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相关概念视频

Surface Tension, Capillary Action, and Viscosity02:57

Surface Tension, Capillary Action, and Viscosity

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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...
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Design Example: Deciding Thickness of Lubricating Fluid in a Shaft01:23

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Effective lubrication between a rotating shaft and its bearing housing is essential in rotating machinery to minimize friction, wear, and energy loss. With carefully controlled thickness and viscosity, the lubricant layer prevents metal-to-metal contact, ensuring smooth operation.
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Viscosity01:17

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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.
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Correlation of Experimental Data01:23

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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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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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小数据集的基于相似性的机器学习:预测生物滑剂基油粘度

Jae Young Kim1,2, Salman A Khan2, Dionisios G Vlachos1,2

  • 1Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.

The journal of physical chemistry. B
|November 23, 2024
PubMed
概括

这项研究引入了一种基于相似性的新型机器学习方法,用于在有限的实验数据下精确地预测分子性质. 该方法提高了预测准确度,并且需要更少的功能,超过了传统技术.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在化学中的应用.

背景情况:

  • 实验性化学数据往往很少,而且获得起来也很昂贵.
  • 传统的机器学习方法在有限的数据集上扎.
  • 对分子性质的准确预测对于化学研究和开发至关重要.

研究的目的:

  • 开发一种机器学习方法,使用小数据集进行准确的分子性质预测.
  • 与现有方法相比,提高预测准确度和减少特征要求.
  • 为了应对化学应用中有限的实验数据的挑战.

主要方法:

  • 开发了一种基于相似性的机器学习方法.
  • 分子被根据结构相似性使用分子指纹分组.
  • 单独的机器学习模型被训练为每个分子组.
  • 该方法在动态粘度和水溶性数据集上得到了验证.

主要成果:

  • 拟议的方法在较大的数据集上表现出与传统方法相比或优于传统方法的性能,使用的功能较少.
  • 与转移学习和随机森林相比,在预测生物滑基油 (KV40) 的动态粘度方面观察到显著的性能改善.
  • 这种方法在有限的数据场景中被证明是可靠的.

结论:

  • 基于相似性的机器学习方法提供了一个强大的框架,可以在有限的数据中准确地预测分子性质.
  • 当数据集中存在明确的结构模式时,这种方法特别有效.
  • 该方法可以对各种分子数据集进行概括,克服数据稀缺的挑战.