8,284 research outputs found
Aspects of Unstructured Grids and Finite-Volume Solvers for the Euler and Navier-Stokes Equations
One of the major achievements in engineering science has been the development of computer algorithms for solving nonlinear differential equations such as the Navier-Stokes equations. In the past, limited computer resources have motivated the development of efficient numerical schemes in computational fluid dynamics (CFD) utilizing structured meshes. The use of structured meshes greatly simplifies the implementation of CFD algorithms on conventional computers. Unstructured grids on the other hand offer an alternative to modeling complex geometries. Unstructured meshes have irregular connectivity and usually contain combinations of triangles, quadrilaterals, tetrahedra, and hexahedra. The generation and use of unstructured grids poses new challenges in CFD. The purpose of this note is to present recent developments in the unstructured grid generation and flow solution technology
Simulation of inviscid compressible multi-phase flow with condensation
Condensation of vapours in rapid expansions of compressible gases is investigated. In the case of high temperature gradients the condensation will start at conditions well away from thermodynamic equilibrium of the fluid. In those cases homogeneous condensation is dominant over heterogeneous condensation. The present work is concerned with development of a simulation tool for computation of high speed compressible flows with homogeneous condensation. The resulting flow solver should preferably be accurate and robust to be used for simulation of industrial flows in general geometries
Geometric Rounding and Feature Separation in Meshes
Geometric rounding of a mesh is the task of approximating its vertex
coordinates by floating point numbers while preserving mesh structure.
Geometric rounding allows algorithms of computational geometry to interface
with numerical algorithms. We present a practical geometric rounding algorithm
for 3D triangle meshes that preserves the topology of the mesh. The basis of
the algorithm is a novel strategy: 1) modify the mesh to achieve a feature
separation that prevents topology changes when the coordinates change by the
rounding unit; and 2) round each vertex coordinate to the closest floating
point number. Feature separation is also useful on its own, for example for
satisfying minimum separation rules in CAD models. We demonstrate a robust,
accurate implementation
Quasiconvex Programming
We define quasiconvex programming, a form of generalized linear programming
in which one seeks the point minimizing the pointwise maximum of a collection
of quasiconvex functions. We survey algorithms for solving quasiconvex programs
either numerically or via generalizations of the dual simplex method from
linear programming, and describe varied applications of this geometric
optimization technique in meshing, scientific computation, information
visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure
Micro computed tomography based finite element models of calcium phosphate scaffolds for bone tissue engineering
Bone is a living tissue that is able to regenerate by itself. However, when severe bone defects occur, the natural regeneration may be impaired. In these cases, bone graft substitutes can be used to induce the natural healing process. As a scaffold for tissue engineering, these bone graft substitutes have to meet specific requirements. Among others, the material must be biocompatible, biodegradable and have a porous structure to allow vascularization, cell migration and formation of new bone. Additionally, the mechanical properties of the scaffold have to resemble the ones of native tissue. The goal of this project is to create a computational model of the calcium phosphate scaffolds that are produced by rapid-prototyping by the Biomaterials, Biomechanics, and Tissue Engineering group at the Technical University of Catalonia. These models are based on finite element analysis and micro computed tomography images in order to consider the actual architecture of the scaffolds. The generated FE-models allow the computation of both local strains, which act as mechanical stimuli on attached cells, as well as the behaviour of the entire scaffold. When considering this information, the scaffold can be optimized for tissue differentiation by tuning both the scaffold architecture and the scaffold material bulk properties.Incomin
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to the local support property of the B-spline basis functions. As a result,
we obtain a generalization of the traditional CNN convolution operator by using
continuous kernel functions parametrized by a fixed number of trainable
weights. In contrast to related approaches that filter in the spectral domain,
the proposed method aggregates features purely in the spatial domain. In
addition, SplineCNN allows entire end-to-end training of deep architectures,
using only the geometric structure as input, instead of handcrafted feature
descriptors. For validation, we apply our method on tasks from the fields of
image graph classification, shape correspondence and graph node classification,
and show that it outperforms or pars state-of-the-art approaches while being
significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201
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