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Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

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In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
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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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Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
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General Characteristics of Pipe Flow II01:24

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When fluid enters a pipe, it first passes through the entrance region, where the velocity profile adjusts due to viscous effects. In this region, a boundary layer forms along the pipe walls and grows until it fully occupies the pipe's cross-section. Once the boundary layer merges, the flow becomes fully developed, with a steady velocity profile that remains consistent along the pipe's length.
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Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks.

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  • 1Department of Mathematics and Computer Science, University of the Balearic Islands, 07122 Palma, Spain.

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Summary
This summary is machine-generated.

This study introduces a deep neural network for 3D underwater pipe and valve segmentation using Autonomous Underwater Vehicles (AUV). The system achieves high accuracy and real-time performance for enhanced subsea inspection and manipulation tasks.

Keywords:
autonomous interventiondeep learningpipeline characterisationpipeline mappingpoint cloud segmentationreal-timeunderwater perception

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Marine Engineering

Background:

  • Underwater operations for infrastructure inspection are increasingly complex and risky.
  • Autonomous Underwater Vehicles (AUVs) offer a solution for automating these tasks, reducing risk and time.
  • Vision-based sensing, particularly RGB data, is crucial for detailed underwater inspection and manipulation.

Purpose of the Study:

  • To develop and validate a deep neural network for pixel-wise 3D segmentation of underwater pipes and valves.
  • To create algorithms for extracting critical information from segmented underwater infrastructure.
  • To demonstrate the real-time applicability of the system on an AUV for practical subsea operations.

Main Methods:

  • Utilized a deep neural network for 3D point cloud segmentation of pipes and valves from stereo camera data.
  • Developed novel algorithms for extracting pipe vectors, gripping points, structural elements, and valve information.
  • Implemented and tested the system in real-time on an Autonomous Underwater Vehicle.

Main Results:

  • Achieved high segmentation performance with a mean F1-score of 88.0% (pixel-wise) and 95.3% (instance-wise).
  • Demonstrated excellent accuracy in extracting information from pipe instances and good performance for valves.
  • Validated real-time execution on an AUV with a frame rate of 0.72 fps, suitable for manipulation and inspection.

Conclusions:

  • The developed deep learning approach enables accurate 3D segmentation and information extraction for underwater infrastructure.
  • The system's real-time performance on AUVs supports practical applications in subsea inspection and manipulation.
  • The provided dataset, model, and algorithms contribute to advancing underwater robotics research.