A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics

TL;DR

This paper introduces a combined autoencoder and GAN framework to efficiently generate CFD pressure-distribution results for aerofoils, reducing prediction time and costs in aerodynamic analysis.

Contribution

It presents a novel synergistic approach that encodes aerofoil geometries and generates CFD results using a conditional GAN, advancing aerodynamic prediction methods.

Findings

Accurately predicts pressure distributions from aerofoil geometries.

Reduces computational time and costs in CFD simulations.

Demonstrates effectiveness on a diverse dataset from JavaFoil.

Abstract

In the realm of computational fluid dynamics (CFD), accurate prediction of aerodynamic behaviour plays a pivotal role in aerofoil design and optimization. This study proposes a novel approach that synergistically combines autoencoders and Generative Adversarial Networks (GANs) for the purpose of generating CFD results. Our innovative framework harnesses the intrinsic capabilities of autoencoders to encode aerofoil geometries into a compressed and informative 20-length vector representation. Subsequently, a conditional GAN network adeptly translates this vector into precise pressure-distribution plots, accounting for fixed wind velocity, angle of attack, and turbulence level specifications. The training process utilizes a meticulously curated dataset acquired from JavaFoil software, encompassing a comprehensive range of aerofoil geometries. The proposed approach exhibits profound potential in reducing the time and costs associated with aerodynamic prediction, enabling efficient evaluation of aerofoil performance. The findings contribute to the advancement of computational techniques in fluid dynamics and pave the way for enhanced design and optimization processes in aerodynamics.