000			04184nam a22004813i 4500
001			EBC31253102
003			MiAaPQ
005			20240724115947.0
006			m o d |
007			cr cnu||||||||
008			240724s2020 xx o ||||0 eng d
020			_a9780750341783 _q(electronic bk.)
020			_z9780750322041
035			_a(MiAaPQ)EBC31253102
035			_a(Au-PeEL)EBL31253102
035			_a(OCoLC)1130295079
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ
050		4	_aTA357.5.M43 D335 2020
082	0		_a620.1064
100	1		_aDabiri, Dana.
245	1	0	_aParticle Tracking Velocimetry.
250			_a1st ed.
264		1	_aBristol : _bInstitute of Physics Publishing, _c2020.
264		4	_c©2020.
300			_a1 online resource (211 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aIOP Ebooks Series
505	0		_aIntro -- Author biographies -- Dana Dabiri -- Charles Pecora -- Chapter 1 Introduction -- References -- Chapter 2 Experimental set-up -- 2.1 Tracer particles -- 2.1.1 Tracers in liquid flows -- 2.1.2 Tracers in subsonic gas flows -- 2.1.3 Tracers in supersonic flows -- 2.2 Illumination -- 2.3 Area/volume illumination optics -- 2.4 Camera -- References -- Chapter 3 Particle image identification -- 3.1 Non-overlapped particles -- 3.1.1 Threshold binarization -- 3.1.2 Centroid estimation -- 3.1.3 Gaussian estimation -- 3.2 Overlapped particles -- 3.2.1 Threshold binarization -- 3.2.2 Neural network particle identification -- 3.2.3 Particle mask correlation -- 3.2.4 Optical flow feature extraction -- 3.2.5 Linear model inversion -- 3.3 Particle identification comparison -- 3.3.1 Non-overlapped particle identification comparison -- 3.3.2 Overlapped particle identification comparison -- References -- Chapter 4 Identification of particles' spatial locations -- 4.1 Spatial location in 2D -- 4.2 Spatial localization in 3D -- 4.2.1 Photogrammetric PTV -- 4.2.2 Tomographic PTV -- 4.2.3 Synthetic aperture PTV -- 4.2.4 Plenoptic imaging -- 4.2.5 Holographic PTV -- 4.3 Optical distortions and calibration in PTV systems -- 4.3.1 2D calibration -- 4.3.2 Stereoscopic calibration -- 4.3.3 3D calibration -- References -- Chapter 5 Particle tracking techniques -- 5.1 Multi-frame approach -- 5.2 Cross correlation -- 5.3 Relaxation methods -- 5.4 Neural networks -- 5.5 Velocity gradient tensor -- 5.6 Polar-coordinate similarity -- 5.7 Optimization methods -- 5.7.1 Deterministic annealing -- 5.7.2 Variational approach -- 5.7.3 Genetic algorithms -- 5.7.4 Ant colony optimization -- 5.7.5 Fuzzy logic PTV -- 5.8 Delaunay tessellation methods -- 5.9 Voronoi diagram methods -- 5.10 Vision-based PTV -- 5.11 Statistical approach -- 5.12 Outlier detection -- References.
505	8		_aChapter 6 Combined tracking and localization for 3D PTV -- 6.1 Shake-the-box -- References -- Chapter 7 3D-PTV comparison -- References -- Chapter 8 Post-processing -- 8.1 Interpolation -- 8.2 Pressure calculation -- References -- Chapter 9 Conclusions -- References.
520			_aParticle tracking velocimetry (PTV) is one of the latest and most powerful flow visualization techniques, using numerous cameras to track flow tracers in two or three dimensions. This book provides a review of both experimental and computational aspects of PTV for academic and industrial researchers and engineers.
588			_aDescription based on publisher supplied metadata and other sources.
590			_aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650		0	_aParticle image velocimetry.
650		0	_aParticle tracking velocimetry.
655		4	_aElectronic books.
700	1		_aPecora, Charles.
776	0	8	_iPrint version: _aDabiri, Dana _tParticle Tracking Velocimetry _dBristol : Institute of Physics Publishing,c2020 _z9780750322041
797	2		_aProQuest (Firm)
830		0	_aIOP Ebooks Series
856	4	0	_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=31253102 _zClick to View
999			_c36801 _d36801