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首页> 外文期刊>Journal of Materials Science and Chemical Engineering >Artificial Neural Network Model for Friction Factor Prediction
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Artificial Neural Network Model for Friction Factor Prediction

机译：预测摩擦系数的人工神经网络模型

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Friction factor estimation is essential in fluid flow in pipes calculations. The Colebrook equation, which is a referential standard for its estimation, is implicit in friction factor, f. This implies that f can only be obtained via iterative solution. Sequel to this, explicit approximations of the Colebrook equation developed using analytical approaches have been proposed. A shift in paradigm is the application of artificial intelligence in the area of fluid flow. The use of artificial neural network, an artificial intelligence technique for prediction of friction factor was investigated in this study. The network having a 2-30-30-1 topology was trained using the Levenberg-Marquardt back propagation algorithm. The inputs to the network consisted of 60,000 dataset of Reynolds number and relative roughness which were transformed to logarithmic scales. The performance evaluation of the model gives rise to a mean square error value of 2.456 × 10–15 and a relative error of not more than 0.004%. The error indices are less than those of previously developed neural network models and a vast majority of the non neural networks are based on explicit analytical approximations of the Colebrook equation.
机译：摩擦系数估算对于管道计算中的流体流动至关重要。 Colebrook方程是其估计的参考标准，它隐含在摩擦系数f中。这意味着只能通过迭代解获得f。与此相伴，已经提出了使用解析方法开发的Colebrook方程的显式近似。范式的转变是人工智能在流体流动领域的应用。在这项研究中，研究了使用人工神经网络，一种预测摩擦系数的人工智能技术。使用Levenberg-Marquardt反向传播算法训练了具有2-30-30-1拓扑的网络。该网络的输入由60,000个雷诺数和相对粗糙度数据集组成，这些数据集已转换为对数刻度。对模型的性能评估得出的均方误差值为2.456×10-15，相对误差不超过0.004％。误差指数小于以前开发的神经网络模型，并且绝大多数非神经网络都基于Colebrook方程的显式解析近似。

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