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Dimensionless Groups in Fluid Mechanics01:15

Dimensionless Groups in Fluid Mechanics

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Dimensionless groups in fluid mechanics provide simplified ratios that help analyze fluid behavior without relying on specific units. The Reynolds number (Re), which represents the ratio of inertial to viscous forces, distinguishes between laminar and turbulent flows, making it essential in the design of pipelines and aerodynamic surfaces. The Froude number (Fr), the ratio of inertial to gravitational forces, is particularly useful in predicting wave formation and hydraulic jumps in...
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For incompressible Newtonian fluids, where density remains constant, stresses show a linear relationship with the deformation rate, defined by normal and shear stresses. Normal stresses depend on the pressure exerted on the fluid and the rate of deformation in specific directions, which determines how fluid flows under varying pressures. Shear stresses, on the other hand, act tangentially across fluid layers. They explain how adjacent fluid layers slide relative to one another, connecting...
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Learning to Navigate in Chemical Fields Without A Map at Low Reynolds Numbers.

Yangzhe Liu1, On Shun Pak2, Alan C H Tsang1

  • 1Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
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Artificial microswimmers can now navigate unknown environments using deep reinforcement learning. This new mapless approach mimics biological strategies for effective target searching and adapts to changing conditions.

Keywords:
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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Biomimicry

Background:

  • Effective navigation for microswimmers, both biological and artificial, is crucial for target localization using limited environmental cues.
  • Biological microswimmers exhibit sophisticated mapless navigation strategies, a capability that remains challenging to replicate in artificial systems.
  • Current artificial microswimmers often rely on pre-existing maps, limiting their adaptability in unknown or dynamic environments.

Purpose of the Study:

  • To develop an autonomous navigation system for artificial microswimmers capable of searching for targets in unknown environments without pre-existing maps.
  • To enable artificial microswimmers to respond to local environmental cues for effective navigation, similar to biological counterparts.

Main Methods:

  • Deep reinforcement learning was utilized to train a reconfigurable artificial microswimmer for mapless navigation.
  • The microswimmer was trained to navigate towards a chemical source by sensing and responding to local chemical signals.

Main Results:

  • The artificial microswimmer successfully navigated towards a chemical source by adapting its locomotion strategy, exhibiting a 'run-and-tumble' behavior akin to bacterial chemotaxis.
  • The mapless microswimmer demonstrated robust performance in environments with significant deviations from its training conditions, including fluctuating and time-varying chemical fields.
  • The system showed capability in exploring complex chemical landscapes with multiple concentration maxima, efficiently locating target areas.

Conclusions:

  • Deep reinforcement learning provides a viable method for creating autonomous, mapless navigation in artificial microswimmers.
  • The developed strategy allows microswimmers to effectively search for targets in unknown and dynamic environments, mimicking biological navigation.
  • This research paves the way for advanced autonomous microswimmers applicable in diverse, unpredictable settings.