An integrated machine learning approach for identifying flow patterns in porous media using principal component analysis and K-means clustering

Key Points

• Identifying flow patterns significantly improves understanding of porous media dynamics, enhancing modeling accuracy.
• Key evidence shows a precise identification of flow patterns using machine learning techniques like k-means clustering and PCA.
• The approach employs machine learning, specifically using principal component analysis and k-means clustering to optimize flow pattern recognition.
• This method supports more efficient modeling of fluid dynamics in porous structures, indicating broader applications in engineering.