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相关概念视频

Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

114
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
114
Multiple Pipe Systems01:21

Multiple Pipe Systems

346
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...
346
Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Major Losses in Pipes01:28

Major Losses in Pipes

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When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to...
532
Design Example: Designing a Residential Plumbing System01:25

Design Example: Designing a Residential Plumbing System

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The design of residential plumbing systems requires carefully evaluating water demand, flow rates, and pressure dynamics to ensure both efficiency and reliability. The nature of water flow within pipes is defined by its Reynolds number, which classifies flow as either laminar (smooth) or turbulent.
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相关实验视频

Updated: May 23, 2025

Surrogate Model Development for Digital Experiments in Welding
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可解释的深度学习模型用于预测水管故障.

Ridwan Taiwo1, Tarek Zayed2, Beenish Bakhtawar2

  • 1Department of Building and Real Estate, the Hong Kong Polytechnic University, Hung Hom, Hong Kong; Institute of Construction and Infrastructure Management, ETH Zurich, Stefano-Franscini-Platz 5, Zurich, Switzerland.

Journal of environmental management
|March 7, 2025
PubMed
概括

这项研究使用像CNN和TabNet这样的深度学习模型,使用贝叶斯优化进行优化,以预测水管泄漏和爆裂. 卷积神经网络 (CNN) 模型在预测水分网络故障方面被证明是最有效的.

关键词:
在美国,CNN是CNN.科普兰算法 科普兰算法深度学习是一种深度学习.爆破的可能性.泄漏的可能性.这就是 SHAP SHAP 的意思.在 TabNet TabNet 里面.水分网络的水分网络.水管的故障是水管的故障.

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科学领域:

  • 环境工程 环境工程
  • 水资源管理 水资源管理
  • 数据科学数据科学数据科学

背景情况:

  • 水分网络 (WDN) 的故障会造成严重的环境和经济损害.
  • 现有的管道故障预测模型缺乏对泄漏和爆破概率的关注.

研究的目的:

  • 开发和评估深度学习模型,用于预测WDN中泄漏和爆发的概率.
  • 通过超参数优化和数据扩展来增强模型性能.
  • 为影响管道故障预测因素提供可解释的见解.

主要方法:

  • 深度神经网络 (DNN),卷积神经网络 (CNN) 和TabNet的应用用于故障预测.
  • 使用贝叶斯优化 (BO) 的超参数优化.
  • 使用科普兰算法和夏普利添加式解释 (SHAP) 的模型解释.

主要成果:

  • 贝叶斯优化显著提高了模型的预测能力,TabNet在标准化数据上的泄漏预测F1得分增加了36.2%.
  • 科普兰算法确定CNN是泄漏和爆破概率预测的最佳模型.
  • 管道直径,材料和年龄被确定为通过SHAP值影响故障预测的关键特征.

结论:

  • 优化的深度学习模型,特别是CNN,为预测WDN管道故障提供了强大的方法.
  • 开发的模型为水利公司提供了可操作的见解,以改善网络管理和减轻故障.
  • 用户友好的Web应用程序可以实用,实时预测泄漏和爆发.