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首页> 外文期刊>Journal of Hydroinformatics >An alternative approach for investigation of the wave-induced scour around pipelines
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An alternative approach for investigation of the wave-induced scour around pipelines

机译：研究管线周围波浪冲刷的另一种方法

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摘要

Scour around submarine pipelines remains a largely complex and not yet fully understood problem, In this study, wave-induced scour around submarine pipelines was investigated. Since various physical processes occur during the development of a scour hole, the effects of each process were considered by employing several nondimensional parameters. To find the effective parameters on equilibrium scour depth, the correlation between independent parameters (e.g. Keulegan-Carpenter number) and dependent parameter (nondimensional scour depth) were determined using different experimental data. Then, an Artificial Neural Network (ANNS) approach was used to develop a more accurate model for prediction of wave-induced scour depth around submarine pipelines. ANN models with different input parameters including gap to diameter ratio, Keulegan-Carpenter number, pipe Reynolds number, Shields number, sediment Reynolds number and boundary layer Reynolds number were trained and evaluated to find the best predictor model. To develop the ANN models, both holdout and tenfold cross-validation methods were used. In addition, an existing empirical method was examined. Results show that the empirical method has a significant error in the prediction of scour depth for the cases with an initial gap between pipe and seabed. It is also indicated that the ANN models outperform the empirical method in terms of prediction capability.
机译：海底管道周围的冲刷仍然是一个非常复杂且尚未完全理解的问题。在这项研究中，研究了海浪管道周围的波浪感应冲刷。由于在冲孔形成过程中会发生各种物理过程，因此通过使用几个无量纲参数来考虑每个过程的效果。为了找到平衡冲刷深度的有效参数，使用不同的实验数据确定了独立参数（例如Keulegan-Carpenter数）和相关参数（无量纲冲刷深度）之间的相关性。然后，使用人工神经网络（ANNS）方法开发了一个更准确的模型，用于预测海底管道周围波浪引起的冲刷深度。训练并评估具有不同输入参数（包括间隙直径比，Keulegan-Carpenter数，管道雷诺数，盾牌数，沉积物雷诺数和边界层雷诺数）的ANN模型，以找到最佳预测器模型。为了开发ANN模型，使用了保留和十倍交叉验证方法。另外，研究了现有的经验方法。结果表明，对于管道与海床之间存在初始间隙的情况，经验方法在冲刷深度的预测中存在重大误差。还表明，在预测能力方面，人工神经网络模型优于经验方法。

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