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相关概念视频

Viscosity01:17

Viscosity

5.9K
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...
5.9K
Viscosity of Fluid01:19

Viscosity of Fluid

430
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.
430
Stokes' Law01:20

Stokes' Law

1.3K
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...
1.3K
Navier–Stokes Equations01:28

Navier–Stokes Equations

518
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...
518
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

230
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...
230
Couette Flow01:22

Couette Flow

284
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...
284

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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跟随流动:深度学习方法用于自主粘度估计.

Michael Walker1, Gabriella Pizzuto1, Hatem Fakhruldeen1

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

Digital discovery
|November 28, 2023
PubMed
概括

这项研究引入了一种使用人工智能的新方法,用于非侵入性地测量流体粘度. 这种方法使粘度测量自动化,在识别液体和加速材料发现方面显著超过人类的准确性.

科学领域:

  • 机器人和自动化 机器人和自动化
  • 人工智能的人工智能
  • 材料科学 材料科学 材料科学

背景情况:

  • 传统的粘度测量是缓慢的,手动的和侵入性的,阻碍了高效的材料发现和自主工作流.
  • 准确和快速的粘度评估对于工艺控制和识别新材料至关重要,但目前的方法存在局限性.

研究的目的:

  • 利用卷积神经网络 (CNN) 开发一种非侵入性,自动化的粘度估计方法.
  • 为了证明这种人工智能驱动的方法在识别未知的实验室溶剂及其加速材料发现潜力的能力.

主要方法:

  • 一个双臂协作机器人被用来自主收集流体运动的视频数据.
  • 在这个视频数据上训练了一个三维卷积神经网络 (3D-CNN),通过分类和回归来估计粘度.
  • 对于液体识别任务,3D-CNN模型的性能与人类参与者进行了比较.

主要成果:

  • 3D-CNN模型在粘度估计和溶剂识别方面取得了很高的准确性,显著超过了人类参与者.
  • 由于每种液体的视频不到50条,该模型在识别五种溶剂时达到88%的准确性,而人类观察的准确率为32%.
  • 这种基于人工智能的方法证明了自主化学的传统粘度计的强有力的替代方案.

结论:

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  • 这种人工智能驱动的非侵入性粘度测量技术加速了材料的发现,并增强了自主化学工作流程.
  • 开发的方法为工艺控制和基于粘度变化识别新材料提供了可靠和高效的替代方案.