Publikationen SGS: Bibliographie 2020 BibTeX
@inproceedings {INPROC-2020-30,
author = {Alireza Naseri and Amin Totounferoush and Ignacio Gonzales and Miriam Mehl and Carlos P{\'e}rez-Segarra},
title = {{A scalable framework for the partitioned solution of fluid–structure interaction problems}},
booktitle = {Computational Mechanics},
publisher = {Springer},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Konferenz-Beitrag},
month = {Mai},
year = {2020},
isbn = {10.1007/s00466-020-01860-y},
keywords = {Mehl, Miriam; P{\'e}rez-Segarra, Carlos},
language = {Englisch},
cr-category = {G.1.8 Partial Differential Equations,
J.2 Physical Sciences and Engineering,
J.3 Life and Medical Sciences},
ee = {https://link.springer.com/article/10.1007/s00466-020-01860-y},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {In this work, we present a scalable and efficient parallel solver for the
partitioned solution of fluid–structure interaction problems through multi-code
coupling. Two instances of an in-house parallel software, TermoFluids, are used
to solve the fluid and the structural sub-problems, coupled together on the
interface via the preCICE coupling library. For fluid flow, the Arbitrary
Lagrangian–Eulerian form of the Navier–Stokes equations is solved on an
unstructured conforming grid using a second-order finite-volume discretization.
A parallel dynamic mesh method for unstructured meshes is used to track the
moving boundary. For the structural problem, the nonlinear elastodynamics
equations are solved on an unstructured grid using a second-order finite-volume
method. A semi-implicit FSI coupling method is used which segregates the fluid
pressure term and couples it strongly to the structure, while the remaining
fluid terms and the geometrical nonlinearities are only loosely coupled. A
robust and advanced multi-vector quasi-Newton method is used for the coupling
iterations between the solvers. Both the fluid and the structural solver use
distributed-memory parallelism. The intra-solver communication required for
data update in the solution process is carried out using non-blocking
point-to-point communicators. The inter-code communication is fully parallel
and point-to-point, avoiding any central communication unit. Inside each
single-physics solver, the load is balanced by dividing the computational
domain into fairly equal blocks for each process. Additionally, a load
balancing model is used at the inter-code level to minimize the overall idle
time of the processes. Two practical test cases in the context of hemodynamics
are studied, demonstrating the accuracy and computational efficiency of the
coupled solver. Strong scalability test results show a parallel efficiency of
83\% on 10,080 CPU cores.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-30&engl=0}
}
@inproceedings {INPROC-2020-16,
author = {Steffen Hirschmann and Andreas Kronenburg and Colin W. Glass and Dirk Pfl{\"u}ger},
title = {{Load-Balancing for Large-Scale Soot Particle Agglomeration Simulations}},
booktitle = {Parallel Computing: Technology Trends},
editor = {Ian Foster and Gerhard R. Joubert and Ludek Kucera and Wolfgang E. Nagel and Frans Peters},
publisher = {IOS Press},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
series = {Advances in Parallel Computing},
volume = {36},
pages = {147--156},
type = {Konferenz-Beitrag},
month = {M{\"a}rz},
year = {2020},
doi = {10.3233/APC200035},
language = {Englisch},
cr-category = {G.0 Mathematics of Computing General},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-16/INPROC-2020-16.pdf},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {In this work, we combine several previous efforts to simulate a large-scale
soot particle agglomeration with a dynamic, multi-scale turbulent background
flow field. We build upon previous simulations which include 3.2 million
particles and implement load-balancing into the used simulation software as
well as tests of the load-balancing mechanisms on this scenario. We increase
the simulation to 109.85 million particles, superpose a dynamically changing
multi-scale background flow field and use our software enhancements to the
molecular dynamics software ESPResSo to simulate this on a Cray XC40
supercomputer. To verify that our setup reproduces essential physics we scale
the influence of the flow field down to make the scenario mostly homogeneous on
the subdomain scale. Finally, we show that even on the homogeneous version of
this soot particle agglomeration simulation, load-balancing still pays off.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-16&engl=0}
}
@article {ART-2020-09,
author = {Alireza Naseri and Amin Totounferoush and Ignacio Gonzales and Miriam Mehl and Carlos David Perez-Segarra},
title = {{A scalable framework for the partitioned solution of fluid–structure interaction problems}},
journal = {Computational Mechanics},
publisher = {Springer Verlag},
volume = {66},
pages = {471--489},
type = {Artikel in Zeitschrift},
month = {Mai},
year = {2020},
isbn = {https://doi.org/10.1007/s00466-020-01860-y},
keywords = {Fluid-Structure Interaction; Partitioned Method; Multi-Code Coupling; Scalability; HPC},
language = {Englisch},
cr-category = {J.2 Physical Sciences and Engineering,
J.3 Life and Medical Sciences,
I.6.3 Simulation and Modeling Applications},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {In this work, we present a scalable and efficient parallel solver for the
partitioned solution of fluid–structure interaction problems through multi-code
coupling. Two instances of an in-house parallel software, TermoFluids, are used
to solve the fluid and the structural sub-problems, coupled together on the
interface via the preCICE coupling library. For fluid flow, the Arbitrary
Lagrangian–Eulerian form of the Navier–Stokes equations is solved on an
unstructured conforming grid using a second-order finite-volume discretization.
A parallel dynamic mesh method for unstructured meshes is used to track the
moving boundary. For the structural problem, the nonlinear elastodynamics
equations are solved on an unstructured grid using a second-order finite-volume
method. A semi-implicit FSI coupling method is used which segregates the fluid
pressure term and couples it strongly to the structure, while the remaining
fluid terms and the geometrical nonlinearities are only loosely coupled. A
robust and advanced multi-vector quasi-Newton method is used for the coupling
iterations between the solvers. Both the fluid and the structural solver use
distributed-memory parallelism. The intra-solver communication required for
data update in the solution process is carried out using non-blocking
point-to-point communicators. The inter-code communication is fully parallel
and point-to-point, avoiding any central communication unit. Inside each
single-physics solver, the load is balanced by dividing the computational
domain into fairly equal blocks for each process. Additionally, a load
balancing model is used at the inter-code level to minimize the overall idle
time of the processes. Two practical test cases in the context of hemodynamics
are studied, demonstrating the accuracy and computational efficiency of the
coupled solver. Strong scalability test results show a parallel efficiency of
83\% on 10,080 CPU cores.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-09&engl=0}
}
@article {ART-2020-08,
author = {Shashank Subramanian and Klaudius Scheufele and Miriam Mehl and George Biros},
title = {{Where did the tumor start? An inverse solver with sparse localization for tumor growth models}},
journal = {Inverse Problems},
publisher = {IOP Publisher},
volume = {36},
number = {4},
type = {Artikel in Zeitschrift},
month = {Februar},
year = {2020},
isbn = {10.1088/1361-6420/ab649c},
language = {Englisch},
cr-category = {G.1.2 Numerical Analysis Approximation,
G.1.6 Numerical Analysis Optimization,
G.1.8 Partial Differential Equations,
I.4 Image Processing and Computer Vision,
I.6.8 Types of Simulation,
J.3 Life and Medical Sciences},
ee = {https://iopscience.iop.org/article/10.1088/1361-6420/ab649c,
https://arxiv.org/abs/1907.06564},
contact = {miriam.mehl@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {We present a numerical scheme for solving an inverse problem for parameter
estimation in tumor growth models for glioblastomas, a form of aggressive
primary brain tumor. The growth model is a reaction–diffusion partial
differential equation (PDE) for the tumor concentration. We use a
PDE-constrained optimization formulation for the inverse problem. The unknown
parameters are the reaction coefficient (proliferation), the diffusion
coefficient (infiltration), and the initial condition field for the tumor PDE.
Segmentation of magnetic resonance imaging (MRI) scans drive the inverse
problem where segmented tumor regions serve as partial observations of the
tumor concentration. Like most cases in clinical practice, we use data from a
single time snapshot. Moreover, the precise time relative to the initiation of
the tumor is unknown, which poses an additional difficulty for inversion. We
perform a frozen-coefficient spectral analysis and show that the inverse
problem is severely ill-posed. We introduce a biophysically motivated
regularization on the structure and magnitude of the tumor initial condition.
In particular, we assume that the tumor starts at a few locations (enforced
with a sparsity constraint on the initial condition of the tumor) and that the
initial condition magnitude in the maximum norm is equal to one. We solve the
resulting optimization problem using an inexact quasi-Newton method combined
with a compressive sampling algorithm for the sparsity constraint. Our
implementation uses PETSc and AccFFT libraries. We conduct numerical
experiments on synthetic and clinical images to highlight the improved
performance of our solver over a previously existing solver that uses standard
two-norm regularization for the calibration parameters. The existing solver is
unable to localize the initial condition. Our new solver can localize the
initial condition and recover infiltration and proliferation. In clinical
datasets (for which the ground truth is unknown), our solver results in
qualitatively different solutions compared to the two-norm regularized solver.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-08&engl=0}
}
