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

2 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

physics.flu-dyn2023

Maximizing Savonius Turbine Performance using Kriging Surrogate Model and Grey Wolf-Driven Cylindrical Deflector Optimization

Paras Singh, Vishal Jaiswal, Subhrajit Roy +1

With the growing demand for power and the pressing need to shift towards renewable energy sources, wind power stands as a vital component of the energy transition. To optimize ener…

physics.flu-dyn2023

Optimization of Inverted Double-Element Airfoil in Ground Effect using Improved HHO and Kriging Surrogate Model

Paras Singh, Arun Ravindranath, Aryan Tyagi +2

In the automotive industry, multi-element wings have been used to improve the aerodynamics of race cars. Multi-element wings can enhance a vehicle's handling and stability by reduc…

physics.flu-dyn2023

A Novel Framework for Optimizing Gurney Flaps using RBF Neural Network and Cuckoo Search Algorithm

Aryan Tyagi, Paras Singh, Aryaman Rao +2

Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lowe…

physics.flu-dyn20232 cited

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

Tanishk Nandal, Vaibhav Fulara, Raj Kumar Singh

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 nov…