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相关概念视频

Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

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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...
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Capillarity in Fluid01:19

Capillarity in Fluid

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Capillarity describes the movement of liquid in small spaces without external forces acting on it. The capillarity is driven by surface tension and adhesive interactions between the liquid and surrounding solid surfaces. This effect is often seen in narrow tubes, porous materials, and fine particles.
Surface tension is crucial to capillarity. It results from cohesive forces between liquid molecules at the liquid-air boundary, forming a skin that resists external forces. When the capillary tube...
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Accelerating Fluids01:17

Accelerating Fluids

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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:
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Distillation: Vapor–Liquid Equilibria01:01

Distillation: Vapor–Liquid Equilibria

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Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
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Vapor Pressure of Fluid01:28

Vapor Pressure of Fluid

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The vapor pressure of a fluid is a crucial concept in fluid mechanics, influencing phenomena such as boiling and cavitation. Vapor pressure refers to the pressure exerted by a vapor at a state of thermodynamic equilibrium with its corresponding liquid phase at a specific temperature. It represents the tendency of molecules to escape from the fluid surface into the vapor phase.
When a liquid is placed in a closed container with a small air space, and the space is evacuated, vapor molecules will...
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Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

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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...
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相关实验视频

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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数据驱动的方法,以粗粒简单的液体在限制的限制.

Ishan Nadkarni1, Haiyi Wu2, Narayana R Aluru1,2

  • 1Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.

Journal of chemical theory and computation
|October 4, 2023
PubMed
概括

我们使用深度神经网络 (DNN) 开发了一个数据驱动的框架,以确定局限空间中的液体的粗粒度 (CG) 列纳德-斯 (LJ) 潜在参数. 这种方法准确地预测了流体的行为,并增强了粗粒技术.

科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 统计力学就是统计力学.

背景情况:

  • 粗粒度 (CG) 方法简化了大规模模拟的复杂分子系统.
  • 精确的CG潜力对于模拟有限环境中的流体至关重要,例如纳米孔状材料.
  • 导出CG参数的传统方法可能是计算密集的,并且可能会与系统特定的效应作斗争.

研究的目的:

  • 开发一个数据驱动的框架来识别局限系统中的粗粒列纳德-斯 (LJ) 潜在参数.
  • 利用深度神经网络 (DNN) 来解决受限流体的逆流体状态问题 (ILST).
  • 为了提高液体在狭窄几何形状的粗粒模拟的准确性和效率.

主要方法:

  • 经过深度神经网络 (DNN) 的训练,可以对局限系统的逆液态 (ILST) 解决方案进行近似训练.
  • 转移学习被用来预测状通道中的多原子液体的单位LJ潜力.
  • 数据驱动的方法与使用相对缩 (RE) 最小化的自下而上的粗粒度方法相结合.

主要成果:

  • DNN模型准确地预测了局限性流体中的不均质效应.
  • 预测的LJ潜力复制了非静电相互作用的全原子 (AA) 系统的流体结构和分子力.

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  • DNN方法和RE最小化之间的协同作用显著提高了代RE方法的稳定性和融合.
  • 结论:

    • 拟议的数据驱动框架有效地识别了限制液体的CG-LJ参数.
    • 将DNN与既有粗粒度技术集成为分子模拟提供了一种强大的方法.
    • 这种方法提供了一种强大而高效的方法,可以在纳米尺度限制下建模复杂的流体行为.