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相关概念视频

Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

411
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...
411
Viscosity of Fluid01:19

Viscosity of Fluid

731
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.
731
Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

610
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...
610
Surface Tension, Capillary Action, and Viscosity02:57

Surface Tension, Capillary Action, and Viscosity

29.6K
Surface Tension
The various IMFs between identical molecules of a substance are examples of cohesive forces. The molecules within a liquid are surrounded by other molecules and are attracted equally in all directions by the cohesive forces within the liquid. However, the molecules on the surface of a liquid are attracted only by about one-half as many molecules. Because of the unbalanced molecular attractions on the surface molecules, liquids contract to form a shape that minimizes the number...
29.6K
Viscosity01:17

Viscosity

6.2K
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...
6.2K

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相关实验视频

Updated: Sep 19, 2025

Combining Microfluidics and Microrheology to Determine Rheological Properties of Soft Matter during Repeated Phase Transitions
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使用机器学习和非平衡分子动力学相结合,探测取决于速率的液体切割粘度.

Hongyu Gao1, Minghe Zhu1, Jia Ma1,2

  • 1Department of Materials Science & Engineering, Saarland University, Campus C6.3, 66123 Saarbrücken, Germany.

Journal of chemical theory and computation
|June 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究将机器学习 (ML) 与非平衡分子动力学 (NEMD) 模拟相结合,以准确预测液体动态粘度. 综合方法克服了实验挑战,在各种切割速率中提供精确的粘度测量.

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Measuring Material Microstructure Under Flow Using 1-2 Plane Flow-Small Angle Neutron Scattering
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科学领域:

  • 计算物理学的计算物理.
  • 材料科学是一种材料科学.
  • 类风病学 类风病学 类风病学

背景情况:

  • 在高切割速率下测量液体动态粘度在实验上具有挑战性.
  • 控制热效应和解决高剪速是关键限制.
  • 了解剪切稀释行为对于复杂的流体动力学至关重要.

研究的目的:

  • 开发一种可靠的方法,用于在切割速率上准确预测粘度.
  • 将机器学习与不平衡分子动力学 (NEMD) 模拟集成.
  • 研究剪切速率,压力和温度对粘度的相互作用.

主要方法:

  • 开发了一个监督的人工神经网络 (ANN) 模型用于粘度预测.
  • 使用LAMMPS.利用非平衡分子动力学 (NEMD) 模拟.
  • 实现了"fix npt/sllod",用于模拟中精确的恒压控制.

主要成果:

  • 该ANN模型准确地预测粘度作为切割速率,压力和温度的函数.
  • 观察到明显的剪切薄化趋势和分子形态的非单调变化.
  • 证明温度对粘度的影响在高剪切速率下降.

结论:

  • 用ML增强的NEMD为粘度预测提供了一个高效和准确的框架.
  • 这项研究提供了关于在剪切应力下分子行为的见解.
  • 这种方法有助于未来研究复杂的流体动力学和材料设计.