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Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Related Experiment Video

Updated: Aug 16, 2025

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
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Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning.

Abdullah Zayat1, Mohanad Obeed1, Anas Chaaban1

  • 1School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces a new method for inspecting high-density polyethylene (HDPE) pipes using ultrasonic sensors and deep neural networks (DNNs). The technique accurately detects pipe diversions, achieving up to 99.6% accuracy with two sensors.

Keywords:
Zadoff–Chu sequenceconvolutional neural network (CNN)deep neural network (DNN)high-density polyethylene (HDPE)long-short term memory (LSTM)piezoelectricrecurrent neural network (RNN)structural health monitoring (SHM)ultrasonic-guided waves (UGWs)

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Area of Science:

  • Materials Science
  • Non-Destructive Testing
  • Signal Processing

Background:

  • High-density polyethylene (HDPE) pipes are widely used in infrastructure.
  • Ensuring the integrity of HDPE pipes is crucial for safety and efficiency.
  • Current inspection methods may have limitations in detecting subtle defects like diversions.

Purpose of the Study:

  • To develop and validate a novel technique for detecting diversions in HDPE pipes.
  • To leverage ultrasonic sensing, advanced signal processing, and deep neural networks (DNNs) for pipe inspection.
  • To achieve high accuracy in identifying pipe diversions.

Main Methods:

  • Utilizing a custom-designed array of piezoelectric transmitters and receivers to send and receive ultrasonic signals through HDPE pipes.
  • Employing the Zadoff-Chu sequence for signal modulation and estimating the pipe's channel response via correlation properties.
  • Feeding the processed ultrasonic signals into a deep neural network (DNN) for feature extraction and diversion detection.

Main Results:

  • The proposed technique achieved an average classification accuracy of 90.3% with a single sensor.
  • With two sensors, the accuracy significantly improved to 99.6% for detecting diversions in 34-inch HDPE pipes.
  • The method demonstrated robustness and potential for generalization to pipes of varying diameters and materials.

Conclusions:

  • The developed ultrasonic and DNN-based technique offers a highly accurate and reliable method for inspecting HDPE pipes for diversions.
  • The system's performance, particularly with dual-sensor configuration, shows significant promise for practical non-destructive evaluation applications.
  • The approach is adaptable for diverse pipe materials and sizes, highlighting its broad applicability in infrastructure monitoring.