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相关概念视频

Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

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Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
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Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

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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...
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Laminar and Turbulent Flow01:07

Laminar and Turbulent Flow

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Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the...
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Navier–Stokes Equations01:28

Navier–Stokes Equations

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For incompressible Newtonian fluids, where density remains constant, stresses show a linear relationship with the deformation rate, defined by normal and shear stresses. Normal stresses depend on the pressure exerted on the fluid and the rate of deformation in specific directions, which determines how fluid flows under varying pressures. Shear stresses, on the other hand, act tangentially across fluid layers. They explain how adjacent fluid layers slide relative to one another, connecting...
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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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Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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相关实验视频

Updated: Jul 19, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
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Image-based Lagrangian Particle Tracking in Bed-load Experiments

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通过基于物理的机器学习进行拉格朗日大模拟.

Yifeng Tian1, Michael Woodward2,3, Mikhail Stepanov2

  • 1Information Sciences Group, Computer, Computational and Statistical Sciences Division (CCS-3), Los Alamos National Laboratory, Los Alamos, NM 87545.

Proceedings of the National Academy of Sciences of the United States of America
|August 16, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了拉格朗的Large Eddy模拟 (L-LES),一种新的方法,利用拉格朗粒子和机器学习来建模流体流. L-LES准确地复制了流统计和结构,为传统的欧勒尔方法提供了替代方案.

关键词:
拉格兰治粒子是什么?大模拟大的模拟基于物理的机器学习.流模拟的流模型.

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

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相关实验视频

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科学领域:

  • 计算流体动力学的流体动力学.
  • 流建模 流建模
  • 机器学习应用 机器学习应用

背景情况:

  • 高雷诺德数的同质异构流 (HIT) 是由纳维埃-斯托克斯 (NS) 方程控制的,这些方程在计算上具有挑战性.
  • 传统的大模拟 (LES) 依赖于欧利尔速度场和关于子网尺度效应的假设.
  • 存在需要替代的流建模方法,以捕捉欧勒和拉格朗的流动特征.

研究的目的:

  • 开发一种新的拉格朗日大模拟 (L-LES) 框架,用于模拟流.
  • 使用机器学习 (ML) 来训练和解决基于直接数值模拟 (DNS) 数据的L-LES方程.
  • 整合基于物理的参数化和神经网络,用于准确的流演变建模.

主要方法:

  • 开发了基于拉格朗日粒子动力学的L-LES启发式,将光滑粒子水力学概括起来.
  • 使用机器学习 (ML) 来训练使用NS-DNS的拉格朗基数数据的L-LES模型.
  • 在可微分编程框架内,整合了基于物理的参数化和神经网络.
  • 利用各种损失函数,包括基于物理的选项,以进行高效的模型训练.

主要成果:

  • 该L-LES模型成功地复制了欧勒尔和独特的拉格朗日流动结构和统计数据.
  • 该模型在一系列动荡的马赫数中表现出了能力.
  • 基于物理的ML训练促进了高效和准确的流演变预测.

结论:

  • 对于流模拟,L-LES提供了一个可行且准确的替代传统的欧勒尔 LES.
  • 拉格朗的方法与ML的整合为计算流体动力学提供了一个强大的新工具.
  • 这种基于物理学的ML方法推进了流中的子电网规模效应的建模.