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Related Concept Videos

Gradually Varying Flow01:29

Gradually Varying Flow

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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 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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Uniform Depth Channel Flow: Problem Solving01:18

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 Flow01:27

Uniform Depth Channel Flow

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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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Typical Model Studies01:30

Typical Model Studies

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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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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 achieved...
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Optimal Flow Sensing for Schooling Swimmers.

Pascal Weber1, Georgios Arampatzis1,2, Guido Novati1

  • 1Computational Science and Engineering Laboratory, ETH Zürich, Clausiusstrasse 33, 8092 Zürich, Switzerland.

Biomimetics (Basel, Switzerland)
|March 19, 2020
PubMed
Summary
This summary is machine-generated.

Artificial swimmers can identify leading groups using surface sensors, mimicking fish neuromasts. Optimal sensor placement allows accurate tracking of school

Keywords:
bayesian experimental designlateral lineoptimal sensor placementschoolingself-propelled swimmers

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Area of Science:

  • Biomimicry in robotics
  • Hydrodynamics and fluid dynamics
  • Collective animal behavior

Background:

  • Fish schooling behavior relies on sensory information beyond vision.
  • Pressure and shear sensors are crucial for detecting nearby conspecifics in fluid environments.
  • Understanding sensor distribution can inform artificial system design.

Purpose of the Study:

  • To determine optimal surface sensor distribution for an artificial swimmer to identify a leading group.
  • To investigate the role of pressure and shear sensors in tracking schooling behavior.
  • To compare artificial sensor placement with natural fish neuromast distribution.

Main Methods:

  • Utilized Bayesian experimental design.
  • Performed numerical simulations of two-dimensional Navier-Stokes equations for multiple self-propelled swimmers.
  • Analyzed surface pressure and shear stress data on a follower artificial swimmer.

Main Results:

  • Identified an optimal sensor distribution for the artificial swimmer.
  • Demonstrated that this distribution is qualitatively similar to fish neuromasts.
  • Showed accurate identification of the leading group's center of mass and number using surface data alone.

Conclusions:

  • Surface-based sensing is sufficient for artificial swimmers to track schools.
  • Biomimetic sensor placement can enhance robotic navigation and collective behavior.
  • This research provides insights into the principles of hydrodynamic sensing in schooling fish.