In this paper, an aerodynamic modeling method integrating pressure distribution information under the framework of small sample is proposed. Firstly, the pressure distribution information and aerodynamic coefficients of airfoil surface under different flow parameters are obtained by numerical simulation or wind tunnel experiments. Secondly, the pressure distribution information is extracted by proper orthogonal decomposition technology to obtain the POD coefficients corresponding to the distribution information under different input parameters. Then, the pressure distribution information is modeled by Kriging algorithm combined with input parameters. The pressure distribution information is integrated to obtain the prediction model of low precision aerodynamic coefficients. Finally, the low precision aerodynamic coefficients are combined with the input parameters to construct a high precision aerodynamic prediction model by Kriging algorithm. The method is verified by the same-state variable airfoil example and the CAS350 airfoil variable-state example. Compared with the traditional Kriging model, this method can effectively improve the prediction accuracy of aerodynamic force and the robustness of the model, and reduce the data amount of learning samples.

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