Prediction of pressure coefficient distributions for basic aerodynamic configurations via point cloud characterization

Data-driven approaches have shown great advantage in rapidly and accurately predicting pressure coefficient distributions, which is of crucial importance to efficient aircraft design. Nevertheless, most data-driven approaches still encounter limitations in characterizing diverse aerodynamic configurations and adapting to varying grid densities, which have hindered their engineering applicability. In response to these challenges, this work adopts point clouds, a specific type of geometric data structure that is inherently suitable for uniformly characterizing diverse 2D/3D geometric shapes as the input for deep learning-based prediction of pressure coefficient distribution. By augmenting the dimensions of point cloud coordinates for local feature enhancement and utilizing the symmetric function “max pooling” to extract global features, the proposed aerodynamic model establishes the mapping between point cloud coordinates and pressure coefficients. Basic aerodynamic configurations like airfoils and wings are employed as test cases, the results demonstrate that the proposed model achieves both high accuracy and robust generalizability across variable geometries. For class-shape transformation-perturbed airfoils, the prediction error can be reduced to one-third of that of the conventional parameterization-based model. For airfoils selected in the University of Illinois Urbana-Champaign airfoil dataset, among which airfoil profiles are widely distributed, the average error of the proposed approach remains approximately 1.5%, whereas the parameterization-based model may fail. For wings, the prediction error still stays below 2.5%. Finally, the model exhibits strong robustness and generalizability across different point cloud densities. In conclusion, this work makes a breakthrough in predicting pressure coefficient distribution for variable geometric configurations, establishing the foundational framework for designing a large model capable of predicting distributed aerodynamic loads in aerospace applications.