This paper utilizes computational fluid dynamics as well as a group method of data handling (GMDH) method to predict the mean velocity of intake. Firstly, the three dimensional flow pattern in a 90-degree intake is simulated with ANSYS-CFX at a transverse ratio equal to one (W*b/W*m = 1) that W*m is the width of the main channel and W*b is the width of the branch channel. The comparison of mean velocity in the simulated intake and experimental channel represents the high accuracy of ANSYS-CFX modeling (mean absolute percentage error (MAPE) = 5% and root mean square error (RMSE) = 0.017). A group method of data handling (GMDH) is one type of artificial intelligence approach that presents elementary equations for calculating the problem’s target parameter and performing well in complex nonlinear systems. In this research, to train and test the GMDH method, input data is needed in all parts of the channel. Since there is not enough laboratory data in all parts of the channel, to increase the benchmarks, the laboratory model is simulated by the Computational Fluid Dynamics (CFD) numerical model. After ensuring the proper accuracy of the numerical results, the built-in CFD numerical model has been used as a tool to create primary benchmarks in the channel points, especially in areas where there is no laboratory data. This generated data has been used in training and testing the GMDH method. The diversion angle with the longitudinal direction of the main channel (θ), the longitudinal coordinates in the intake (y*), and the ratio of the branch channel width to the main channel (Wr) have been applied as the input training data in the GMDH method to estimate mean velocity. The results of the statistical indexes used to quantitatively examine this model, (R2 = 0.86, MAPE = 10.44, RMSE = 0.03, SI = 0.12), indicated the accuracy of this model in predicting the mean velocity of the flow within open channel intakes. Appl. Sci. 2020, 10, 7521; doi:10.3390/app10217521 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 7521 2 of 15

中文翻译：

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数据处理组方法 (GMDH) 和计算流体动力学 (CFD) 的组合用于预测通道入口速度

本文利用计算流体动力学以及一组数据处理方法 (GMDH) 方法来预测平均摄入速度。首先，用ANSYS-CFX以横向比等于1（W*b/W*m = 1）模拟90度进气道的三维流动模式，W*m是主通道的宽度，W *b 是分支通道的宽度。模拟进气和实验通道中平均速度的比较代表了ANSYS-CFX建模的高精度（平均绝对百分比误差（MAPE）= 5％和均方根误差（RMSE）= 0.017）。数据处理的群方法 (GMDH) 是一种人工智能方法，它提供用于计算问题目标参数并在复杂非线性系统中表现良好的基本方程。在这项研究中，为了训练和测试 GMDH 方法，通道的所有部分都需要输入数据。由于通道各部分的实验室数据不足，为提高基准，实验室模型采用计算流体动力学（CFD）数值模型进行模拟。在确保数值结果的适当准确性后，内置的 CFD 数值模型已被用作在通道点中创建主要基准的工具，尤其是在没有实验室数据的区域。生成的数据已用于训练和测试 GMDH 方法。与主通道纵向的导流角（θ），进水口的纵向坐标（y*），并且分支通道宽度与主通道的比率（Wr）已被用作GMDH方法中的输入训练数据来估计平均速度。用于定量检验该模型的统计指标的结果（R2 = 0.86，MAPE = 10.44，RMSE = 0.03，SI = 0.12）表明该模型在预测明渠进水口内的平均流速方面具有准确性。应用程序 科学。2020、10、7521；doi:10.3390/app10217521 www.mdpi.com/journal/applsci 科学。2020, 10, 7521 2 of 15