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@ARTICLE{Lintermann:875379,
author = {Lintermann, Andreas and Schröder, Wolfgang},
title = {{L}attice–{B}oltzmann simulations for complex geometries
on high-performance computers},
journal = {CEAS Aeronautical Journal},
volume = {11},
issn = {1869-5590},
address = {Wien [u.a.]},
publisher = {Springer},
reportid = {FZJ-2020-01990},
pages = {745-766},
year = {2020},
abstract = {Complex geometries pose multiple challenges to the field of
computational fluid dynamics. Grid generation for intricate
objects is often difficult and requires accurate and
scalable geometrical methods to generate meshes for
large-scale computations. Such simulations, furthermore,
presume optimized scalability on high-performance computers
to solve high-dimensional physical problems in an adequate
time. Accurate boundary treatment for complex shapes is
another issue and influences parallel load-balance. In
addition, large serial geometries prevent efficient
computations due to their increased memory footprint, which
leads to reduced memory availability for computations. In
this paper, a framework is presented that is able to address
the aforementioned problems. Hierarchical Cartesian
boundary-refined meshes for complex geometries are obtained
by a massively parallel grid generator. In this process, the
geometry is parallelized for efficient computation.
Simulations on large-scale meshes are performed by a
high-scaling lattice–Boltzmann method using the
second-order accurate interpolated bounce-back boundary
conditions for no-slip walls. The method employs Hilbert
decompositioning for parallel distribution and is hybrid
MPI/OpenMP parallelized. The parallel geometry allows to
speed up the pre-processing of the solver and massively
reduces the local memory footprint. The efficiency of the
computational framework, the application of which to, e.g.,
subsonic aerodynamic problems is straightforward, is shown
by simulating clearly different flow problems such as the
flow in the human airways, in gas diffusion layers of fuel
cells, and around an airplane landing gear configuration},
cin = {JSC},
ddc = {620},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {511 - Computational Science and Mathematical Methods
(POF3-511) / Rhinodiagnost $(jhpc54_20180501)$},
pid = {G:(DE-HGF)POF3-511 / $G:(DE-Juel1)jhpc54_20180501$},
typ = {PUB:(DE-HGF)16},
doi = {10.1007/s13272-020-00450-1},
url = {https://juser.fz-juelich.de/record/875379},
}