Abstract
The major free spans of submarine pipelines, caused by the combined action of waves and currents can pose significant risks to pipeline operational safety. In this study, a numerical model based on the Reynolds-averaged Navier–Stokes (RANS) equation and sediment transport principles was established to investigate local scouring around a pipeline under wave and current conditions. The influence of the pipeline diameter D, flow incident angle a, current velocity Uc and KC number on the scour depth was analyzed. Based on a total of 145 sets of numerical simulation cases, a dataset was established, and all data were normalized and divided into a training set (80%) and a test set (20%). A decision-tree regression model was used to train the dataset, and a machine-learning prediction model was constructed. In the testing stage, the model had R2 = 0.94, RMSE = 0.043 for the scour depth S prediction; R2 = 0.92, RMSE = 0.096 for the dimensionless scour depth S/D prediction; and R2 = 0.97, RMSE = 0.267 for the scour hole width L prediction. The machine-learning prediction model exhibited good performance and computational efficiency, and can be used for rapid prediction of submarine pipeline scour characteristics.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callSimilar Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
