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Related Concept Videos

Viscosity01:17

Viscosity

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When water is poured into a glass, it falls freely and quickly, whereas if honey or maple syrup is poured over a pancake, it flows slowly and sticks to the surface of the container. This difference in the flow of different kinds of liquids arises due to the fluid friction between the liquid layers and the liquid and the surrounding material. This property of fluids is called fluid viscosity. In this example, water has a lower viscosity than honey and maple syrup.
The SI unit of viscosity is...
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Viscosity of Fluid01:19

Viscosity of Fluid

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Viscosity measures the resistance a fluid offers to flow and deformation. It results from internal friction between layers of fluid moving relative to one another. Dynamic viscosity, denoted by the Greek letter mu (μ), quantifies the force needed to move one fluid layer over another. For Newtonian fluids like water and air, the relationship between the shearing stress and the rate of shearing strain is linear, meaning their viscosity remains constant regardless of the applied stress.
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Stokes' Law01:20

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Viscous forces, like friction, are intermolecular forces that resist the relative motion of molecules over each other. When a solid body moves through a liquid, viscous forces drag it in the opposite direction. The force's magnitude depends on the solid's shape and size, as well as its speed and the liquid's coefficient of viscosity, density and temperature.
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Navier–Stokes Equations01:28

Navier–Stokes Equations

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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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Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

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Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
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Couette Flow01:22

Couette Flow

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Couette flow represents the flow of fluid between two parallel plates, with one plate fixed and the other moving with a constant velocity. This configuration allows for a simplified analysis using the Navier-Stokes equations, which govern fluid motion under conditions of viscosity and incompressibility. For Couette flow, the assumptions include a steady, laminar, incompressible flow with a zero-pressure gradient in the flow direction. This flow type is beneficial for understanding shear-driven...
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Go with the flow: deep learning methods for autonomous viscosity estimations.

Michael Walker1, Gabriella Pizzuto1, Hatem Fakhruldeen1

  • 1Department of Chemistry, University of Liverpool L69 3BX UK aicooper@liverpool.ac.uk.

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This study introduces a new method using artificial intelligence to measure fluid viscosity non-invasively. This approach automates viscosity measurements, significantly outperforming human accuracy in identifying liquids and accelerating material discovery.

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Materials Science

Background:

  • Traditional viscosity measurements are slow, manual, and invasive, hindering efficient material discovery and autonomous workflows.
  • Accurate and rapid viscosity assessment is crucial for process control and identifying new materials, but current methods present limitations.

Purpose of the Study:

  • To develop a non-invasive, automated method for viscosity estimation using convolutional neural networks (CNNs).
  • To demonstrate the capability of this AI-driven approach in identifying unknown laboratory solvents and its potential for accelerating material discovery.

Main Methods:

  • A dual-armed collaborative robot was employed to autonomously collect video data of fluid motion.
  • A 3-dimensional convolutional neural network (3D-CNN) was trained on this video data for viscosity estimation via classification and regression.
  • The performance of the 3D-CNN model was compared against human participants for liquid identification tasks.

Main Results:

  • The 3D-CNN model achieved high accuracy in viscosity estimation and solvent identification, significantly outperforming human participants.
  • With less than 50 videos per liquid, the model reached 88% accuracy in identifying five solvents, compared to 32% for human observation.
  • The AI-based method demonstrated a robust alternative to traditional viscometry for autonomous chemistry.

Conclusions:

  • This AI-powered, non-invasive viscosity measurement technique accelerates material discovery and enhances autonomous chemistry workflows.
  • The developed method offers a reliable and efficient alternative for process control and identifying novel materials based on viscosity changes.