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相关概念视频

Steady, Laminar Flow in Circular Tubes01:23

Steady, Laminar Flow in Circular Tubes

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Hagen-Poiseuille flow describes a viscous fluid's steady, incompressible flow through a cylindrical tube with a constant radius R. This flow profile is often applied to understand fluid transport in narrow channels, such as capillaries. It serves as a foundational example of laminar flow. In this model, cylindrical coordinates (r,θ,z) are used to describe the radial (r), angular (θ), and axial (z) dimensions within the tube. For Hagen-Poiseuille flow, the velocity profile is purely axial,...
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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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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 enhance...
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Steady, Laminar Flow Between Parallel Plates01:17

Steady, Laminar Flow Between Parallel Plates

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Understanding steady, laminar flow between parallel plates is essential for analyzing and designing flow in narrow rectangular channels, commonly found in various water conveyance and drainage systems. The Navier-Stokes equations govern fluid motion and are generally challenging to solve due to their nonlinearity. However, simplifications are possible in certain cases, like the steady laminar flow between parallel plates. For this scenario, we assume steady, incompressible, laminar flow.
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Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

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A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
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Turbulent Flow

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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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相关实验视频

Updated: Jan 8, 2026

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

Published on: February 27, 2016

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卷积神经网络辅助的参数识别在圆形空气旋束中.

Bingsong Cao, Zhangrong Mei, Yonghua Mao

    Journal of the Optical Society of America. A, Optics, image science, and vision
    |December 18, 2025
    PubMed
    概括

    一个新的紧卷积神经网络 (CNN) 准确地识别了圆空气波束 (EAVB) 的参数. 这种机器学习方法提高了光通信能力和维度,以满足未来的高带宽需求.

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 机器学习应用 机器学习应用
    • 光学通信是指光学通信.

    背景情况:

    • 卷积神经网络 (CNN) 是图像识别的强大工具.
    • 圆形空气旋束 (EAVBs) 为轨道角动量 (OAM) 通信提供了扩展的维度.
    • 分类EAVB参数对于利用它们的通信潜力至关重要.

    研究的目的:

    • 开发一个紧的CNN架构来对EAVB的拓电荷 (m) 和圆参数 (t) 进行分类.
    • 训练和验证CNN使用物理增强数据集,结合模拟和实验数据.
    • 以未见的实验EAVB模式来评估网络的稳定性.

    主要方法:

    • 为EAVB参数分类量身定制的紧型CNN架构的开发.
    • 在一个数据集上训练CNN,并增加了基于物理的模拟和实验强度模式.
    • 在标准和未见的实验数据集上测试网络的准确性和概括能力.

    主要成果:

    • 在标准测试套件上,CNN在对EAVB参数 (m和t) 的分类中取得了超过99.80%的准确性.
    • 该网络在未见的实验模式上表现出高强度,准确度从98.24%到100%不等.
    • 所有未增强的新实验模式都被CNN准确地识别了.

    更多相关视频

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    Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
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    Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

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    Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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    结论:

    • 开发的紧型CNN非常有效地对EAVBs的关键参数进行分类.
    • 这种机器学习方法为基于EAVB的光通信铺平了道路,提高了容量和维度.
    • 这些发现支持将人工智能集成到光学系统中,以满足不断增长的带宽需求.