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Related Concept Videos

General Characteristics of Pipe Flow I01:22

General Characteristics of Pipe Flow I

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...
Single Pipe Systems01:24

Single Pipe Systems

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 known. The...
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,...
Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is achieved...

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Non-Invasive Detection of Lead Connection Pipe with Machine Learning Model.

Ayomide Zul Kazeem1, Xiong Bill Yu2

  • 1Department of Civil and Environmental Engineering, Case Western Reserve University, 2104 Adelbert Road, Bingham 275, Cleveland, Ohio 44106-7201, United States.

ACS ES&T Water
|February 20, 2026
PubMed
Summary

Accurate lead pipe detection is crucial for infrastructure upgrades. A new noninvasive method uses physics-based modeling and machine learning to identify lead pipes with 99.9% accuracy, offering a cost-effective solution.

Keywords:
K-nearest neighborclassificationfinite element modellead pipemachine learning

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Area of Science:

  • Environmental Engineering
  • Data Science
  • Materials Science

Background:

  • The U.S. Environmental Protection Agency mandates lead service line replacement within 10 years.
  • Accurate identification of buried lead pipes is a significant challenge for water utilities.
  • Current detection methods are often costly, disruptive, or unreliable due to environmental interference.

Purpose of the Study:

  • To develop a noninvasive, efficient, and cost-effective method for detecting buried lead pipes.
  • To integrate physics-based modeling with machine learning for improved pipe material classification.
  • To provide a scalable solution for utilities to comply with regulatory requirements.

Main Methods:

  • Developed a physics-based finite element analysis (FEA) surrogate model to simulate the dynamic behavior of buried pipes.
  • Incorporated realistic loading conditions, such as stop-valve openings, into the FEA model.
  • Generated over 13,000 synthetic acceleration signals, simulating real-world noise and signal limitations.
  • Trained seven machine learning (ML) models, including K-nearest neighbor (KNN) and Extreme Gradient Boosting (XGBoost), on the simulated data.

Main Results:

  • The integrated FEA-ML framework achieved high accuracy in classifying pipe materials.
  • K-nearest neighbor (KNN) and Extreme Gradient Boosting (XGBoost) models demonstrated 99.9% classification accuracy.
  • The method proved effective in identifying pipe acceleration under various conditions and noise levels.

Conclusions:

  • The developed noninvasive approach offers a scalable and cost-effective solution for lead pipe detection.
  • This framework enables utilities to efficiently locate and replace lead pipes, ensuring regulatory compliance.
  • The method minimizes operational disruptions and resource expenditure compared to traditional techniques.