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Related Concept Videos

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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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Plane Potential Flows01:23

Plane Potential Flows

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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
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Rapidly Varying Flow01:24

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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...
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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.
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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

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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.
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Updated: Feb 25, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management.

Harun Ur Rashid1, Aleksandra Pachalieva2, Daniel O'Malley2

  • 1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. hrashid@lanl.gov.

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Summary

This study introduces a physics-informed machine learning model for subsurface reservoir pressure control. It significantly reduces the need for expensive simulations by using transfer learning, enabling practical and accurate predictions.

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Area of Science:

  • Geosciences
  • Computational Science
  • Machine Learning

Background:

  • Subsurface reservoir pressure control is complex due to geological heterogeneity and multiphase flow dynamics.
  • High-fidelity physics-based simulations are computationally expensive and often prohibitive for predicting reservoir behavior.
  • Uncertain and heterogeneous reservoir properties necessitate numerous simulations, posing a significant challenge.

Purpose of the Study:

  • To develop a computationally efficient and accurate method for subsurface reservoir pressure control.
  • To address the limitations of traditional physics-based simulations in handling complex reservoir dynamics.
  • To enable practical predictions for realistic injection-extraction scenarios.

Main Methods:

  • A physics-informed machine learning workflow coupling a differentiable multiphase flow simulator (DPFEHM framework) with a convolutional neural network (CNN).
  • The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits.
  • Transfer learning is employed, pretraining the model on less expensive single-phase, steady-state simulations before finetuning on multiphase scenarios.

Main Results:

  • The developed method achieves high-accuracy training with significantly fewer simulations (under three thousand) compared to previous estimates (up to ten million).
  • Incorporating transient multiphase flow physics enhances prediction accuracy for injection-extraction scenarios.
  • The workflow demonstrates practical and accurate predictions for complex subsurface flows.

Conclusions:

  • Physics-informed machine learning offers a computationally efficient alternative to traditional simulations for reservoir pressure control.
  • Transfer learning from simpler simulations drastically reduces the computational cost of training complex multiphase flow models.
  • This approach enables more accessible and accurate subsurface reservoir management.