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相关概念视频

Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

257
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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Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

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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...
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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.
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Laminar Flow01:27

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Laminar flow represents a smooth, orderly fluid motion where particles move along parallel paths, resulting in minimal mixing between layers. Streamlined particle paths characterize this flow regime and occur under conditions where viscous forces dominate over inertial forces. The distinction between laminar, transitional, and turbulent flow is primarily determined by the Reynolds number, a dimensionless quantity calculated as:
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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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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
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学习通过图形神经网络模拟气溶动力学.

Fabiana Ferracina1,2, Payton Beeler1, Mahantesh Halappanavar3

  • 1Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.

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概括

我们开发了基于图形的气溶动力学学习 (GLAD),以加快气候模型的速度. 这种机器学习方法准确地模拟了气溶颗粒的行为,改善了气候和空气质量预测.

关键词:
气溶化学动力学基于图形的学习学习.机器学习是机器学习.神经网络的神经网络的神经网络基于粒子的方法.粒子解析的模拟模拟

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科学领域:

  • 大气科学 大气科学
  • 计算化学的计算化学
  • 机器学习 机器学习

背景情况:

  • 气溶颗粒显著影响气候,天气和空气质量.
  • 准确建模多样化和不断演变的粒子特性需要计算密集的粒子解析模型.
  • 现有的模型在捕获详细的气溶动态的计算成本方面扎.

研究的目的:

  • 为了加速计算上昂贵的颗粒分辨率的气溶模型.
  • 引入一种新的机器学习框架,即基于图形的气溶动力学学习 (GLAD),用于气溶模拟.
  • 训练一个替代模型来预测气溶的微物理和化学.

主要方法:

  • 实现了一个基于图形网络的模拟器 (GNS),其中粒子是图形节点.
  • 使用PartMC-MOSAIC粒子解析模型的输出训练了一个图形神经网络 (GNN).
  • 模拟的气溶动力学,包括硫酸对混合成分颗粒的凝结.

主要成果:

  • 经过训练的GNN准确地学习了化学动力学,并在不同的场景中进行了概括.
  • 与传统模型相比,实现了高效的训练和预测时间.
  • 证明了GLAD框架的稳定性和适应性.

结论:

  • GLAD提供了一种高效准确的模拟气溶动态的方法.
  • 这种机器学习方法可以显著加速气候和空气质量建模.
  • 该框架显示,它有望促进我们对气溶微物理和化学的理解.