Paper Title :A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics
Author :Tanishk Nandal Vaibhav Fulara, Raj Kumar Singh
Article Citation :Tanishk Nandal Vaibhav Fulara ,Raj Kumar Singh ,
(2023 ) " A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics " ,
International Journal of Mechanical and Production Engineering (IJMPE) ,
pp. 14-23,
Volume-11,Issue-7
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.
Keywords - Autoencoders, Computational Fluid Dynamics, Deep learning, Generative Adversarial Networks
Type : Research paper
Published : Volume-11,Issue-7
DOIONLINE NO - IJMPE-IRAJ-DOIONLINE-19991
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Copyright: © Institute of Research and Journals
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Published on 2023-11-10 |
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