An intelligent prediction model and transfer learning framework for aerodynamic performance of 3D aircraft

Zhang Peng; Xu Dan; Sun Guopeng; Wang Yueqing; Liu Yushuang; Chen Yi; Chen Jianqiang

doi:10.7638/kqdlxxb-2025.0049

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An intelligent prediction model and transfer learning framework for aerodynamic performance of 3D aircraft

更新时间：2026-07-11

- An intelligent prediction model and transfer learning framework for aerodynamic performance of 3D aircraft
- Vol. 44, Issue 6, Pages: 14-24(2026)
- 作者机构：
  
  1. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
  2. 军事科学院战略评估咨询中心,北京,100091
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2025.0049
  CLC： V11;TP183
- Published：2026
- 稿件说明：
移动端阅览
张鹏, 徐丹, 孙国鹏, 等. 面向三维飞行器的气动性能智能预测模型及迁移学习框架研究[J]. 空气动力学学报, 2026, 44(6): 14−24.

Zhang Peng, Xu Dan, Sun Guopeng, et al. An intelligent prediction model and transfer learning framework for aerodynamic performance of 3D aircraft[J]. 2026, 44(6): 14-24.
张鹏, 徐丹, 孙国鹏, 等. 面向三维飞行器的气动性能智能预测模型及迁移学习框架研究[J]. 空气动力学学报, 2026, 44(6): 14−24. DOI： 10.7638/kqdlxxb-2025.0049

Zhang Peng, Xu Dan, Sun Guopeng, et al. An intelligent prediction model and transfer learning framework for aerodynamic performance of 3D aircraft[J]. 2026, 44(6): 14-24. DOI： 10.7638/kqdlxxb-2025.0049.

摘要

人工智能技术的飞速发展为飞行器气动性能计算模式的创新带来了机遇。本文面向三维飞行器发展了一种基于点云表征和Transformer架构的气动性能智能预测模型，对气动参数进行端到端预测，加速了气动问题求解过程。为解决智能模型的大样本需求与气动领域小样本现实之间的矛盾，本文基于模型参数复用方法和域对抗学习方法发展了面向气动性能预测任务的迁移学习框架，有效减小了模型对训练样本的需求。数据集构建上，基于自有三维飞行器仿真性能数据，构建了共包含上万样本的外形泛化、工况泛化及外形、工况双泛化测试数据集。实验结果表明，气动性能预测模型对单个三维飞行器的气动性能预测时间仅为3.096 s，计算效率较传统CFD方法提升2个数量级，预测结果的非标准平均相对误差仅为5.6%，此外，该模型在跨外形、跨工况等测试数据集上均表现出良好的泛化性；提出的迁移学习框架在保持模型同等精度水平（10%）的同时对训练样本量的需求降低了70%，可有效应对空气动力学中三维仿真数据稀缺的挑战，有望为飞行器设计优化、快速研制提供关键技术支撑。

Abstract

The rapid advancement of artificial intelligence technology has brought opportunities for innovation in the computational modeling of aircraft aerodynamic performance. This paper develops an intelligent prediction model for aerodynamic performance based on point cloud representation and Transformer architecture for three-dimensional aircraft

achieving end-to-end prediction of aerodynamic parameters and accelerating the process of solving aerodynamic problems. Furthermore

to address the contradiction between the large sample requirements of intelligent models and the small sample characteristics of the aerodynamic field

a transfer learning framework for aerodynamic performance prediction tasks is developed based on model parameter reuse methods and domain adversarial learning

effectively reducing the training sample requirements of the model. In terms of dataset construction

this paper builds a generalized test dataset containing over ten thousand samples

including shape generalization

condition generalization

and dual generalization

based on proprietary simulation performance data of three-dimensional aircraft. The results show that the aerodynamic performance prediction model requires only 3.096 s for predicting the aerodynamic performance of a single three-dimensional aircraft

achieving a computational efficiency improvement of two orders of magnitude compared to computational fluid dynamics (CFD) methods. The non-standard mean relative error of the prediction results is only 5.6%

and the model demonstrates excellent generalization across shape and condition test datasets. Under the transfer learning framework proposed in this paper

the aerodynamic performance prediction model can maintain the same accuracy level (10%) while reducing the training sample requirement by 70%

effectively addressing the challenge of scarce three-dimensional simulation data in aerodynamics. This approach is expected to provide key technical support for aircraft design optimization and rapid development.

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