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Related Concept Videos

Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

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A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is...
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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Related Experiment Video

Updated: Sep 26, 2025

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
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Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks.

Robson Pacífico Guimarães Lima1,2, Juan Moises Mauricio Villanueva3, Heber Pimentel Gomes4

  • 1Technology Center (CT), Postgraduate Program in Mechanical Engineering (PPGEM), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58058-600, PB, Brazil.

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

This study introduces a novel soft sensor to estimate water flow indirectly, reducing the need for expensive physical flow meters. The developed system achieved a maximum error of 10% compared to traditional sensors.

Keywords:
artificial neural networksindirect measurementsoft sensorwater supply systems

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Area of Science:

  • Environmental Engineering
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Water supply systems are critical infrastructure requiring reliable monitoring.
  • Traditional flow meters can be costly and complex to install.
  • Indirect measurement offers a potential solution to overcome these limitations.

Purpose of the Study:

  • To develop and validate a soft sensor for indirect flow estimation in water supply systems.
  • To reduce reliance on physical flow meters through advanced modeling techniques.
  • To improve the efficiency and cost-effectiveness of water resource management.

Main Methods:

  • Utilized adaptive control for pressure controller design.
  • Employed artificial neural networks to build nonlinear system models.
  • Integrated system parameters like pressure, engine speed, and valve angle for flow estimation.

Main Results:

  • Successfully estimated water flow indirectly, bypassing the need for physical flow meters.
  • Demonstrated suppression of physical flow meter acquisition and installation costs.
  • Achieved a maximum error of 10% when compared to an electromagnetic flow sensor during experimental validation.

Conclusions:

  • The soft sensor approach is a viable and cost-effective alternative for water flow monitoring.
  • Artificial intelligence and adaptive control can significantly enhance water supply system management.
  • Further research can optimize soft sensor accuracy and applicability in diverse hydraulic systems.