Application of artificial neural network to predict the friction factor of open channel flow
Application of artificial neural network to predict the friction factor of open channel flow
复制标题
应用人工神经网络预测明渠水流摩擦系数
DOI：
10.1016/j.cnsns.2008.06.020
复制
发表时间：
2009-05
期刊：
影响因子：
--
通讯作者：
Huai Wenxin
中科院分区：
文献类型：
--
作者：
Zeng Yuhong;Huai Wenxin
明渠水流的摩阻系数一般受雷诺数和糙率条件的影响，可以通过实验室或现场测量确定。在实际应用中，研究人员经常发现，摩擦系数的正确选择对水力问题的正确预测至关重要。本文以雷诺数和相对粗糙度为输入参数，建立了一个三层人工神经网络模型来预测明渠水流摩阻因子。采用Levenberg-Marquardt（LM）学习算法对模型进行训练，并将训练后的网络与实验数据进行分离，得到了实验结果与预测结果之间良好的相关性。最后，将人工神经网络的模拟结果与经验公式的计算结果进行了比较，两者的比较表明，只要有足够的样本，人工神经网络模型可以正确地预测摩擦因数与其影响因素之间的非线性关系。应用结果表明，人工神经网络模型是一种方便、有效的方法，可以应用于工程实践中，对传统的水力学问题进行分析，而这些问题大多是建立在实验室试验的基础上。
The friction factor of an open channel flow is generally affected by the Reynolds number and the roughness conditions, and can be decided by laboratory or field measurements. During practical applications, researchers often find that a correct choice of the friction factor can be crucial to make a sound prediction of hydraulic problems. In this paper, a three-layer artificial neural network (ANN) was set up to predict the friction factors of an open channel flow, with the Reynolds number and the relative roughness as two input parameters. The Levenberg–Marquardt (LM) learning algorithm was employed to train the model by using laboratory experimental data, and the trained network was tested by a single set separated from the rest of the data and a good correlation between the experimental and predicted results has been obtained. Finally, the ANN simulated results were compared with the calculated results obtained by the empirical formula and both comparisons showed that the ANN model can be used to predict the non-linear relationship between the friction factor and its influencing factors correctly once enough samples are provided. The successful application proved that ANN model can be used in engineering practice as a convenient and effective method, and those traditional hydraulic problems which are mostly based on laboratory tests can be analyzed by ANN modelling.
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DOI：
10.1016/s0169-7439(00)00099-x
发表时间：
2000-12
影响因子：
3.9
作者：
Robert Kocjancic;J. Zupan
通讯作者：
Robert Kocjancic;J. Zupan
DOI：
--
发表时间：
2003
期刊：
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影响因子：
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