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OPTIMIZATION OF FLEXIBLE PIPES DYNAMIC ANALYSIS USING ARTIFICIAL NEURAL NETWORKS

机译:基于人工神经网络的柔性管动力学分析优化

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Irregular wave dynamic analysis is an extremely computational expensive process on flexible pipes design. One emerging method that aims to reduce these computational costs is the hybrid methodology that combines Finite Element Analyses (FEA) and Artificial Neural Network (ANN). The proposed hybrid methodology aims to predict flexible pipe tension and curvatures in the bend stiffener region. Firstly using short FEA simulations to train the ANN, and then using only the ANN and the prescribed floater motions to get the rest of the response histories. Two approaches are developed with respect to the training data. One uses an ANN for each sea state in the wave scatter diagram and the other develops an ANN for each wave incidence direction. In order to evaluate the accuracy of the proposed approaches, a local analysis is applied, based on the predicted tension and curvatures, to calculate stresses in tension armour wires and the corresponding flexible pipe fatigue lifes. The results are compared to those from full nonlinear FEM simulation.
机译:不规则波浪动力分析在挠性管道设计上是一个计算量巨大的过程。一种旨在减少这些计算成本的新兴方法是将有限元分析(FEA)和人工神经网络(ANN)相结合的混合方法。提出的混合方法旨在预测弯曲加劲肋区域中的挠性管张力和曲率。首先使用简短的FEA仿真来训练ANN,然后仅使用ANN和规定的浮动运动来获取其余的响应历史记录。针对训练数据开发了两种方法。一种在波浪散布图中针对每种海况使用ANN,另一种针对每种波浪入射方向开发ANN。为了评估所提出方法的准确性,基于预测的张力和曲率进行了局部分析,以计算张力铠装线中的应力和相应的挠性管疲劳寿命。将结果与来自完全非线性FEM仿真的结果进行比较。

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