CHEN Z Q,LI X,ZHAI C Y,et al．Rapid prediction of surface pressure distribution on variable-sweep wing aircraft using improved PointNet++ algorithm［J］.Journal of Henan Polytechnic University( Natural Science) ，doi:10.16186/j.cnki.1673-9787.2025090006.

doi:10.16186/j.cnki.1673-9787.2025090006

Received：2025-09-05

Revised：2026-02-10

Online：2026-04-16

Rapid prediction of surface pressure distribution on variable-sweep wing aircraft using improved PointNet++ algorithm(Online)

Chen Zhiqiang, Li Xiang, Zhai Chaoyang, Wang Xiaolu, Zhang Xinzhe

( School of Aeronautics and Astronautics, Zhengzhou University of Aeronautics, Zhengzhou 450000, Henan, China)

Abstract: Objective The new - generation variable - sweep wing aircraft, featuring the unique capability of dynamically adjusting the sweep angle, can flexibly adapt to a wide range of flight mission requirements. Its aerodynamic performance was optimized by altering the wing sweep angle under different flight conditions. However, the dynamic changes in the sweep angle induced complex coupling characteristics between the aerodynamic shape and the flow field, which significantly increased the complexity and uncertainty of the design cycle. As the core aspect of aircraft design, aerodynamic design relied on the rapid acquisition of pressure distribution data across the entire flight envelope to support the rapid iteration and optimization of design schemes. Therefore, this study aimed to develop an efficient and accurate method for the fast prediction of surface pressure distribution on variable - sweep wing aircraft, so as to shorten the design cycle and enhance design efficiency. Methods In this study, a self - built aerodynamic database covering multiple sweep angle states was constructed based on Fluent's dynamic mesh technology. This database contained a wealth of surface pressure distribution data. Furthermore, by integrating the advanced PointNet++ model, an intelligent mapping model from dynamic shapes to surface pressure field distributions was established. This model was trained to learn the data characteristics within the aerodynamic database, enabling second - level predictions of surface pressure distribution on variable - sweep wing aircraft. Meanwhile, to improve prediction accuracy, random sampling and grouping techniques were applied to the surface pressure data computed by CFD, which effectively reduced data noise and enhanced the model's generalization ability. Results Extensive testing and validation were conducted, and it was demonstrated that the intelligent mapping model developed in this study performed excellently in predicting the surface pressure distribution of variable - sweep wing aircraft. Compared with the concentrated stress data computed by CFD, the prediction error of the lift coefficient was controlled within 8%, indicating good accuracy.ConclusionThe intelligent mapping method proposed in this study, which was based on Fluent's dynamic mesh technology and the PointNet++ model, provided an efficient and accurate solution for the aerodynamic design of variable - sweep wing aircraft and possessed certain application value.

Key words: surface pressure prediction; data-driven; neural network; rapid prediction; morphing aircraft