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Related Concept Videos

Turbulent Flow01:24

Turbulent Flow

Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent spots,...
Laminar and Turbulent Flow01:07

Laminar and Turbulent Flow

Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the streamlines...
Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

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:
Bernoulli's Equation00:59

Bernoulli's Equation

In the middle of the nineteenth century, it was observed that two trains passing each other at a high relative speed get pulled towards each other. The same occurs when two cars pass each other at a high relative speed. The reason is that the fluid pressure drops in the region where the fluid speeds up. As the air between the trains or the cars increases in speed, its pressure reduces. The pressure on the outer parts of the vehicles is still the atmospheric pressure, while the resultant...
Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

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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Bernoulli's Principle01:01

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Bernoulli's equation incorporates how fluid pressure changes across a static, incompressible fluid by equating the kinetic energy contribution to zero. It is also helpful in analyzing horizontal flows in which the gravitational energy density is constant throughout. The latter equation is so useful that it is called Bernoulli's principle. According to Bernoulli's principle, the fluid pressure drops if the speed increases and vice versa.
Bernoulli's principle has several applications. It is used...

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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Bayesian estimation of turbulent motion.

Patrick Héas1, Cédric Herzet, Etienne Mémin

  • 1Inria, Rennes Bretagne-Atlantique, F-35042 Rennes, France. Patrick.Heas@inria.fr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel regularizer for fluid motion estimation using image sequences. The method improves accuracy by leveraging physical laws and Bayesian inference for turbulent flow analysis.

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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Published on: February 22, 2018

Area of Science:

  • Fluid dynamics
  • Image processing
  • Computational physics

Background:

  • Estimating fluid motion from image sequences is crucial for understanding turbulent flows.
  • Existing methods often struggle with the multiscale nature of turbulence.
  • Physical laws governing turbulent flows offer potential for improved motion estimation.

Purpose of the Study:

  • To propose a novel regularizer for fluid motion estimation based on physical laws of turbulence.
  • To develop a Bayesian framework for jointly estimating motion, hyperparameters, and physical priors.
  • To enhance the accuracy of fluid motion estimation from image sequences.

Main Methods:

  • Imposing scale invariance between histograms of motion increments at different scales.
  • Reformulating the problem from a Bayesian perspective.
  • Jointly estimating motion, regularization hyperparameters, and selecting physical priors via posterior maximization.

Main Results:

  • The proposed Bayesian estimator was assessed on synthetic and real turbulent fluid flow image sequences.
  • Results demonstrate superior performance compared to existing state-of-the-art methods.
  • Accurate estimation of fluid motion, hyperparameters, and physical priors was achieved.

Conclusions:

  • The developed regularizer effectively captures the multiscale structure of turbulent flows.
  • The Bayesian approach provides a robust framework for fluid motion estimation.
  • This work advances the state-of-the-art in analyzing turbulent fluid dynamics from image data.