The Application of A Combined Computational Fluid Dynamics (CFD) Artificial Neural Network (ANN) to Increase The Prediction Accuracy of Sediment Grading in Subsea Pipes: A Literature Review

Ridho Akbar (Universitas Muhammadiyah Surabaya)
Wimala L. Dhanistha (Marine Engineering Department, Sepuluh NopemberInstitute of Technology, Surabaya, Indonesia)
M.Rizky Syarifudin (Marine Engineering Department, Sepuluh NopemberInstitute of Technology, Surabaya, Indonesia)
Nathalia Damastuti (Engineering and Computer Science Faculty, NarotamaUniversity, Surabaya, Indonesia)
Ridho Akbar (Institute of Information Processing and Automation, College of information engineering, Zhejiang University of Technology, Hangzhou, China)

IJEEIT : International Journal of Electrical Engineering and Information Technology
Vol 5 No 2 (2022): September 2022

In recent years, the implementation of subsea pipelines for oil and gas transportation has increased. One of the important aspects of the design process of the subsea pipeline is scour prediction. Scouring causes the subsea pipeline to lose its support and is susceptible to failure due to deflection. This paper presents the result of a literature review of scouring-related research to obtain a method to increase scouring prediction accuracy. Based on the literature research, it is known that the errors found in Computational Fluid Dynamics (CFD) are mainly affected by the flow models. Existing flow models cannot fully represent the complexity of turbulent flow that occurs during the scouring process. Artificial Neural Network (ANN) can reduce the error value. But, the CFD-ANN hybrid methods can potentially decrease the error value by about 20% more than CFD. Therefore, the CFD-ANN hybrid method is expected to be a new method that could be used to predict subsea pipeline scouring in the oil and gas industry.