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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

400
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.
400
Surface Tension, Capillary Action, and Viscosity02:57

Surface Tension, Capillary Action, and Viscosity

27.8K
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...
27.8K
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
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

221
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...
221
Accelerating Fluids01:17

Accelerating Fluids

1.0K
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:
1.0K

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相关实验视频

Updated: Jun 30, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

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推进材料性质预测:使用基于物理的机器学习模型来预测粘度.

Alex K Chew1, Matthew Sender2, Zachary Kaplan1

  • 1Schrödinger, Inc., New York, 10036, USA.

Journal of cheminformatics
|March 15, 2024
PubMed
概括
此摘要是机器生成的。

将分子动力学 (MD) 描述符集成到定量结构-属性关系 (QSPR) 模型中,可以显著改善材料的粘度预测,特别是在有限的数据的情况下. 这种方法提高了材料科学机器学习的准确性和可解释性.

关键词:
经典分子动力学模拟的模拟.机器学习 机器学习有机分子有机分子.物理属性 物理属性定量结构 财产关系粘度 粘度 粘度 粘度 粘度

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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

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Experimental Multiscale Methodology for Predicting Material Fouling Resistance
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Experimental Multiscale Methodology for Predicting Material Fouling Resistance

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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

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Experimental Multiscale Methodology for Predicting Material Fouling Resistance
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 在材料中的机器学习

背景情况:

  • 基于物理学的模型难以准确计算材料特性,如粘度.
  • 数据驱动的机器学习 (ML) 模型在材料科学中面临挑战,因为数据的可用性有限.
  • 准确预测粘度对于理解液体系统和材料行为至关重要.

研究的目的:

  • 提高ML模型的准确性和可解释性,用于预测材料特性.
  • 将来自分子动力学 (MD) 模拟的物理信息描述符集成到定量结构-属性关系 (QSPR) 模型中.
  • 使用QSPR模型准确预测小型有机分子的温度依赖粘度.

主要方法:

  • 策划了来自科学文献和数据库的4000多个小型有机分子粘度的数据集.
  • 开发了基于描述器和图形神经网络的QSPR模型,其中包含MD模拟描述器.
  • 利用特征重要性工具来确定关键的预测描述符.

主要成果:

  • 整合MD描述符显著提高了粘度预测的准确性,特别是对于数据集的数据点少于1000个.
  • 通过MD描述器捕获的分子间相互作用被确定为粘度预测中最关键的特征.
  • 开发的QSPR模型准确地预测了六种电池相关溶剂的粘度和温度之间的反向关系,包括看不见的溶剂.

结论:

  • 将MD描述符集成到QSPR模型中是一种有效的策略,可以提高具有挑战性的材料性质的预测准确性.
  • 这种混合方法克服了基于物理的模型的局限性和材料科学机器学习中的数据稀缺性.
  • 该研究证明了MD增强的QSPR对预测液体系统中温度依赖粘度的实用性.