Burst Pressure Prediction of Defective Pipeline Test Spools by Metal Magnetic Memory Method

Abstract This paper proposes a novel method for predicting the burst pressure of defective pipelines using magnetic memory method (MMM), aiming to enhance safety and reliability in pipeline management. Defects with varying depths and lengths were introduced into API 5L X42 and ASTM A106 test spools using an electrolysis method. The spools were subjected to pressurized burst testing, during which MMM signals were continuously monitored on their surfaces. Additional data from pressure transducers and strain sensors provided insights into stress and pressure distributions. The analysis focused on the behavior of MMM signals around defects as internal pressure increased. Results showed a clear correlation between the evolution of MMM signals and rising internal pressure, indicating localized stress concentrations at defect sites. Based on this data, a formula was derived to calculate burst pressure using the magnetic stress concentration factor. The model demonstrated a prediction error of less than 10% compared to actual test data. It also outperformed traditional models like ASME B31G and DNV-RP-F101, which rely solely on defect geometry, by incorporating stress-induced magnetic changes for a more accurate risk assessment. The MMM-based prediction method offers a more effective approach to burst pressure forecasting, contributing to proactive pipeline integrity management.