Schröder, Andreas (2023) Dense Lagrangian Particle Tracking and data assimilation in fluid mechanics. In: 15th International Symposium on Particle Image Velocimetry – ISPIV 2023, Seiten 1-2. The California State University (ScholarWorks). 15th International Symposium on Particle Image Velocimetry – ISPIV 2023, 2023-06-18 - 2023-06-21, San Diego, CA, USA.
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Offizielle URL: https://piv.sdsu.edu/keynote-speakers/
Kurzfassung
3D Lagrangian Particle Tracking (LPT) is an image-based flow measurement technique allowing to determine position, velocity and acceleration along a large number of particle tracks at high temporal resolution within the investigated volume. Providing many long individual particle tracks at statistically significant amounts transport and dispersion properties of the (turbulent) flow can be explored in detail. Furthermore, advanced binning procedures can produce a 3D array of important one- and multi-point statistics of the flow (like averages, Reynolds stresses, two-point-correlations etc.) at very high spatial resolution down to subpixel accuracies. 3D LPT is based on pulsed volumetric illumination of tracer particles inside the flow and imaging of the scattered particle light with temporal resolution onto typically three to six camera projections. The Shake-The-Box (STB) technique is an advanced 3D LPT method which combines the triangulation-based advanced Iterative Particle Reconstruction (IPR) technique with the exploitation of the temporal and spatial coherence of Lagrangian particle tracks in the investigated flow. STB enables the processing of particle image densities up to 0.15 ppp (particles per pixel) under good experimental conditions with an almost complete suppression of ghost particles. Subsequently, the dense scattered particle tracks are temporally filtered for estimating position, velocity and acceleration (material derivative) which can be used in a second step as input for data assimilation approaches using Navier-Stokes-constraints delivering the full time-resolved 3D velocity gradient tensor (VGT)- and pressure fields.
elib-URL des Eintrags: | https://elib.dlr.de/196258/ | ||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||
Zusätzliche Informationen: | https://piv.sdsu.edu/, Keynote Speakers, Proceedings of the 15th International Symposium on Particle Image Velocimetry | ScholarWorks (calstate.edu) | ||||||||
Titel: | Dense Lagrangian Particle Tracking and data assimilation in fluid mechanics | ||||||||
Autoren: |
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Datum: | Juni 2023 | ||||||||
Erschienen in: | 15th International Symposium on Particle Image Velocimetry – ISPIV 2023 | ||||||||
Referierte Publikation: | Ja | ||||||||
Open Access: | Nein | ||||||||
Gold Open Access: | Nein | ||||||||
In SCOPUS: | Nein | ||||||||
In ISI Web of Science: | Nein | ||||||||
Seitenbereich: | Seiten 1-2 | ||||||||
Verlag: | The California State University (ScholarWorks) | ||||||||
Name der Reihe: | Conference Proceedings | ||||||||
Status: | veröffentlicht | ||||||||
Stichwörter: | Lagrangian particle tracking, data assimilation, Shake-The-Box, turbulence | ||||||||
Veranstaltungstitel: | 15th International Symposium on Particle Image Velocimetry – ISPIV 2023 | ||||||||
Veranstaltungsort: | San Diego, CA, USA | ||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||
Veranstaltungsbeginn: | 18 Juni 2023 | ||||||||
Veranstaltungsende: | 21 Juni 2023 | ||||||||
Veranstalter : | SDSU | ||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||
HGF - Programm: | Luftfahrt | ||||||||
HGF - Programmthema: | Effizientes Luftfahrzeug | ||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||
DLR - Forschungsgebiet: | L EV - Effizientes Luftfahrzeug | ||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Virtuelles Flugzeug und Validierung | ||||||||
Standort: | Göttingen | ||||||||
Institute & Einrichtungen: | Institut für Aerodynamik und Strömungstechnik > Experimentelle Verfahren, GO | ||||||||
Hinterlegt von: | Micknaus, Ilka | ||||||||
Hinterlegt am: | 09 Aug 2023 14:56 | ||||||||
Letzte Änderung: | 24 Apr 2024 20:56 |
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