Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

General Characteristics of Pipe Flow I01:22

General Characteristics of Pipe Flow I

599
Pipe flow refers to the movement of fluids within fully enclosed conduits, typically cylindrical in shape, such as water pipes or hydraulic hoses. These conduits are designed to withstand high-pressure gradients that drive fluid movement, contrasting with open-channel flows, where gravity is the primary driving force. Rectangular conduits, like air conditioning and heating ducts, generally operate at lower pressures and are less suited for high-pressure applications.
The classification of fluid...
599
Plane Potential Flows01:23

Plane Potential Flows

284
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...
284
Turbulent Flow01:24

Turbulent Flow

108
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...
108
General Characteristics of Pipe Flow II01:24

General Characteristics of Pipe Flow II

556
When fluid enters a pipe, it first passes through the entrance region, where the velocity profile adjusts due to viscous effects. In this region, a boundary layer forms along the pipe walls and grows until it fully occupies the pipe's cross-section. Once the boundary layer merges, the flow becomes fully developed, with a steady velocity profile that remains consistent along the pipe's length.
The distance to reach a fully developed flow is called the entrance length and depends on the...
556
Single Pipe Systems01:24

Single Pipe Systems

77
In pipe flow analysis, problems are typically categorized into three types — Type I, Type II, and Type III — based on the known parameters and the desired outcome. Each type of problem addresses specific engineering requirements using fluid properties, pipe characteristics, and operational conditions.
In a Type I problem, fluid properties (density and viscosity), pipe characteristics (including diameter, length, and surface roughness), and the flow rate or average velocity are...
77
Multiple Pipe Systems01:21

Multiple Pipe Systems

333
Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
333

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Author Correction: Real-time monitoring of water states in large-diameter aqueducts - learning from distributed acoustic sensing signals.

Communications engineering·2025
Same author

Real-time monitoring of water states in large-diameter aqueducts - learning from distributed acoustic sensing signals.

Communications engineering·2025
Same author

Response time of global deltas to changes in fluvial sediment supply.

Nature communications·2025
Same author

Uncertainty quantification of the pressure waveform using a Windkessel model.

International journal for numerical methods in biomedical engineering·2024
Same author

Water exchange capability induced by seasonal and regional variability: Assessment of Hong Kong waters.

Marine pollution bulletin·2024
Same author

Signatures of obstructions and expansions in the arterial frequency response.

Computer methods and programs in biomedicine·2023
Same journal

The overlooked risk of horizontal transfer of plasmid-borne antibiotic resistance genes induced by organophosphate esters in aquaculture environments.

Water research·2026
Same journal

Coastal saltmarshes as nature-based solutions for pesticide mitigation through groundwater-surface water interactions.

Water research·2026
Same journal

Coupled geochemical profiling and metagenomics reveal controls on phosphine preservation and emission in a eutrophic Estuary.

Water research·2026
Same journal

Enabling smart decentralized constructed wetlands for greywater reuse with an attention-enhanced ensemble model: from nutrient treatment optimization to process-informed modeling.

Water research·2026
Same journal

Patterns and mechanisms of cross-media antimicrobial resistance development in a typical reclaimed water-receiving urban river.

Water research·2026
Same journal

Development of an electronic nose to characterize geosmin and 2-methylisoborneol of water collected from different phases in water treatment plants.

Water research·2026
查看所有相关文章

相关实验视频

Updated: May 22, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
00:09

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

5.5K

以光谱物理为基础的神经网络用于暂时管道流量模拟.

Vincent Tjuatja1, Alireza Keramat1, Mostafa Rahmanshahi1

  • 1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, PR China.

Water research
|March 12, 2025
PubMed
概括
此摘要是机器生成的。

物理信息神经网络 (PINNs) 适用于管道中的频域波分析. 新的物理信息复杂估值神经网络 (PICVNN) 模型改善了短暂压力预测和异常检测.

关键词:
频域建模频率领域建模液压过渡的过渡时间基于物理学的神经网络.水是一个水.

更多相关视频

Echo Particle Image Velocimetry
16:31

Echo Particle Image Velocimetry

Published on: December 27, 2012

14.6K
The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

8.4K

相关实验视频

Last Updated: May 22, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
00:09

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

5.5K
Echo Particle Image Velocimetry
16:31

Echo Particle Image Velocimetry

Published on: December 27, 2012

14.6K
The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

8.4K

科学领域:

  • 流体动力学 流体动力学
  • 计算式水力学是指计算式水力学
  • 在工程领域的机器学习.

背景情况:

  • 精确的波浪传播建模对于供水管道监测和定位至关重要.
  • 基于物理学的神经网络 (PINNs) 将物理定律与数据整合在一起,但通常仅限于时间域的短暂波分析.
  • 频域模型为系统识别和管道异常检测提供了更高的灵敏度.

研究的目的:

  • 开发一种新的物理信息神经网络 (PINN) 模型,用于频率领域的短暂波传播.
  • 为了提高管道监测和评估应用的波浪预测准确度.
  • 调查模型在处理不确定性和检测泄漏等异常方面的能力.

主要方法:

  • 开发一个物理信息复杂估值神经网络 (PICVNN) 用于频域水建模.
  • 整合物理原理与复杂值的神经网络来分析短暂波数据.
  • 与具有不同观察点的经典复杂值神经网络 (CVNN) 基准进行比较分析.

主要成果:

  • 该PICVNN模型准确地重建了短暂压力,在预测准确性方面超过了经典CVNN模型.
  • 该模型在处理输入参数的不确定性,数学模型和识别未知的泄漏方面表现出强度.
  • 与传统的CVNN相比,PICVNN实现了更高的准确性,但需要更长的培训时间.

结论:

  • 开发的PICVNN是用于管道频域短暂波分析的有效工具.
  • PICVNN作为一种可靠的信号融合方法,通过整合各种传感器数据来提高准确性和可靠性.
  • 这种方法促进了PINNs在管道监测和系统识别中的应用.