Statistical Characterization of Multi-Phase Flow by Dynamic Tomography

  • The paper presents a special reconstruction algorithm that is capable to monitor density differences in multi-phase flows. The flow cross section is represented as discrete dynamic random field. A fixed gray value is assigned to each flow phase characterizing the material property of the phase. The image model is given by a set of non-linear stochastic difference equations. The corresponding inversion task is not accessible by common tomographic techniques applying reconstruction algorithms like filtered backprojection or algebraic reconstruction technique (ART). The developed algorithm is based on the Kalman filter technique adapted to non-linear phenomena. The average velocity distribution together with the corresponding covariance matrix of the liquid flow through a pipe serves as prior information in statistical sense. To overcome the non-linearity in the process model as well as in the measurement model the statistical linearization technique is applied. Moreover the RiccatiThe paper presents a special reconstruction algorithm that is capable to monitor density differences in multi-phase flows. The flow cross section is represented as discrete dynamic random field. A fixed gray value is assigned to each flow phase characterizing the material property of the phase. The image model is given by a set of non-linear stochastic difference equations. The corresponding inversion task is not accessible by common tomographic techniques applying reconstruction algorithms like filtered backprojection or algebraic reconstruction technique (ART). The developed algorithm is based on the Kalman filter technique adapted to non-linear phenomena. The average velocity distribution together with the corresponding covariance matrix of the liquid flow through a pipe serves as prior information in statistical sense. To overcome the non-linearity in the process model as well as in the measurement model the statistical linearization technique is applied. Moreover the Riccati equation, giving the error covariance matrix, and the equation for the optimal gain coefficients can be solved in advance and later used in the filter equation. It turns out that the resulting reconstruction or filter algorithm is recursive, i.e. yielding the quasi-optimal solution to the formulated inverse problem at every reconstruction step by successively counting for the new information collected in the projections. The applicability of the developed algorithm is discussed in terms of characterizing or monitoring a multi-phase flow in a pipe. ©2003 American Institute of Physicszeige mehrzeige weniger

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Metadaten
Autor*innen:Gerd-Rüdiger Jaenisch, Uladzimir Samadurau, Valentin M. Artemiev, A. Naumov
Persönliche Herausgeber*innen:D. O. Thompson
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Review of progress in quantitative nondestructive evaluation, volume 22
Jahr der Erstveröffentlichung:2003
Verlag:American Institute of Physics
Verlagsort:Melville, NY
Erste Seite:643
Letzte Seite:650
Veranstaltung:29th Annual Review of Progress in Quantitative Nondestructive Evaluation
Veranstaltungsort:Bellingham, WA, USA
Beginndatum der Veranstaltung:2002-07-14
Enddatum der Veranstaltung:2002-07-19
DOI:10.1063/1.1570197
ISSN:0743-0760
ISSN:0094-243X
ISBN:0-7354-00117-9
Bemerkung:
Geburtsname von Jaenisch, Gerd-Rüdiger: Tillack, G.-R. -  Birth name of Jaenisch, Gerd-Rüdiger: Tillack, G.-R.
Verfügbarkeit des Dokuments:Physisches Exemplar in der Bibliothek der BAM vorhanden ("Hardcopy Access")
Bibliotheksstandort:Sonderstandort: Publica-Schrank
Datum der Freischaltung:19.02.2016
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:24.11.2003
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