Simulation-driven leak diagnostics in pipelines: machine learning and curve fitting-based prediction models

Abstract

Leakages in pipelines are still a significant challenge for fluid transportation systems, since they raise risks to efficiency, positive environmental impact and cost-effectiveness. Methods like eye inspection, pressure measurement and tracking flow rates do not usually catch leaks efficiently or accurately in big and busy pipeline installations. This study provides a way to use interpretable physical modeling and the predictive ability of machine learning to make the detection and classification of leaks more efficient. Second-degree polynomial regression and Random Forest regression models are both used in the study which are applied to synthetic data made using COMSOL Multiphysics. By analyzing pressure and velocity using regression, we can clearly understand the effects on leak size and position and by using Random Forest, we can attain much higher precision in predictions, with R² scores of 0.998 for leak size and 0.9999 for leak position. Looking at the importance of various features, it was clear that flow velocity has the most influence on leak dynamics and K-Means clustering organized the risks into helpful severity groups. All of these models together build a strong and flexible system designed for smart pipeline infrastructure use. It moves forward in predictive maintenance and helps unite our common sense with modern analytic methods used for pipeline condition monitoring.