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相关概念视频

Turbulent Flow01:24

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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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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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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Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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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.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
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相关实验视频

Updated: Jan 9, 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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基于事件的摄像机建模用于大气流预测.

Dor Mizrahi1,2, Daniel Brisk1, Yogev Mordechai1

  • 1Applied Physics Division, Soreq Nuclear Research Center, Yavne 81800, Israel.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
概括
此摘要是机器生成的。

被动的神经形态事件摄像头可以在没有活跃的发射器的情况下估计大气动荡 (Cn2). 这项技术为实时大气监测提供了一个紧的,低功耗的替代方案.

关键词:
大气中的光学流.基于事件的视觉传感器.机器学习回归回归神经形态相机的神经形态相机路径集成的C n 2估计.

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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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相关实验视频

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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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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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科学领域:

  • 大气物理和光学.
  • 神经形态工程的神经形态工程
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 大气动荡对光学系统和远程成像产生重大影响.
  • 传统的流测量工具 (闪电计) 是重的,需要活跃的组件和精确的对齐.
  • 需要被动,紧,低功耗的大气监测解决方案.

研究的目的:

  • 评估被动神经形态事件摄像机在估计大气流中的有效性.
  • 使用事件流数据来确定折射率结构参数 (Cn2).
  • 为了比较事件摄像机的性能与地面真相闪仪.

主要方法:

  • 在300米的路径上进行实地实验,使用事件摄像头和CMOS摄像头,用闪光计作为地面真相.
  • 从事件流数据中提取19个统计特征,使用不同的整合时间 (2-50秒).
  • 训练机器学习回归模型 (例如,XGBoost) 来从提取的特征中预测Cn2.

主要成果:

  • 最好的模型 (XGBoost) 实现了高的皮尔森相关性 (0.93) 和平均绝对相对误差的35%在一个广泛的流范围 (10^-14到10^-12m^-2/3).
  • 精度随着更长的整合时间和目标对比度更高的地区而提高.
  • 量化了整合时间,目标对比度和特征稳定性对现场条件下估计准确性的影响.

结论:

  • 被动神经形态事件摄像头可以在没有主动照明的情况下可靠地估计大气动荡 (Cn2).
  • 这项技术是传统闪电仪的可行替代品,可实现紧且低功耗的实时大气监测.
  • 事件驱动传感显示了先进环境监测应用的潜力.