  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.
