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Videos de Conceptos Relacionados

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

Stokes' Law

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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.
The expression for the force on a solid spherical object in a fluid is called Stokes' law. Stokes' law is valid only...
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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.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
390
Accelerating Fluids01:17

Accelerating Fluids

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When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
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Types of Fluids01:27

Types of Fluids

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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...
485

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Updated: Sep 9, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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Comentario sobre "Predicción avanzada de las propiedades de los materiales: utilizando modelos de aprendizaje

Maximilian Fleck1, Samir Darouich2,3, Marcelle B M Spera1

  • 1Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.

Journal of cheminformatics
|August 28, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje automático basado en la física mejora la predicción de la viscosidad cuando los datos son escasos. La integración de descriptores de dinámica molecular, como el calor de la vaporización, mejora la precisión de los líquidos puros.

Área de la Ciencia:

  • Química Física
  • Ciencias de los materiales
  • Química computacional
Palabras clave:
Teoría de la tasa de EyringUna red neuronal inspirada en la físicaViscosidad

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Sus antecedentes:

  • El aprendizaje automático basado en datos (ML) se enfrenta a desafíos en la predicción de propiedades con datos limitados.
  • La integración de modelos basados en la física con el aprendizaje automático puede mejorar el rendimiento predictivo.
  • Masticar y demás. Utilizó descriptores de dinámica molecular (DM) en relaciones cuantitativas estructura-propiedad (QSPR) para predecir la viscosidad del líquido.