39,886 research outputs found
voFoam - A geometrical Volume of Fluid algorithm on arbitrary unstructured meshes with local dynamic adaptive mesh refinement using OpenFOAM
A new parallelized unsplit geometrical Volume of Fluid (VoF) algorithm with
support for arbitrary unstructured meshes and dynamic local Adaptive Mesh
Refinement (AMR), as well as for two and three dimensional computation is
developed. The geometrical VoF algorithm supports arbitrary unstructured meshes
in order to enable computations involving flow domains of arbitrary geometrical
complexity. The implementation of the method is done within the framework of
the OpenFOAM library for Computational Continuum Mechanics (CCM) using the C++
programming language with modern policy based design for high program code
modularity. The development of the geometrical VoF algorithm significantly
extends the method base of the OpenFOAM library by geometrical volumetric flux
computation for two-phase flow simulations.
For the volume fraction advection, a novel unsplit geometrical algorithm is
developed, which inherently sustains volume conservation utilizing unique
Lagrangian discrete trajectories located in the mesh points. This practice
completely eliminates the possibility of an overlap between the flux polyhedra
and hence significantly increases volume conservation. A new efficient
(quadratic convergent) and accurate iterative flux correction algorithm is
developed, which avoids topological changes of the flux polyhedra. Our
geometrical VoF algorithm is dimension agnostic, providing automatic support
for both 2D and 3D computations, following the established practice in
OpenFOAM. The geometrical algorithm used for the volume fraction transport has
been extended to support dynamic local AMR available in OpenFOAM. Furthermore,
the existing dynamic mesh capability of OpenFOAM has been modified to support
the geometrical mapping algorithm executed as a part of the dynamic local AMR
cycle. The method implementation is fully parallelized using the domain
decomposition approach.Comment: voFoam - geometrical unsplit VoF algorithm on unstructered meshe
MeshODE: A Robust and Scalable Framework for Mesh Deformation
We present MeshODE, a scalable and robust framework for pairwise CAD model
deformation without prespecified correspondences. Given a pair of shapes, our
framework provides a novel shape feature-preserving mapping function that
continuously deforms one model to the other by minimizing fitting and rigidity
losses based on the non-rigid iterative-closest-point (ICP) algorithm. We
address two challenges in this problem, namely the design of a powerful
deformation function and obtaining a feature-preserving CAD deformation. While
traditional deformation directly optimizes for the coordinates of the mesh
vertices or the vertices of a control cage, we introduce a deep bijective
mapping that utilizes a flow model parameterized as a neural network. Our
function has the capacity to handle complex deformations, produces deformations
that are guaranteed free of self-intersections, and requires low rigidity
constraining for geometry preservation, which leads to a better fitting quality
compared with existing methods. It additionally enables continuous deformation
between two arbitrary shapes without supervision for intermediate shapes.
Furthermore, we propose a robust preprocessing pipeline for raw CAD meshes
using feature-aware subdivision and a uniform graph template representation to
address artifacts in raw CAD models including self-intersections, irregular
triangles, topologically disconnected components, non-manifold edges, and
nonuniformly distributed vertices. This facilitates a fast deformation
optimization process that preserves global and local details. Our code is
publicly available
Towards a Computational Framework for Modeling the Impact of Aortic Coarctations upon Left Ventricular Load
Computational fluid dynamics (CFD) models of blood flow in the left ventricle
(LV) and aorta are important tools for analyzing the mechanistic links between
myocardial deformation and flow patterns. Typically, the use of image-based
kinematic CFD models prevails in applications such as predicting the acute
response to interventions which alter LV afterload conditions. However, such
models are limited in their ability to analyze any impacts upon LV load or key
biomarkers known to be implicated in driving remodeling processes as LV
function is not accounted for in a mechanistic sense.
This study addresses these limitations by reporting on progress made towards
a novel electro-mechano-fluidic (EMF) model that represents the entire physics
of LV electromechanics (EM) based on first principles. A biophysically detailed
finite element (FE) model of LV EM was coupled with a FE-based CFD solver for
moving domains using an arbitrary Eulerian-Lagrangian (ALE) formulation. Two
clinical cases of patients suffering from aortic coarctations (CoA) were built
and parameterized based on clinical data under pre-treatment conditions. For
one patient case simulations under post-treatment conditions after geometric
repair of CoA by a virtual stenting procedure were compared against
pre-treatment results. Numerical stability of the approach was demonstrated by
analyzing mesh quality and solver performance under the significantly large
deformations of the LV blood pool. Further, computational tractability and
compatibility with clinical time scales were investigated by performing strong
scaling benchmarks up to 1536 compute cores. The overall cost of the entire
workflow for building, fitting and executing EMF simulations was comparable to
those reported for image-based kinematic models, suggesting that EMF models
show potential of evolving into a viable clinical research tool.Comment: This research was supported by the grants F3210-N18 and I2760-B30
from the Austrian Science Fund (FWF), the EU grant CardioProof agreement
611232 and a BioTechMed award to GP. Additionally, this project has received
funding from the European Union's Horizon 2020 research and innovation
programme under the Marie Sklodowska-Curie Action H2020-MSCA-IF-2016
InsiliCardio, GA No. 75083
Cartoonish sketch-based face editing in videos using identity deformation transfer
We address the problem of using hand-drawn sketches to create exaggerated
deformations to faces in videos, such as enlarging the shape or modifying the
position of eyes or mouth. This task is formulated as a 3D face model
reconstruction and deformation problem. We first recover the facial identity
and expressions from the video by fitting a face morphable model for each
frame. At the same time, user's editing intention is recognized from input
sketches as a set of facial modifications. Then a novel identity deformation
algorithm is proposed to transfer these facial deformations from 2D space to
the 3D facial identity directly while preserving the facial expressions. After
an optional stage for further refining the 3D face model, these changes are
propagated to the whole video with the modified identity. Both the user study
and experimental results demonstrate that our sketching framework can help
users effectively edit facial identities in videos, while high consistency and
fidelity are ensured at the same time.Comment: In Computers & Graphics, 2019. (12 pages, 10 figures
A GPU-based Multi-level Algorithm for Boundary Value Problems
A novel and scalable geometric multi-level algorithm is presented for the
numerical solution of elliptic partial differential equations, specially
designed to run with high occupancy of streaming processors inside Graphics
Processing Units(GPUs). The algorithm consists of iterative, superposed
operations on a single grid, and it is composed of two simple full-grid
routines: a restriction and a coarsened interpolation-relaxation. The
restriction is used to collect sources using recursive coarsened averages, and
the interpolation-relaxation simultaneously applies coarsened finite-difference
operators and interpolations. The routines are scheduled in a saw-like refining
cycle. Convergence to machine precision is achieved repeating the full cycle
using accumulated residuals and successively collecting the solution. Its total
number of operations scale linearly with the number of nodes. It provides an
attractive fast solver for Boundary Value Problems (BVPs), specially for
simulations running entirely in the GPU. Applications shown in this work
include the deformation of two-dimensional grids, the computation of
three-dimensional streamlines for a singular trifoil-knot vortex and the
calculation of three-dimensional electric potentials in heterogeneous
dielectric media.Comment: 14 pages, 7 figure
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
We propose an end-to-end deep learning architecture that produces a 3D shape
in triangular mesh from a single color image. Limited by the nature of deep
neural network, previous methods usually represent a 3D shape in volume or
point cloud, and it is non-trivial to convert them to the more ready-to-use
mesh model. Unlike the existing methods, our network represents 3D mesh in a
graph-based convolutional neural network and produces correct geometry by
progressively deforming an ellipsoid, leveraging perceptual features extracted
from the input image. We adopt a coarse-to-fine strategy to make the whole
deformation procedure stable, and define various of mesh related losses to
capture properties of different levels to guarantee visually appealing and
physically accurate 3D geometry. Extensive experiments show that our method not
only qualitatively produces mesh model with better details, but also achieves
higher 3D shape estimation accuracy compared to the state-of-the-art
Uniqueness of Transformation based on Jacobian Determinant and curl-Vector
Numerical examples demonstrated that a prescribed positive Jacobian
determinant alone can not uniquely determine a diffeomorphism. It is
conjectured that the uniqueness of a transformation can be assured by its
Jacobian determinant and the curl-vector. In this work, we study the uniqueness
problem analytically and propose an approach to the proof of the uniqueness of
a transformation with prescribed Jacobian determinant and curl-vector.Comment: 11 pages, 8 figure
Towards realistic HPC models of the neuromuscular system
Realistic simulations of detailed, biophysics-based, multi-scale models
require very high resolution and, thus, large-scale compute facilities.
Existing simulation environments, especially for biomedical applications, are
designed to allow for a high flexibility and generality in model development.
Flexibility and model development, however, are often a limiting factor for
large-scale simulations. Therefore, new models are typically tested and run on
small-scale compute facilities. By using a detailed biophysics-based,
chemo-electromechanical skeletal muscle model and the international open-source
software library OpenCMISS as an example, we present an approach to upgrade an
existing muscle simulation framework from a moderately parallel version towards
a massively parallel one that scales both in terms of problem size and in terms
of the number of parallel processes. For this purpose, we investigate different
modeling, algorithmic and implementational aspects. We present improvements
addressing both numerical and parallel scalability. In addition, our approach
includes a novel visualization environment, which is based on the MegaMol
environment capable of handling large amounts of simulated data. It offers a
platform for fast visualization prototyping, distributed rendering, and
advanced visualization techniques. We present results of a variety of scaling
studies at the Tier-1 supercomputer HazelHen at the High Performance Computing
Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to
2.6 and achieved good scalability on up to 768 cores, where the previous
implementation used only 4 cores
A unified continuum and variational multiscale formulation for fluids, solids, and fluid-structure interaction
We develop a unified continuum modeling framework for viscous fluids and
hyperelastic solids using the Gibbs free energy as the thermodynamic potential.
This framework naturally leads to a pressure primitive variable formulation for
the continuum body, which is well-behaved in both compressible and
incompressible regimes. Our derivation also provides a rational justification
of the isochoric-volumetric additive split of free energies in nonlinear
continuum mechanics. The variational multiscale analysis is performed for the
continuum model to construct a foundation for numerical discretization. We
first consider the continuum body instantiated as a hyperelastic material and
develop a variational multiscale formulation for the hyper-elastodynamic
problem. The generalized-alpha method is applied for temporal discretization. A
segregated algorithm for the nonlinear solver is designed and carefully
analyzed. Second, we apply the new formulation to construct a novel unified
formulation for fluid-solid coupled problems. The variational multiscale
formulation is utilized for spatial discretization in both fluid and solid
subdomains. The generalized-alpha method is applied for the whole continuum
body, and optimal high-frequency dissipation is achieved in both fluid and
solid subproblems. A new predictor multi-corrector algorithm is developed based
on the segregated algorithm to attain a good balance between robustness and
efficiency. The efficacy of the new formulations is examined in several
benchmark problems. The results indicate that the proposed modeling and
numerical methodologies constitute a promising technology for biomedical and
engineering applications, particularly those necessitating incompressible
models
A curved-element unstructured discontinuous Galerkin method on GPUs for the Euler equations
In this work we consider Runge-Kutta discontinuous Galerkin methods (RKDG)
for the solution of hyperbolic equations enabling high order discretization in
space and time. We aim at an efficient implementation of DG for Euler equations
on GPUs. A mesh curvature approach is presented for the proper resolution of
the domain boundary. This approach is based on the linear elasticity equations
and enables a boundary approximation with arbitrary, high order. In order to
demonstrate the performance of the boundary curvature a massively parallel
solver on graphics processors is implemented and utilized for the solution of
the Euler equations of gas-dynamics
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