Kazi Alam

Computational Fluid Dynamics (CFD) is widely used to analyze fluid flow patterns, but interpreting these patterns manually is time-consuming and requires expert knowledge. This paper introduces a method that combines physics-guided synthetic CFD data generation with explainable deep learning models to automate flow pattern classification. The approach involves generating synthetic data using physics-based simulations and training deep learning models—specifically Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)—to classify flow patterns. Explainable AI techniques are applied to interpret the model decisions. The results show that Vision Transformers outperform CNNs in classification accuracy and offer better interpretability.