Accurate and Efficient Fluid Flow Regime Classification Using Localized Texture Descriptors and Machine Learning

This paper presents an image-based framework for classifying fluid flow regimes into low and high-speed states by utilizing spatially localized texture features combined with machine learning techniques. Traditional approaches, such as Computational Fluid Dynamics (CFD) and Direct Numerical Simulations (DNS), often require extensive post-processing to extract fluid flow properties. This makes them time-consuming and less practical for real-time applications. To address this, the proposed method leverages the Local Binary Pattern (LBP) feature extraction technique. LBP effectively captures local neighborhood patterns and converts complex flow behaviors into quantifiable texture features from images of CFD. These features are then classified using various machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). The LBP-based approach demonstrates excellent performance, with the k-NN classifier achieving a maximum accuracy of 0.9879 in the case of flow past an elliptical cylinder. Similarly, the SVM classifier attains up to 0.9540 accuracy for the flow past an airfoil. Evaluations cover a range of Reynolds numbers from 200 to 5000 and turbulence intensities of 5% and 20%, confirming the robustness and effectiveness of the method. A comparative analysis with other texture-based techniques, namely Local Ternary Pattern (LTP) and Gray Level Co-occurrence Matrix (GLCM), further highlights the advantages of the proposed method. The LBP approach outperforms LTP and GLCM by 14.5% and 2.4%, respectively, in terms of prediction accuracy. This demonstrates the superior capability of LBP in flow regime classification. The dataset used in this study is publicly available at: https://www.kaggle.com/datasets/palanithanarajk/fluid-flow-images