Buckle propagation failure of subsea pipelines; experimental, analytical, and numerical simulations.

AbstractThis research program investigates buckle propagation in subsea pipeline systems, The study aims to understand and predict propagation pressure (<italic>PP</italic>), essential for ensuring the structural integrity and safe operation of these pipelines, hypothesizing that incorporating strain hardening effects into the strain energy approach can significantly enhance the prediction of propagation pressure (<italic>PP</italic>) and that geometric parameters, particularly the diameter-to-thickness (<italic>D/t</italic>) ratio, play a major role in <italic>PP</italic> prediction. Accurate <italic>PP</italic> prediction is essential for ensuring the structural integrity and safe operation of these pipelines. The investigation is conducted through analytical, experimental, numerical, and machine learning approaches. A new analytical solution for <italic>PP</italic> is proposed, incorporating strain hardening effects using a strain energy approach. Experimental tests conducted using a hyperbaric chamber cover a wide range of pipeline configurations and materials, including steel, aluminum, and stainless steel. Complementing experimental data, numerical simulations using finite element methods facilitate parametric dependence analysis and the visualization of results through 3D charts. More than 600 FE models were simulated to investigate the influence of geometric and material parameters on <italic>PP</italic>. The findings from both experimental and numerical analyses indicated that the geometric and material properties have a significant impact on <italic>PP</italic>, specifically, the diameter-to-thickness (<italic>D/t</italic>) ratio, yield stress (<italic>σy</italic>), and tangent modulus (<italic>E′</italic>). Furthermore, Machine Learning (ML) techniques have been used to predict the <italic>PP</italic> value and have concluded that the Random Forest (RF) technique outperformed the K-Nearest Neighbors (KNN), and Multi-layer Perceptron (MLP) neural network techniques. Overall, this study concludes that analytical, numerical, and machine learning approaches offer valuable insights into buckle propagation in subsea pipelines, contributing to improved safety and reliability in offshore oil and gas operations. [ABSTRACT FROM AUTHOR]