Genetic algorithm tracking technique for particle image velocimetry and comparison with other tracking models
Abstract
Particle Image Velocimetry (PIV) is a nonintrusive measurement technique, which can be used to study the structure of various fluid flows. PIV is a very efficient measurement technique since it can obtain both qualitative and quantitative spatial information about the flow field being studied. This information can be further processed into information such as vorticity and pathlines. Other flow measurement techniques (Laser Doppler Velocimetry, Hot Wire Anemometry, etc...) only provide quantitative information at a single point. A study on the performance of the Sub-Grid Genetic Tracking Algorithm for use in Particle Image Velocimetry was performed. A comparison with other tracking routines as the Cross Correlation, Spring Model and Neural Network tracking techniques was conducted. All four algorithms were used to track with synthetic data, and the results are compared with those obtained from a Large Eddy simulation computational fluid dynamics program. The simulated vectors were compared with the results from the four tracking techniques, to determine the yield and reliability of each tracking algorithm.
- Authors:
-
- Texas A and M Univ., College Station, TX (United States). Dept. of Nuclear Engineering
- Publication Date:
- OSTI Identifier:
- 445549
- Report Number(s):
- CONF-961105-
ISBN 0-7918-1520-X; TRN: IM9713%%285
- Resource Type:
- Conference
- Resource Relation:
- Conference: 1996 international mechanical engineering congress and exhibition, Atlanta, GA (United States), 17-22 Nov 1996; Other Information: PBD: 1996; Related Information: Is Part Of ASME Heat Transfer Division: Proceedings. Volume 2: Heat transfer in turbulent flows; Fundamentals of convection heat transfer; Fundamentals of natural convection in laminar and turbulent flows; Natural circulation; HTD-Volume 333; Wroblewski, D. [ed.] [Boston Univ., MA (United States)]; Anand, N.K. [ed.] [Texas A and M Univ., College Station, TX (United States)]; Pletcher, R. [ed.] [Iowa State Univ., Ames, IA (United States)] [and others]; PB: 396 p.
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING NOT INCLUDED IN OTHER CATEGORIES; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; FLOW VISUALIZATION; ALGORITHMS; COMPARATIVE EVALUATIONS; IMAGE PROCESSING; VELOCIMETERS; PERFORMANCE
Citation Formats
Yoon, C, Hassan, Y A, Ortiz-Villafuerte, J, and Schmidl, W D. Genetic algorithm tracking technique for particle image velocimetry and comparison with other tracking models. United States: N. p., 1996.
Web.
Yoon, C, Hassan, Y A, Ortiz-Villafuerte, J, & Schmidl, W D. Genetic algorithm tracking technique for particle image velocimetry and comparison with other tracking models. United States.
Yoon, C, Hassan, Y A, Ortiz-Villafuerte, J, and Schmidl, W D. 1996.
"Genetic algorithm tracking technique for particle image velocimetry and comparison with other tracking models". United States.
@article{osti_445549,
title = {Genetic algorithm tracking technique for particle image velocimetry and comparison with other tracking models},
author = {Yoon, C and Hassan, Y A and Ortiz-Villafuerte, J and Schmidl, W D},
abstractNote = {Particle Image Velocimetry (PIV) is a nonintrusive measurement technique, which can be used to study the structure of various fluid flows. PIV is a very efficient measurement technique since it can obtain both qualitative and quantitative spatial information about the flow field being studied. This information can be further processed into information such as vorticity and pathlines. Other flow measurement techniques (Laser Doppler Velocimetry, Hot Wire Anemometry, etc...) only provide quantitative information at a single point. A study on the performance of the Sub-Grid Genetic Tracking Algorithm for use in Particle Image Velocimetry was performed. A comparison with other tracking routines as the Cross Correlation, Spring Model and Neural Network tracking techniques was conducted. All four algorithms were used to track with synthetic data, and the results are compared with those obtained from a Large Eddy simulation computational fluid dynamics program. The simulated vectors were compared with the results from the four tracking techniques, to determine the yield and reliability of each tracking algorithm.},
doi = {},
url = {https://www.osti.gov/biblio/445549},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Dec 31 00:00:00 EST 1996},
month = {Tue Dec 31 00:00:00 EST 1996}
}