Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Viscosity01:17

Viscosity

6.1K
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...
6.1K
Viscosity of Fluid01:19

Viscosity of Fluid

679
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.
679
Stokes' Law01:20

Stokes' Law

1.6K
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...
1.6K
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

390
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

1.4K
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:
1.4K
Types of Fluids01:27

Types of Fluids

485
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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Subjective cognition trajectories, Alzheimer biomarkers, and incident mild cognitive impairment.

The journal of prevention of Alzheimer's disease·2026
Same author

Evaluation of fully automated ApoE4 proteotyping for <i>APOE</i> ε4 genotype estimation in the FINDERI cohort.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Leveraging Chemical Hidden-Space Representations Effectively in Bayesian Optimization for Experiment Design through Dimension-Aware Hyperpriors.

Journal of chemical theory and computation·2026
Same author

Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.

Journal of chemical information and modeling·2026
Same author

Intrathecal Kappa Free Light Chains in Relation to IgM Synthesis and MRZH Reaction in a Mixed Neurological Cohort.

Journal of neurochemistry·2026
Same author

Subjective cognition trajectories, Alzheimer biomarkers, and incident mild cognitive impairment.

medRxiv : the preprint server for health sciences·2026
Same journal

OpenStats: how to combine statistics and research data management (RDM) to leverage efficient scientific data analysis by guided statistics.

Journal of cheminformatics·2026
Same journal

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026
Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026
Same journal

Sampling out-of-distribution chemical spaces via Bayesian flow.

Journal of cheminformatics·2026
Same journal

Hold on tight: the kinetic profiling of opioid receptor ligands using the CORAL-MD.

Journal of cheminformatics·2026
Same journal

Transformer-accelerated discovery of inhibitors targeting the RpsA<sub>Δ438</sub> deletion in PZA-resistant tuberculosis.

Journal of cheminformatics·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

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

Published on: January 15, 2022

4.0K

关于"推进材料属性预测:使用基于物理的机器学习模型来确定粘度"的评论

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
概括
此摘要是机器生成的。

当数据稀缺时, 基于物理的机器学习可以改善粘度预测. 整合分子动力学描述器,如蒸发热, 提高纯液体的准确性.

科学领域:

  • 物理化学
  • 材料科学
  • 计算化学

背景情况:

  • 数据驱动的机器学习 (ML) 面临着有限数据的属性预测挑战.
  • 整合基于物理的模型与机器学习可以提高预测性能.
  • 和其他人. 在定量结构-属性关系 (QSPR) 中使用分子动力学 (MD) 描述器来预测液体粘度.
关键词:
耳环率理论基于物理学的神经网络粘度 粘度 粘度

更多相关视频

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

1.1K
Experimental Multiscale Methodology for Predicting Material Fouling Resistance
09:13

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

1.5K

相关实验视频

Last Updated: Sep 9, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

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

Published on: January 15, 2022

4.0K
Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

1.1K
Experimental Multiscale Methodology for Predicting Material Fouling Resistance
09:13

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

1.5K