AI-Augmented Turbulence and Aerodynamic Modelling: Accelerating High-Fidelity CFD Simulations with Physics-informed Neural Networks

Shumail Sahibzada , Farrukh Sher Malik, Sheharyar Nasir, Shahrukh Khan Lodhi

Computational Fluid Dynamics (CFD) simulations are essential for understanding and optimizing aerodynamic performance across various engineering applications, from aerospace to automotive design. However, high-fidelity CFD simulations are computationally expensive, requiring extensive time and resources to resolve turbulence and complex flow interactions accurately. This study proposes an AI-augmented turbulence and aerodynamic modeling framework that integrates Physics-Informed Neural Networks (PINNs) with traditional CFD solvers to accelerate high-fidelity simulations while maintaining accuracy [2]. By embedding fundamental fluid dynamics equations into deep learning architectures, our approach enables efficient turbulence modeling, reducing computational time without sacrificing precision. The framework leverages deep neural networks trained on high-resolution CFD data to predict turbulence dynamics and aerodynamic properties, thereby supplementing conventional turbulence models such as Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES). Our results demonstrate that the AI-augmented approach accelerates CFD simulations by up to 70%, significantly reducing computational costs while preserving high accuracy in key aerodynamic metrics such as drag coefficient, lift-to-drag ratio, and pressure distribution. Comparative analyses with traditional solvers confirm that our model successfully captures complex flow structures and turbulence interactions, validating its effectiveness in real-world aerodynamic applications. This study highlights the transformative potential of physics-informed AI in engineering simulations, bridging the gap between data-driven modeling and physics-based computation. The findings pave the way for the widespread adoption of AI-enhanced aerodynamic analysis, enabling real-time optimization and rapid prototyping in next-generation aerospace, automotive, and renewable energy systems.