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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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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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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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Related Experiment Video

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Swimming Performance Assessment in Fishes
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Learning to swim efficiently in a nonuniform flow field.

Krongtum Sankaewtong1, John J Molina1, Matthew S Turner1,2

  • 1Department of Chemical Engineering, Kyoto University, Kyoto 615-8510, Japan.

Physical Review. E
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

Microswimmers use mechanical cues for navigation. Deep reinforcement learning reveals that swimmers can learn complex tasks using local or global information, optimizing their movement in non-uniform flows.

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Area of Science:

  • Fluid dynamics
  • Robotics
  • Biophysics

Background:

  • Microswimmers navigate complex fluid environments by sensing mechanical cues.
  • Understanding microswimmer navigation is crucial for applications in targeted drug delivery and micro-robotics.

Purpose of the Study:

  • To investigate how microswimmers can be trained to perform specific swimming tasks in non-uniform flow fields using deep reinforcement learning.
  • To analyze the impact of local versus non-local information on navigation strategies.
  • To explore different swimming modes (pusher, puller, neutral).

Main Methods:

  • Combining deep reinforcement learning (DRL) with direct numerical simulations (DNS) to model microswimmer hydrodynamics.
  • Training DRL agents to achieve specific objectives in a zigzag shear flow.
  • Analyzing the information requirements for optimal policy acquisition.

Main Results:

  • Microswimmers can learn to navigate in vorticity, shear-gradient, and shear-flow directions.
  • Optimal policy for vorticity and shear-gradient tasks requires only laboratory frame orientation information.
  • Optimal policy for shear-flow tasks necessitates both translational and rotational velocity information.
  • Swimmers using local hydrodynamic force sensing achieved comparable performance to those using laboratory frame variables.

Conclusions:

  • Deep reinforcement learning is effective for training microswimmers in complex flows.
  • The type of information available significantly influences navigation strategy and task performance.
  • Sensing local hydrodynamic forces can be a viable alternative to using global frame information for microswimmer navigation.