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相关概念视频

Density, Specific Weight, Specific Gravity and Compressibility of Fluid01:27

Density, Specific Weight, Specific Gravity and Compressibility of Fluid

Density, specific weight, specific gravity, and compressibility are fundamental properties of fluids. Density is the mass per unit volume, characterizing the mass of a fluid system. It influences buoyancy, pressure, flow dynamics, viscosity, thermal conductivity, and sound propagation. For instance, in pipeline design, accurate density measurements ensure that the pipeline can handle the fluid's mass.
Specific weight represents the weight per unit volume and is calculated by multiplying density...
Plane Potential Flows01:23

Plane Potential Flows

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 Flow
Uniform flow...
Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
The orifice meter is a simple,...

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相关实验视频

Updated: Jun 19, 2026

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
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基于声波排放的管道泄漏检测和尺寸识别,使用定制的一维密度网.

Faisal Saleem1, Zahoor Ahmad1, Muhammad Farooq Siddique1

  • 1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
概括

本研究介绍了一种先进的声学排放 (AE) 管道监测系统,使用实证波纹转换 (EWT) 和DenseNet深度学习来准确检测泄漏和大小分类,显著提高工业安全.

关键词:
在DenseNet中,使用的是DenseNet.声学排放的声音排放.深度学习是一种深度学习.经验波形变换 经验波形变换一个单一的维度.管道泄漏情况 管道泄漏情况

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科学领域:

  • 工程 工程师 工程师 工程师
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 工业管道的完整性依赖于有效的泄漏检测.
  • 传统方法在噪声灵敏度,适应性和计算成本方面的局限性.
  • 实时监控需要强大而高效的泄漏识别解决方案.

研究的目的:

  • 为精确的管道泄漏检测和尺寸分类开发一种基于声辐射 (AE) 的新方法.
  • 克服传统监测技术的局限性.
  • 提高工业管道的安全性和寿命.

主要方法:

  • 利用经验波形变换 (EWT) 进行自适应频率分解和信号细分.
  • 应用适应性值和无声化来提高信号质量.
  • 采用定制的一维DenseNet深度学习模型进行特征提取和分类.

主要成果:

  • 在真实世界AE数据上实现了99.76%的特殊泄漏检测准确度.
  • 证明了正常运行和各种泄漏严重程度之间的可靠区分.
  • 在各种环境中展示了降低计算成本的强大性能.

结论:

  • 拟议的基于AE的方法与EWT和DenseNet提供了一个非常准确和高效的管道泄漏监控解决方案.
  • 这种方法通过准确的泄漏检测和严重程度分类来提高运营安全和完整性.
  • 该技术可以适应各种操作条件,比传统方法有了显著的进步.