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Types of Fluids01:27

Types of Fluids

591
Fluids can be classified into Newtonian and non-Newtonian fluids based on their response to shear stress. Newtonian fluids have a linear relationship between shear stress and the shear strain rate, following Newton's law of viscosity. Their viscosity remains constant regardless of the shear rate, making their behavior predictable and easier to analyze. Common examples include water, air, oil, and gasoline.
In contrast, non-Newtonian fluids do not follow Newton's law of viscosity, and...
591
Characteristics of Fluids01:31

Characteristics of Fluids

756
Fluids differ from solids primarily in their molecular structure and stress response. Solids have tightly packed molecules with strong intermolecular forces, maintaining their shape and resisting deformation. In contrast, fluids have molecules spaced farther apart with weaker forces, allowing them to flow and deform easily.
Fluids, which include both liquids and gases, are substances that deform continuously under shearing stress. For example, water and oil are liquids with molecules that can...
756
Characteristics of Fluids01:20

Characteristics of Fluids

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When a force is applied parallel to the top surface of a solid, it resists the applied force due to the internal frictional forces between the layers of the solid known as shearing resistance. However, when the force is removed, the shearing forces restore the original shape of the solid. Other deformation forces also cause temporary changes in shape if the forces are not beyond a threshold magnitude. Solids tend to retain their shape, making the study of their rest and motion easier. Beyond...
6.7K
Surface Tension of Fluid01:22

Surface Tension of Fluid

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Surface tension is a fundamental property of fluids, occurring at the boundary between a liquid and a gas or between two immiscible liquids. This phenomenon arises from the cohesive forces between molecules at the fluid's surface, creating an effect similar to a stretched elastic membrane. Inside each fluid, molecules are equally attracted in all directions by neighboring molecules, but surface molecules experience a net inward force, resulting in surface tension.
Surface tension varies...
728
Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

705
As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
705
Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

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Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
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Dynamic and weak electric double layers in ultrathin nanopores.

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Related Experiment Video

Updated: Oct 29, 2025

Analyzing Mixing Inhomogeneity in a Microfluidic Device by Microscale Schlieren Technique
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Understanding simple liquids through statistical and deep learning approaches.

A Moradzadeh1, N R Aluru1

  • 1Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

The Journal of Chemical Physics
|July 9, 2021
PubMed
Summary
This summary is machine-generated.

Statistical and deep learning methods reveal quasi-universal properties of simple liquids. A statistical model explains structural similarity by matching net force distributions, while DeepILST identifies equivalent Lennard-Jones liquids.

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Area of Science:

  • Statistical Mechanics
  • Computational Physics
  • Machine Learning

Background:

  • Simple liquids exhibit quasi-universal structural properties despite varying interaction potentials.
  • Understanding these properties is crucial for molecular simulations and materials science.

Purpose of the Study:

  • To provide a probabilistic explanation for the structural similarity in simple liquids.
  • To develop and validate a deep learning framework for identifying structurally equivalent liquids.

Main Methods:

  • A statistical model was developed to analyze the probability density function of the net force.
  • The DeepILST framework was employed to parameterize Lennard-Jones potentials and identify isomorphic liquids.
  • Radial distribution functions and Kullback-Leibler errors were used for validation.

Main Results:

  • The statistical model successfully explains structural similarity by matching net force distributions.
  • DeepILST identified structurally equivalent Lennard-Jones liquids and demonstrated consistency across various potentials.
  • The study provides insights into the quasi-universality of simple liquids.

Conclusions:

  • The combination of statistical modeling and deep learning offers powerful tools for understanding liquid properties.
  • Quasi-universality in simple liquids can be effectively characterized and predicted.
  • This research advances the parameterization and simulation of liquid systems.