A Study of Artificial Neural Network for Constructing Channel Velocity Profiles Measured with FLDV and ADV
Date Issued
2005
Date
2005
Author(s)
Yang, Han-Chung
DOI
zh-TW
Abstract
The Artificial Neural Network (ANN) has great capability for solving various complex problems, such as function approximation. The main objective of this study was to evaluate the applicability of the ANN for simulating and estimating mean velocity profiles and the discharges accordingly. The data obtained from the open channel of the Chihtan purification plant, Taipei and experimental on steep open channel flow (Yang, 1998) were used to train and verify the proposed ANN.
An Acoustic Doppler Velocimeter (ADV) was adopted in this experimental study to measure the mean velocity profiles in the open channel of the Chihtan purification plant, Taipei, at fixed measuring section with eight different discharges and ten different depths. The total number of experimental data sets was 640. In this study, the aspect ratio varied from 1.388 to 1.438, the Reynolds number varied from 400000 to 800000, and the channel bed slope of 0.1% was selected. The Froude number ranged from 0.068 to 0.118, and all of the experiments belonged to subcritical flow. Yang (1998) used the Fiber-optic Laser Doppler Velocimetry (FLDV) to investigate the characteristics of the steep open channel flow over a smooth boundary, that the aspect ratio varied from 3.79 to 11.36, the Reynolds number varied from 10000 to 80000, and the channel bed slope of 0.3%, 1% and 2% were selected. The Froude number ranged from 0.96 to 2.72, and most of the experiments belonged to supercritical flow.
The backpropagation (BP) algorithm was applied to construct the neural network. The structure of the BP neural network included input, hidden, and output layers. The input layer contained B/H, S, and Z, while the output layer has only one node representing the velocity value in the specific location of y/H on the vertical. The experimental data were split into three sub-sets, training, validation and testing sets to train and to verify the built ANN. The results demonstrated that the constructed neural network models could be embedded as a module for estimating or generating the profiles of mean velocity for turbulent open channel flows. The model could be used as a powerful tool to simulate and estimate the flow profiles for the similar flow conditions to reduce the cost of the experimental work. The trained model could also be used to provide the flow profiles for missing data, and estimated the discharge for a given specific depth.
Subjects
光纖雷射杜卜勒測速儀
倒傳遞網路
類神經網路
聲波杜卜勒測速儀
AcousticDoppler Velocimeter
Fiber-optic Laser Doppler Velocimetry
Back-Propagation Network
Artificial Neural Network
Type
thesis
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