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相关概念视频

Turbulent Flow01:24

Turbulent Flow

125
Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent...
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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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Laminar Flow01:27

Laminar Flow

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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:
574
Rapidly Varying Flow01:24

Rapidly Varying Flow

48
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...
48
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

107
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.
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相关实验视频

Updated: May 29, 2025

Induction of Microstreaming by Nonspherical Bubble Oscillations in an Acoustic Levitation System
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深度强化学习用于在流分离泡中的主动流量控制.

Bernat Font1,2, Francisco Alcántara-Ávila3, Jean Rabault4

  • 1Faculty of Mechanical Engineering, Delft University of Technology, Delft, Netherlands. b.font@tudelft.nl.

Nature communications
|February 6, 2025
PubMed
概括

深度强化学习 (DRL) 有效地控制了流分离气泡,将面积减少了9.0%. 这种先进的DRL策略优于传统方法,为复杂的流体动力学提供更流,更有效的流量控制.

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科学领域:

  • 流体动力学 流体动力学
  • 计算流体动力学的流体动力学.
  • 机器学习 机器学习

背景情况:

  • 流分离气泡 (TSB) 在流体动力学中提出了重大挑战.
  • 经典的周期强迫为TSB提供了有限的控制效果.

研究的目的:

  • 在数字上评估深度强化学习 (DRL) 对TSB的经典周期强迫的控制效率.
  • 调查在粗网上训练的DRL控制策略应用于细网的可行性,从而降低计算成本.

主要方法:

  • 一个流分离气泡的数值模拟.
  • 深度强化学习 (DRL) 控制与定期强迫的比较.
  • 评估DRL战略的可转移性,从粗到精细的计算网格.
  • 通过DRL控制诱导的流体物理和旋动态的分析.

主要成果:

  • 基于DRL的控制减少了TSB面积的9.0%,超过定期控制的6.8%的减少.
  • 在粗网上训练的DRL策略有效地控制了在细网上的流量.
  • DRL提供了更顺的控制和瞬间的动量保存.
  • 在多种频率范围内,DRL引发了大规模的反旋转.

结论:

  • 与周期强迫相比,DRL为流分离气泡提供了更高的控制效率.
  • 粗网DRL训练是一种可行的方法,可以减少流控制中的计算费用.
  • 开发的开源CFD和DRL框架支持用于高级流体动力学研究的超大规模计算.