An Overview of Artificial Intelligence and Machine Learning in Computational Fluid Dynamics

Computational Fluid Dynamics (CFD) is widely used in various industries to model fluid flow, but is particularly influential in the field of aerospace engineering. Traditional CFD methods are computationally expensive and time-consuming, limiting their effectiveness in real-time applications, hence, the integration of Artificial Intelligence (AI), especially Machine Learning (ML) and deep learning (DL), has significantly enhanced CFD simulations by reducing computational costs, improving accuracy, and enabling faster predictions. AI-based approaches, such as Artificial Neural Networks (ANN) and Physics-Informed Neural Networks (PINN), have revolutionized CFD by learning from existing data and incorporating physical laws into simulations. Artificial Neural Networks provide fast approximations of flow behavior and reduce the dependence on iterative numerical solutions, while PINNs combine physics-Informed with neural networks to improve the accuracy of complex flow predictions, including turbulence modeling. These advances have led to improved aerodynamic designs, real-time flow monitoring, and optimization of engineering systems, resulting in cost-effective and efficient solutions. Despite these advantages, challenges such as data quality, model interpretability, and the need for specialized knowledge remain critical barriers to the adoption of AI in CFD.

کلیدواژه ها:

Computational Fluid Dynamics (CFD) ، Artificial Intelligence (AI) ، Machine Learning (ML) ، Artificial Neural Networks (ANN) ، Physics-Informed Neural Networks (PINN)