Fast pressure distribution prediction of airfoils using deep learning

Xinyu Hui, Junqiang Bai, Hui Wang, Yang Zhang

科研成果: 期刊稿件文章同行评审

148 引用 (Scopus)

摘要

In the aerodynamic design, optimization of the pressure distribution of airfoils is crucial for the aerodynamic components. Conventionally, the pressure distribution is solved by computational fluid dynamics, which is a time-consuming task. Surrogate modeling can leverage such expense to some extent, but it needs careful shape parameterization schemes for airfoils. As an alternative, deep learning approximates inputs-outputs mapping without solving the efficiency-expensive physical equations and avoids the limitations of particular parameterization methods. Therefore, this paper presents a data-driven approach for predicting the pressure distribution over airfoils based on Convolutional Neural Network (CNN). Given the airfoil geometry, a supervised learning problem is presented for predicting aerodynamic performance. Furthermore, we utilize a universal and flexible parametrization method called Signed Distance Function to improve the performances of CNN. Given the unseen airfoils from the validation dataset to the trained model, our model achieves predicting the pressure coefficient in seconds, with a less than 2% mean square error.

源语言英语
文章编号105949
期刊Aerospace Science and Technology
105
DOI
出版状态已出版 - 10月 2020

指纹

探究 'Fast pressure distribution prediction of airfoils using deep learning' 的科研主题。它们共同构成独一无二的指纹。

引用此