近日，美国加州大学机械工程悉的Grace X. Gu及其研究小组与土木与环境工程系的Shaofan Li以及北京大学的Yu Ao合作并取得一项新进展。经过不懈努力，他们提出有限翼在各种巡航条件下气动分析中基于自适应机器学习的优化方法。相关研究成果已于2023年12月16日在国际知名学术期刊《理论与应用力学快报》上发表。

该研究团队创建了一种基于数字化机翼形状变形和深度学习算法的自适应优化方法，该方法能够在不同巡航条件下快速制定特定气动性能要求的有限机翼设计。该方法分为三个阶段：第一阶段是利用径向基函数（RBF）插值生成机翼形状；第二阶段是进行计算流体动力学（CFD）模拟，收集输入数据；第三阶段是构建最优机翼构型替代模型的深度神经网络。这种方法可以显著降低数值模拟的计算成本，并且具有优化各种不同任务环境、负载条件和安全要求的飞行器的潜力。

据悉，由于多用途飞行任务的复杂性，传统的机翼气动优化过程既耗时又不精确。已有大量文献考虑了二维无限翼型优化，而三维有限翼优化由于计算成本高，研究较少。

附：英文原文

Title: An adaptive machine learning based optimization methodology in the aerodynamic analysis of a finite wing under various cruise conditions

Author: Zilan Zhang, Yu Ao, Shaofan Li, Grace X. Gu

Issue&Volume: 2023-12-16

Abstract: Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions. Plenty of existing literature has considered twodimensional infinite airfoil optimization, while three-dimensional finite wing optimizations are subject to limited study because of high computational costs. Here we create an adaptive optimization methodology built upon a digitized wing shape deformation and deep learning algorithms, which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions. This methodology unfolds in three stages: radial basis function (RBF) interpolated wing generation, collection of inputs from computational fluid dynamics (CFD) simulations, and deep neural network that constructs the surrogate model for the optimal wing configuration. To this end, the proposed methodology can significantly reduce the computational cost of numerical simulations. It also has potential to optimize various aerial vehicles undergoing different mission environments, loading conditions, and safety requirements.

DOI: 10.1016/j.taml.2023.100489

Source: http://taml.cstam.org.cn/article/doi/10.1016/j.taml.2023.100489?pageType=en