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Related Concept Videos

Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

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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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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
554

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Updated: Feb 24, 2026

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Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

Roos Sophia de Freitas Dam1, William Luna Salgado1, Eddie Jesús Avilán Puertas1

  • 1Instituto de Engenharia Nuclear, Divisão de Radiofármacos (IEN / DIRAD), Rua Hélio de Almeida, 75, Cidade Universitária, RJ, 21941-906, Brazil.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|February 22, 2026
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Summary

This study introduces a non-invasive radioactive particle tracking method for precise oil flow rate measurement in pipelines. The technique accurately predicts fluid fractions and velocities, minimizing operational costs and system downtime.

Keywords:
Deep neural networksFlow rateGamma densitometryMCNP6 codeSuperficial velocityVolume fraction

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Area of Science:

  • Nuclear Engineering
  • Petroleum Engineering
  • Computational Fluid Dynamics

Background:

  • Accurate flow rate determination is vital in the oil industry for operational efficiency.
  • Conventional flowmeters often require direct fluid contact, leading to maintenance issues and increased costs.
  • Non-intrusive methods are needed to overcome limitations of traditional flowmeters.

Purpose of the Study:

  • To propose a minimally intrusive methodology for flow rate determination in two-phase systems.
  • To predict fluid volume fractions and calculate superficial velocities using radioactive particle tracking.
  • To reduce operational costs and system shutdowns associated with conventional flowmeters.

Main Methods:

  • Utilized radioactive particle tracking with a sealed Cesium-137 source.
  • Simulated a stratified saltwater-oil flow in a PVC pipe using MCNP6 Monte Carlo code.
  • Employed deep neural networks for volume fraction prediction and cross-correlation for velocity calculation.

Main Results:

  • Achieved accurate prediction of fluid volume fractions.
  • Calculated superficial velocities using time delays from cross-correlation of oil-phase signals.
  • Attained a maximum mean absolute percentage error (MAPE) of 2.24% for oil flow rate compared to theoretical values.

Conclusions:

  • The radioactive particle tracking technique offers a viable, minimally intrusive solution for oil flow rate determination.
  • Deep neural networks and cross-correlation effectively processed the radioactive signals for accurate flow analysis.
  • The proposed method demonstrates high accuracy and potential for reducing costs in the oil industry.