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The inverse design method of aerodynamic configuration is hard to give a reasonable pressure distribution, and strongly rely on experience of designers. The method has been difficult to adapt to the needs of modern aircraft design. Aiming at the shortcoming of the method, an efficient and robust aerodynamic configuration inverse design method is developed, employing knowledge of machine learning methods and optimization methods. The present method establishes the mapping between the high dimensional data obtained from aerodynamic shape and pressure distribution and the variables in the latent space. Then, the global optimization is carried out in the latent space by using the genetic algorithm. The optimum pressure distribution and the corresponding shape can be obtained. Through the the GTM model with high precision, there is not necessary for the flow solver in the whole iterative process, thus the design efficiency can be enhanced. Besides, by taking the advantage of optimization method, the target pressure distribution can be given in a very flexible way, and does not need to be physically meaningful. This feature can reduce reliance on the design experience. Airfoils in low speed and transonic flow and a three-dimensional laminar nacelle design cases are carried out. It is shown that the method robustly and efficiently converges to the target pressure, and has good engineering application potential.

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The continuous development of computer technology and computational fluid dynamics has led to great progress in aerodynamic shape design methods. Aerodynamic design methods have been developed from cut-and-try to reverse design and further to optimal design. Aerodynamic analysis methods have been evolved from wind tunnel test to low fidelity computational fluid dynamics (CFD) and then to high fidelity CFD simulation. With the higher and higher requirements regarding aircraft performance, the design conditions, optimization models and design constraints are becoming more and more complex. Consequently, the continuously increasing number of design variables and design goals lead to new challenges for aerodynamic design methods. Based on years of research experiences by the author and author' s team and the domestic and abroad related research findings, aerodynamic shape design methods are systematically reviewed in the present paper. New problems and challenges in aerodynamic design are summarized. Moreover, future research trends of aerodynamic shape design are prospected. [ABSTRACT FROM AUTHOR]