001     912543
005     20260122171922.0
037 _ _ |a FZJ-2022-05716
041 _ _ |a English
100 1 _ |0 P:(DE-HGF)0
|a Abdelsamie, A.
|b 0
|e Corresponding author
111 2 _ |a Direct and Large-Eddy Simulation
|c Udine
|d 2022-10-26 - 2022-10-28
|g DLES13
|w Italy
245 _ _ |a Transition and acoustic excitation of stenotic pipe flows at different Reynolds numbers
260 _ _ |c 2022
336 7 _ |0 33
|2 EndNote
|a Conference Paper
336 7 _ |2 DataCite
|a Other
336 7 _ |2 BibTeX
|a INPROCEEDINGS
336 7 _ |2 DRIVER
|a conferenceObject
336 7 _ |2 ORCID
|a LECTURE_SPEECH
336 7 _ |0 PUB:(DE-HGF)6
|2 PUB:(DE-HGF)
|a Conference Presentation
|b conf
|m conf
|s 1670938589_31169
|x After Call
520 _ _ |a A human respiratory system consists of phonation components that are coupled in a complex manner in order to ensure various vital functions, in particular voice generation.The interdisciplinary nature of the processes controlling sound generation complicates the analysis. Analytical studies are limited and can only be used to characterize the main acoustic sources in connection to various types of fluid motion.Numerical investigations of sound generation require an accurate simulation of the flow field with a proper representation of the respiratory pathways and process conditions in order to get the acoustic source terms. Furthermore, voice formation is closelyrelated to the resonance of acoustic modes in and around the mouth cavity; in order to be able to model this properly, it is essential to identify first the sound sources excited within the vocal tract.To better elucidate this complex process, the fundamental mechanisms in a biofluid flow mimicking such configurations were investigated based on a simplified stenotic pipe using high-resolution DNS and LES.
536 _ _ |0 G:(DE-HGF)POF4-5111
|a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|c POF4-511
|f POF IV
|x 0
536 _ _ |0 G:(DE-Juel-1)SDLFSE
|a SDL Fluids & Solids Engineering
|c SDLFSE
|x 1
700 1 _ |0 P:(DE-Juel1)176474
|a Koh, Seong-Ryong
|b 1
700 1 _ |0 P:(DE-HGF)0
|a Janiga, G.
|b 2
700 1 _ |0 P:(DE-HGF)0
|a Thévenin, D.
|b 3
909 C O |o oai:juser.fz-juelich.de:912543
|p VDB
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-HGF)0
|a University of Magdeburg
|b 0
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)176474
|a Forschungszentrum Jülich
|b 1
|k FZJ
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-HGF)0
|a University of Magdeburg
|b 2
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-HGF)0
|a University of Magdeburg
|b 3
913 1 _ |0 G:(DE-HGF)POF4-511
|1 G:(DE-HGF)POF4-510
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|a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|v Enabling Computational- & Data-Intensive Science and Engineering
|x 0
914 1 _ |y 2022
920 _ _ |l no
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED


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