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Turbulent Flow

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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,...

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Related Experiment Video

Updated: May 22, 2026

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Simultaneous video stabilization and moving object detection in turbulence.

Omar Oreifej¹, Xin Li, Mubarak Shah

¹University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA. oreifej@eecs.ucf.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 25, 2012

Summary

This summary is machine-generated.

This study introduces a new method for stabilizing videos affected by optical turbulence while simultaneously detecting moving objects. The approach effectively separates background, turbulence, and object components, preserving details of interest.

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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

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Last Updated: May 22, 2026

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Optical turbulence causes video deformations, challenging traditional stabilization methods.
Existing techniques like averaging or dewarping can distort important moving objects.

Purpose of the Study:

To develop a novel method for simultaneous turbulence mitigation and moving object detection.
To overcome limitations of current methods that distort moving objects.

Main Methods:

A three-term low-rank matrix decomposition is proposed.
The method decomposes video sequences into background, turbulence, and object components.
Minimization of nuclear norm, Frobenius norm, and l1 norm is employed.

Main Results:

The approach effectively mitigates turbulence while preserving moving objects.
Demonstrated robustness on challenging sequences with significant atmospheric distortion.
Successfully detected extremely tiny moving objects within turbulent videos.

Conclusions:

The novel matrix decomposition method offers a robust solution for simultaneous turbulence mitigation and object detection.
This technique enhances video analysis in the presence of atmospheric turbulence.
Preserves critical details of moving objects, valuable for various applications.