Fast pressure distribution prediction of airfoils using deep learning

Xinyu Hui, Junqiang Bai, Hui Wang, Yang Zhang

Research output: Contribution to journalArticlepeer-review

148 Scopus citations

Abstract

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.

Original languageEnglish
Article number105949
JournalAerospace Science and Technology
Volume105
DOIs
StatePublished - Oct 2020

Keywords

  • Aerodynamic design
  • Convolutional neural network
  • Machine learning
  • Pressure distribution prediction

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