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Prediction of Frictional Pressure Drop using Artificial Neural Network for Air-water Flow through U-Bends

机译:通过U形弯管使用人工神经网络预测摩擦压力下降

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Flow through piping components are more complex than that of straight pipe and the hydrodynamic parameters are important for design it. Artificial Neural Network (ANN) modeling is useful for the prediction when the solution from first principle equations is not tractable. Experimental data on air-water flow through U-bends are collected from our earlier published paper [29] and ANN modeling is used for the prediction of frictional pressure drop across the U-bends using three different algorithms of Multilayer Perceptrons (MLPs), i.e., Backpropagation, Levenberg-Marquardt and Scaled Conjugate gradient having a single hidden layer.
机译:流过管道部件的流量比直管更复杂,流体动力学参数对于设计而言是重要的。 人工神经网络(ANN)建模对于当来自第一原理方程的解决方案而不是易于预测时是有用的。 从我们之前的公布纸张收集通过U形弯的空气流量的实验数据[29],并且ANN建模用于使用三种不同的多层感知(MLP)的三种不同算法来预测跨U形弯曲的摩擦压力下降,即 ,Backpropagation,Levenberg-Marquardt和缩放共轭梯度具有单个隐藏层。

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