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

W. Dhanistha, M. Syarifudin, Nathalia Damastuti, Ridho Akbar
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Abstract

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.
应用计算流体力学(CFD)联合人工神经网络(ANN)提高海底管道泥沙分级预测精度:文献综述
近年来,海底管道在油气运输中的应用越来越多。冲刷预测是海底管道设计过程中的一个重要方面。冲刷使海底管道失去支撑,容易因偏转而失效。本文通过对冲刷相关研究的文献综述,提出了一种提高冲刷预测精度的方法。通过文献研究可知,计算流体力学(CFD)中的误差主要受流动模型的影响。现有的流动模型不能完全反映冲刷过程中湍流流动的复杂性。人工神经网络(ANN)可以减小误差值。但是,CFD- ann混合方法可以将误差值比CFD降低约20%。因此,CFD-ANN混合方法有望成为预测油气行业海底管道冲刷的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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