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相关概念视频

Gradually Varying Flow01:29

Gradually Varying Flow

37
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
37
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

59
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
59
Rapidly Varying Flow01:24

Rapidly Varying Flow

56
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
56
Couette Flow01:22

Couette Flow

233
Couette flow represents the flow of fluid between two parallel plates, with one plate fixed and the other moving with a constant velocity. This configuration allows for a simplified analysis using the Navier-Stokes equations, which govern fluid motion under conditions of viscosity and incompressibility. For Couette flow, the assumptions include a steady, laminar, incompressible flow with a zero-pressure gradient in the flow direction. This flow type is beneficial for understanding shear-driven...
233
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

137
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
137
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

107
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
107

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Updated: Jun 14, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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粗粒度分子动力学与规范化流动

Samuel Tamagnone1, Alessandro Laio1,2, Marylou Gabrié3

  • 1International School for Advanced Studies (SISSA), Via Bonomea 265, Trieste 34136, Italy.

Journal of chemical theory and computation
|September 3, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一个新的采样算法,使用规范流和不平衡动力学来进行高效的分子模拟. 这种方法可以快速探索复杂的能源景观,并生成热化配置.

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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科学领域:

  • 计算化学的计算化学
  • 统计力学 统计力学
  • 机器学习 机器学习

背景情况:

  • 分子模拟对于理解复杂系统至关重要.
  • 采样具有高自由能源障碍的复杂能源景观仍然是一个挑战.
  • 现有的方法难以有效地探索元稳定状态.

研究的目的:

  • 引入一种新的采样算法,利用规范化流和不平衡动态.
  • 为了使高维系统的有效探索和克服能源障碍.
  • 为了生成热化配置和自由能源景观.

主要方法:

  • 提出了一个带有非本地更新的马尔科夫链蒙特卡洛算法.
  • 规范化流量模型是一个中等尺寸的集体变量 (CV).
  • 不平衡动态被用来根据CV更新提出完全的配置移动.
  • 该流被训练来复制自由能源的景观.

主要成果:

  • 该算法成功地取样了热化配置.
  • 它展示了跨越能源障碍和超稳定状态的高效勘探.
  • 证明了对具有高自由能障碍的溶液聚合物系统的成功应用.

结论:

  • 拟议的算法为分子模拟提供了一种高效的方法.
  • 它有效地处理具有复杂能源景观和超稳定状态的系统.
  • 训练的正常化流可以重复用于生成配置.