Study the Affecting Factors on Free overfall Flow and Bed Roughness in Semi-Circular Channels by Artificial Neural Network

Q3 Environmental Science

Tikrit Journal of Engineering Sciences Pub Date : 2022-12-25 DOI:10.25130/tjes.29.4.8

Raad Hoobi, Ayad Saoud Najem

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引用次数: 1

Abstract

One of the significant problems facing the water resource engineer is calculating the coefficient of roughness for subsequent design calculations of the discharge amount of a channel or river. In this study, experiments were conducted in a semi-circular, straight channel to investigate the factors affecting bed roughness and flow discharge using Artificial Neural Network (ANN). For this purpose, three semi-circular channel models with free overfall were constructed and installed in a 6-meter-long laboratory flume. The length of these models was 2.50 m with three different diameters (D= 150, 187, and 237mm) and three bed slopes (S=0.004, 0.008, and 0.012). Three sand particle sizes (ds) were used for each semi-circular channel to roughen the bed. The results showed that the Manning roughness coefficient obtained using a rough bed surface was higher than the channel with a smooth bed surface. Also, the results revealed that the Manning roughness coefficient and the Froude number were inversely related. (ANN) analysis showed a good agreement between the experimental and predicted results of flow and roughness. The bring depth (yb) had an 85.8% impact percentage on the free overfall discharge for semi-circular channels, while the bottom slope (S) had only 1.1%.

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利用人工神经网络研究了半圆形河道自由溢流和河床粗糙度的影响因素

水资源工程师面临的一个重要问题是计算粗糙度系数，以便随后对河道或河流的流量进行设计计算。在本研究中，使用人工神经网络（ANN）在半圆形直通道中进行了实验，以研究影响河床粗糙度和流量的因素。为此，建造了三个带有自由溢流的半圆形渠道模型，并将其安装在一个6米长的实验室水槽中。这些模型的长度为2.50 m，具有三种不同的直径（D=150、187和237mm）和三个床坡（S=0.004、0.008和0.012）。每个半圆形通道使用三种砂粒尺寸（ds）来使床变粗糙。结果表明，使用粗糙床表面获得的曼宁粗糙度系数高于使用光滑床表面的通道。结果表明，曼宁粗糙度系数与弗劳德数呈负相关。（ANN）分析表明，流量和粗糙度的实验结果与预测结果之间具有良好的一致性。对于半圆形渠道，引入深度（yb）对自由溢流流量的影响百分比为85.8%，而底坡（S）仅为1.1%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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