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Rapidly Varying Flow01:24

Rapidly Varying Flow

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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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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 Flow01:27

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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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Laminar Flow: Problem Solving01:24

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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Turbulent Flow: Problem Solving01:09

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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Gradually Varying Flow01:29

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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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Predicting congregational and crowd spread-out flow using YOLOv4 and DeepSORT.

Nahla Aljojo1, Hanin Ardah2, Ahmed Alamri3

  • 1Department of Information Systems & Technology, College of Computer sciences & Engineering, University of Jeddah, Jeddah, Saudi Arabia. nmaljojo@uj.edu.sa.

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Summary
This summary is machine-generated.

This study uses YOLOv4 and DeepSORT for crowd management during religious events like Hajj. The system accurately tracks individuals, aiding in safety and population flow analysis for large gatherings.

Keywords:
CongestionCrowd managementDeepSORTFlexi-ManeuversYOLOv4

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Safety

Background:

  • Urban expansion necessitates advanced crowd control and population management strategies.
  • Major events require structured organization for participant safety and emergency mitigation.
  • Analyzing congregational and dispersed crowd flow dynamics presents significant challenges.

Purpose of the Study:

  • To develop and evaluate a predictive system for managing large crowds during religious events.
  • To address challenges in predicting and analyzing crowd flow dynamics.
  • To enhance safety and efficiency in managing mass gatherings.

Main Methods:

  • A case study analyzing religious events (Hajj and Umrah) in Saudi Arabia with 1.5-4 million participants.
  • Implementation of two distinct algorithms: YOLOv4 for object detection and DeepSORT for object tracking.
  • Training the YOLOv4 and DeepSORT models on a dataset of pilgrim images captured during Hajj 2019.

Main Results:

  • YOLOv4 achieved 95.30% accuracy, 94.80% precision, 95.60% recall, and 95.20% F1-score.
  • DeepSORT achieved 91.50% accuracy and 92.30% recall.
  • The system successfully identified and tracked individuals, counted entries/exits, and classified crowd density (low, medium, high).

Conclusions:

  • The developed system effectively manages complex crowd dynamics during major religious gatherings.
  • The YOLOv4 and DeepSORT approach demonstrates the potential to revolutionize crowd management strategies.
  • Accurate individual identification and flow analysis are achievable even in crowded, dynamic environments.