Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

678
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.
678
Rapidly Varying Flow01:24

Rapidly Varying Flow

613
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
613
Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

968
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...
968
Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

1.6K
Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:
1.6K
Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

2.0K
A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
The first step is to compute the cross-sectional areas of the pipe and the Venturi throat to analyze the pressure difference indicated by the pressure gauge. Next, the continuity equation is...
2.0K
Velocity Potential01:20

Velocity Potential

812
In steady, incompressible flow through a long, straight pipe with a uniform cross-section, the flow in the central region (far from the pipe walls) is irrotational. This irrotational nature means that fluid particles do not rotate around their axes, and a scalar function called the velocity potential, represented by ϕ, can be used to describe their movement. In irrotational flows, the velocity field V is defined as the gradient of the velocity potential:
812

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamic prediction of large spherical and cylindrical microplastic deposition: a machine learning approach for transport and deposition.

Environmental science and pollution research international·2025
Same author

Future soil erosion trends in Canadian agricultural lands from runoff and sustainability impacts.

Scientific reports·2025
Same author

Multitemporal river flow discharge prediction: A new framework for integrated environmental management and flood control.

Journal of environmental management·2025
Same author

Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment.

Chemosphere·2024
Same author

Developing environmental, social and governance (ESG) strategies on evaluation of municipal waste disposal centers: A case of Mexico.

Chemosphere·2024
Same author

Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers.

BMC medicine·2022

Related Experiment Video

Updated: Mar 21, 2026

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

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

Published on: August 26, 2019

6.2K

A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

Isa Ebtehaj1, Hossein Bonakdari1

  • 1Department of Civil Engineering, Razi University, Kermanshah, Iran E-mail bonakdari@yahoo.com.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|May 6, 2016
PubMed
Summary

This study introduces a novel method using support vector regression and the firefly algorithm to predict the minimum velocity needed to prevent sediment deposition in sewer pipes. The combined approach accurately determines the densimetric Froude number, ensuring optimal pipe design.

More Related Videos

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

1.2K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.7K

Related Experiment Videos

Last Updated: Mar 21, 2026

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

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

Published on: August 26, 2019

6.2K
Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

1.2K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.7K

Area of Science:

  • Civil Engineering
  • Environmental Engineering
  • Computational Fluid Dynamics

Background:

  • Sediment transport in sewer pipes is critical for system design and maintenance.
  • Preventing sediment deposition requires understanding the minimum velocity needed to maintain transport.
  • Existing methods for predicting this velocity may lack accuracy or efficiency.

Purpose of the Study:

  • To develop and validate a novel hybrid model for predicting the minimum velocity required to avoid sediment settling in pipe channels.
  • To express this minimum velocity as the densimetric Froude number (Fr).
  • To optimize the performance of support vector regression (SVR) models using the firefly algorithm (FFA).

Main Methods:

  • A hybrid model combining Support Vector Regression (SVR) and the Firefly Algorithm (FFA) was developed.
  • The Firefly Algorithm (FFA) was employed to optimize the parameters of the Support Vector Machine (SVM).
  • Dimensional analysis was used to identify key parameters influencing the densimetric Froude number (Fr), including sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)), and overall sediment friction factor (λ(s)).

Main Results:

  • The SVR-FFA model demonstrated superior performance in predicting the densimetric Froude number (Fr).
  • The model achieved a mean absolute percentage error (MAPE) of 2.123% and a root mean square error (RMSE) of 0.116.
  • Performance was benchmarked against genetic programming, artificial neural networks, and existing regression-based equations, with SVR-FFA showing significantly better results.

Conclusions:

  • The proposed SVR-FFA model offers a highly accurate and efficient method for predicting the minimum velocity required to prevent sediment deposition in sewer pipes.
  • This approach provides a valuable tool for optimizing sewer pipe design and management.
  • The study highlights the effectiveness of hybrid machine learning algorithms in addressing complex environmental engineering challenges.