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Related Concept Videos

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...
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Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

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In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
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Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

526
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:
526
Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

458
Bernoulli's equation for flow normal to a streamline explains how pressure varies across curved streamlines due to the outward centrifugal forces induced by the fluid's curvature. The pressure is higher on the inner side of the curve, near the center of curvature, and decreases outward to balance these centrifugal forces.
The pressure difference depends on the fluid's velocity and radius of curvature. The pressure variation is minimal in flows with nearly straight streamlines.
458
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

43
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps
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A robust pressure drop prediction model in vertical multiphase flow: a machine learning approach.

Fahd Saeed Alakbari1, Mohammed Abdalla Ayoub2, M A Awad3

  • 1Centre of Advanced Process Safety (CAPS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.

Scientific Reports
|April 18, 2025
PubMed
Summary

This study introduces a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for accurate multiphase flow pressure drop prediction in vertical wells. The ANFIS model significantly outperforms existing methods, enhancing operational efficiency and design accuracy.

Keywords:
Adaptive Neuro-Fuzzy inference system (ANFIS)Multiphase flowPressure drop in vertical wellsRobust pressure drop model.

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence in Energy

Background:

  • Accurate pressure drop prediction is vital for oil and gas production optimization.
  • Existing models often lack accuracy with diverse or outlier datasets.

Purpose of the Study:

  • To develop and validate a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for precise pressure drop prediction in vertical multiphase flow.
  • To demonstrate the superiority of the ANFIS model over conventional methods.

Main Methods:

  • Compiled a dataset of 335 experimental records covering a wide parameter range.
  • Developed an ANFIS model using key inputs: wellhead pressure, flow rates, diameter, temperature, and length.
  • Evaluated model performance using statistical metrics (AAPE, RMSE, R²), cross-plots, error distributions, Kruskal-Wallis test, and confidence intervals.

Main Results:

  • The ANFIS model achieved high accuracy with an Average Absolute Percentage Error (AAPE) of 2.92%, Root Mean Square Error (RMSE) of 1.9638%, and a coefficient of determination (R²) of 0.9645.
  • Statistical tests and error analyses confirmed the ANFIS model's superior predictive capability.
  • The ANFIS model outperformed commonly used published models in pressure drop prediction.

Conclusions:

  • The proposed ANFIS model is a reliable and advanced tool for predicting pressure drops in vertical wells with multiphase flow.
  • This improved accuracy leads to significant advancements in production design and operational efficiency.