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@INBOOK{Cingi:875419,
author = {Lintermann, Andreas},
editor = {Cingi, Cemal and Bayar Muluk, Nuray},
title = {{A}pplication of {C}omputational {F}luid {D}ynamics
{M}ethods to {U}nderstand {N}asal {C}avity {F}lows},
address = {Cham},
publisher = {Springer International Publishing},
reportid = {FZJ-2020-02025},
pages = {75-84},
year = {2020},
comment = {All Around the Nose / Cingi, Cemal (Editor) ; Cham :
Springer International Publishing, 2020, Chapter 106 ; ISBN:
978-3-030-21216-2 ; doi:10.1007/978-3-030-21217-9},
booktitle = {All Around the Nose / Cingi, Cemal
(Editor) ; Cham : Springer
International Publishing, 2020, Chapter
106 ; ISBN: 978-3-030-21216-2 ;
doi:10.1007/978-3-030-21217-9},
abstract = {Computational fluid dynamics methods enable to numerically
predict complex flows with the help of computers. In the
fields of Engineering and Physics they are already in use
for decades to support design decissions and to get insight
into complex physical phenomena. The simulation techniques
have massively evolved over the past years and can nowadays
be applied in medical context to analyze bio-fluidmechanical
processes. Thanks to the continuous increase of
computational power and parallelism as well as algorithmic
advancements, accurate predictions of the flow in the nasal
cavity are possible today. This chapter introduces the
reader to the concepts of the computational fluid dynamics
of the nose. It delivers some fundamentals on pre-processing
medical image data, various techniques to generate
computational meshes and gives an overview of methods to
solve the governing equations of fluid motion. Thereby,
advantages and disadvantages of the various approaches are
explained. Subsequently, a variety of methods to analyze the
flow and particle dynamics in the nasal cavity, ranging from
streamline visualizations, pressure loss and temperature
increase considerations, wall-shear stress and heat-flux
distributions, to the analysis of the particle deposition
behavior and transitional flow, is presented. The chapter
concludes with how such methods can be used in clinical
applications and elaborates how future developments might
support decision making in medical pathways.},
pnm = {511 - Computational Science and Mathematical Methods
(POF3-511)},
pid = {G:(DE-HGF)POF3-511},
typ = {PUB:(DE-HGF)7},
doi = {10.1007/978-3-030-21217-9_106},
url = {https://juser.fz-juelich.de/record/875419},
}