Session: DA-04-01 Inelastic, Nonlinear, and Limit Load Analysis

Paper Number: 129762

129762 - An Efficient Prediction Method for Critical Buckling Compression Axial Load of Corroded Pipelines 

Corrosion and external load are unavoidable problems in the operation of steel oil and gas pipelines. It is of great significance to clarify the local buckling critical load of corroded pipelines under the influence of various parameters for pipeline integrity management and safety assessment. In this paper, a BPNN model with efficient prediction ability is established by combining finite element method (FEM) and machine learning (ML) method, which can realize rapid and accurate prediction of critical buckling axial compression load of X80 corroded pipeline with any 9 input parameters. Specifically, the accuracy of the 3D, nonlinear FE model was verified by experimental data, and then lots of buckling simulations of corroded pipelines with different input parameters were carried out. The data set with input-output relationship obtained by the FEM is used as the training data of the deep learning model. Through the hyperparameter debugging process, a 9-dimensional input layer, a 5-dimensional 80-node hidden layer and a 1-dimensional output layer BPNN model with deep learning and efficient prediction ability is established. Compared with the FEM, the prediction efficiency of the model can be improved by thousands of times. Finally, the influence of corrosion defect geometry on the critical buckling load of corroded pipelines is studied by using the established efficient prediction model. It is clear that the defect depth and defect width have a great influence on the critical buckling load, while the larger defect length almost no longer affects the buckling load.

Presenting Author: Yuran Fan China University of Petroleum, Beijing

Presenting Author Biography: Currently mainly engaged in research on the integrity of oil and gas pipelines.

Authors:

Yuran Fan China University of Petroleum, Beijing
Tieyao Zhang China University of Petroleum, Beijing
Yi Shuai China University of Petroleum, Beijing
Lei Shi Sinopec (Dalian) Research Institute of Petroleum and Petrochemicals Co., Ltd.
Jian Shuai China University of petroleum, Beijing
Xingtao Li China National Oil and Gas Exploration and Development Co.
Haipeng Liu Sino-Pipeline International Company Limited