DOCSLIB.ORG

From Segmented Images to Good Quality Meshes Using Delaunay Refinement

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
From Segmented Images to Good Quality Meshes Using Delaunay Refinement Mesh generation Applications CGAL-mesh From Segmented Images to Good Quality Meshes using Delaunay Refinement Jean-Daniel Boissonnat INRIA Sophia-Antipolis Geometrica Project-Team Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Grid-based methods (MC and variants) I Produces unnecessarily large meshes I Regularization and decimation are difficult tasks I Imposes dominant axis-aligned edges Mesh generation Applications CGAL-mesh A determinant step towards numerical simulations Challenges I Quality of the mesh : accurary, topological correctness, size and shape of the elements I Number of elements, processing time I Robustness issues Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Mesh generation Applications CGAL-mesh A determinant step towards numerical simulations Challenges I Quality of the mesh : accurary, topological correctness, size and shape of the elements I Number of elements, processing time I Robustness issues Grid-based methods (MC and variants) I Produces unnecessarily large meshes I Regularization and decimation are difficult tasks I Imposes dominant axis-aligned edges Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Mesh generation Applications CGAL-mesh Affine Voronoi diagrams and Delaunay triangulations Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Mesh generation Applications CGAL-mesh Mesh generation by Delaunay refinement A simple greedy technique that goes back to the early 80’s [Frey, Hermeline] input : some domain D to be meshed output : a finite set of points E ⊂ D a triangulation T ⊂ Del(E) that covers or approximate D with well-shaped simplices main loop : REPEAT if f is a bad simplex of Del(E) take a point p so that f is not a simplex of Del(E [ fpg) E p update Del(E) UNTIL all simplices are good Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Mesh generation Applications CGAL-mesh Delaunay refinement : some milestones Polyhedral domains 1990-1995 First guaranteed method for 2D polygonal domains [Chew, Ruppert] 1998-2002 First guaranteed method for 3D polygonal domains [Shewchuk] 2000-2005 Sliver removal [Cheng et al.] 2003-2004 Sharp features [Cheng et al.] Curved domains 1993-1997 Restricted Delaunay triangulation [Chew, Edelsbrunner & Shah] 1998 "-sample, first guaranteed method for surface reconstruction [Amenta & Bern] 2003 First guaranteed surface mesher [B. & Oudot] 2005 First guaranteed volume mesher [Oudot & al.] 2007 Piecewise smooth surfaces [Dey et al., Rineau & Yvinec] Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Mesh generation Applications CGAL-mesh Outline of the talk Mesh generation Restricted Delaunay Triangulation Surface mesh generation Meshing 3D objects with curved boundaries Anisotropic mesh generation Applications Image segmentation Manifold reconstruction Spatio-temporal scenes modeling CGAL-mesh Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Restricted Delaunay triangulation [Chew 93] Definition The restricted Delaunay triangulation DeljS(E) is the set of facets of the Delaunay triangulation whose dual edges intersect the surface Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Restricted Delaunay triangulation [Chew 93] Definition The restricted Delaunay triangulation DeljS(E) is the set of facets of the Delaunay triangulation whose dual edges belong to VorjS(E) Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Restricted Delaunay triangulation [Chew 93] Definition The restricted Delaunay triangulation DeljS(E) is the set of facets of the Delaunay triangulation whose dual edges belong to VorjS(E) Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Sampling conditions [Amenta & Bern 98] The medial axis of S is the set of points with at least two closest points on S a finite point set E ⊆ S is an "-sample of S if 8x 2 S, dE(x) ≤ "dMA(x) dMA denotes the distance to the medial axis of S Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation I dMA is 1-Lipschitz : jdMA(x) − dMA(y)j ≤ kx − yk I dMA(x) κ(x) ≤ 1 (κ(x) = largest absolute curvature at x) 1;1 I dMA > 0 if S is C i.e. normals exist everywhere and the normal field is Lipschitz Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted DT and sampling [B.-Oudot 05] If all the surface Delaunay balls B(cf ; rf ) of DeljS(E) are small enough (rf ≤ " dMA(cf )), =) E is an O(")-sample of S Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Sampling smooth surfaces e e c c f f Surface Delaunay balls Each facet in DeljS(E) is circumscribed by a surface Delaunay ball B(cf ; rf ) centered on the surface and empty of vertices. Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Sampling smooth surfaces e e c c f f Surface Delaunay balls Each facet in DeljS(E) is circumscribed by a surface Delaunay ball B(cf ; rf ) centered on the surface and empty of vertices. Restricted DT and sampling [B.-Oudot 05] If all the surface Delaunay balls B(cf ; rf ) of DeljS(E) are small enough (rf ≤ " dMA(cf )), =) E is an O(")-sample of S Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Properties of the restricted Delaunay triangulation Local properties If E is a "-sample of S, Normals [Amenta & Bern 1998] The angles between normals to S and to DeljS(E) are O(") Curvatures [Cohen-Steiner & Morvan 2003] The curvature tensor of S can be estimated from DeljS(E) Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Properties of the restricted Delaunay triangulation Global properties [Amenta & Bern], [Dey et al.], [B.& Oudot] If E is a "-sample of S, " ≤ 0:12 I DeljS(E) is a triangulated surface isotopic to S 2 I The Hausdorff distance between S and DeljS(E) is O(" ) Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Algorithm INIT compute an initial (small) sample E0 ⊂ S REPEAT IF f is a bad facet insert in Del3D(cf ) update E and DeljS(E) UNTIL all facets are good Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Surface mesh generation φ : S ! R = Lipschitz function [Chew 1993, B. & Oudot 2003] 8x 2 S; 0 < φmin ≤ φ(x) < 0:2dMA(x) ORACLE : For a facet f of DeljS(E), return cf , rf and φ(cf ) dΩ A facet f is bad if rf > φ(cf ) >ε Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Surface mesh generation φ : S ! R = Lipschitz function [Chew 1993, B. & Oudot 2003] 8x 2 S; 0 < φmin ≤ φ(x) < 0:2dMA(x) ORACLE : For a facet f of DeljS(E), return cf , rf and φ(cf ) dΩ A facet f is bad if rf > φ(cf ) Algorithm INIT compute an initial (small) sample E0 ⊂ S >ε REPEAT IF f is a bad facet insert in Del3D(cf ) update E and DeljS(E) UNTIL all facets are good Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes I Can be adapted to produce facets with good aspect ratio I General (black box) model of surfaces, applicable in various contexts ? How to compute dMA(x)? Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Properties of the algorithm I The procedure terminates and therefore I Produces a good approximation of the surface I Produces sparse "-samples Emerging Trends in Visual Computing From Segmented Images to Good Quality Meshes I General (black box) model of surfaces, applicable in various contexts ? How to compute dMA(x)? Restricted Delaunay Triangulation Mesh generation Surface mesh generation Applications Meshing 3D objects with curved boundaries CGAL-mesh Anisotropic mesh generation Properties
Load more

Recommended publications
  • Polyhedral Mesh Generation and a Treatise on Concave Geometrical Edges
    Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 124 ( 2015 ) 174 – 186 24th International Meshing Roundtable (IMR24) Polyhedral Mesh Generation and A Treatise on Concave Geometrical Edges Sang Yong Leea,* aKEPCO International Nuclear Graduate School, 658-91 Haemaji-ro Seosaeng-myeon Ulju-gun, Ulsan 689-882, Republic of Korea Abstract Three methods are investigated to remove the concavity at the boundary edges and/or vertices during polyhedral mesh generation by a dual mesh method. The non-manifold elements insertion method is the first method examined. Concavity can be removed by inserting non-manifold surfaces along the concave edges and using them as an internal boundary before applying Delaunay mesh generation. Conical cell decomposition/bisection is the second method examined. Any concave polyhedral cell is decomposed into polygonal conical cells first, with the polygonal primal edge cut-face as the base and the dual vertex on the boundary as the apex. The bisection of the concave polygonal conical cell along the concave edge is done. Finally the cut-along-concave-edge method is examined. Every concave polyhedral cell is cut along the concave edges. If a cut cell is still concave, then it is cut once more. The first method is very promising but not many mesh generators can handle the non-manifold surfaces especially for complex geometries. The second method is applicable to any concave cell with a rather simple procedure but the decomposed cells are too small compared to neighbor non-decomposed cells. Therefore, it may cause some problems during the numerical analysis application. The cut-along-concave-edge method is the most viable method at this time.
    [Show full text]
  • Tetrahedral Meshing in the Wild
    Tetrahedral Meshing in the Wild YIXIN HU, New York University QINGNAN ZHOU, Adobe Research XIFENG GAO, New York University ALEC JACOBSON, University of Toronto DENIS ZORIN, New York University DANIELE PANOZZO, New York University Fig. 1. A selection of the ten thousand meshes in the wild tetrahedralized by our novel tetrahedral meshing technique. We propose a novel tetrahedral meshing technique that is unconditionally Additional Key Words and Phrases: Mesh Generation, Tetrahedral Meshing, robust, requires no user interaction, and can directly convert a triangle soup Robust Geometry Processing into an analysis-ready volumetric mesh. The approach is based on several core principles: (1) initial mesh construction based on a fully robust, yet ACM Reference Format: eicient, iltered exact computation (2) explicit (automatic or user-deined) Yixin Hu, Qingnan Zhou, Xifeng Gao, Alec Jacobson, Denis Zorin, and Daniele tolerancing of the mesh relative to the surface input (3) iterative mesh Panozzo. 2018. Tetrahedral Meshing in the Wild . ACM Trans. Graph. 37, 4, improvement with guarantees, at every step, of the output validity. The Article 1 (August 2018), 14 pages. https://doi.org/10.1145/3197517.3201353 quality of the resulting mesh is a direct function of the target mesh size and allowed tolerance: increasing allowed deviation from the initial mesh and 1 INTRODUCTION decreasing the target edge length both lead to higher mesh quality. Our approach enables łblack-boxž analysis, i.e. it allows to automatically Triangulating the interior of a shape is a fundamental subroutine in solve partial diferential equations on geometrical models available in the 2D and 3D geometric computation.
    [Show full text]
  • A Parallel Mesh Generator in 3D/4D
    Portland State University PDXScholar Portland Institute for Computational Science Publications Portland Institute for Computational Science 2018 A Parallel Mesh Generator in 3D/4D Kirill Voronin Portland State University, [email protected] Follow this and additional works at: https://pdxscholar.library.pdx.edu/pics_pub Part of the Theory and Algorithms Commons Let us know how access to this document benefits ou.y Citation Details Voronin, Kirill, "A Parallel Mesh Generator in 3D/4D" (2018). Portland Institute for Computational Science Publications. 11. https://pdxscholar.library.pdx.edu/pics_pub/11 This Pre-Print is brought to you for free and open access. It has been accepted for inclusion in Portland Institute for Computational Science Publications by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. A parallel mesh generator in 3D/4D∗ Kirill Voronin Portland State University [email protected] Abstract In the report a parallel mesh generator in 3d/4d is presented. The mesh generator was developed as a part of the research project on space-time discretizations for partial differential equations in the least-squares setting. The generator is capable of constructing meshes for space-time cylinders built on an arbitrary 3d space mesh in parallel. The parallel implementation was created in the form of an extension of the finite element software MFEM. The code is publicly available in the Github repository [1]. Introduction Finite element method is nowadays a common approach for solving various application problems in physics which is known first of all for its easy-to-implement methodology.
    [Show full text]
  • Polyhedral Mesh Generation
    POLYHEDRAL MESH GENERATION W. Oaks1, S. Paoletti2 adapco Ltd, 60 Broadhollow Road, Melville 11747 New York, USA [email protected], [email protected] ABSTRACT A new methodology to generate a hex-dominant mesh is presented. From a closed surface and an initial all-hex mesh that contains the closed surface, the proposed algorithm generates, by intersection, a new mostly-hex mesh that includes polyhedra located at the boundary of the geometrical domain. The polyhedra may be used as cells if the field simulation solver supports them or be decomposed into hexahedra and pyramids using a generalized mid-point-subdivision technique. This methodology is currently used to provide hex-dominant automatic mesh generation in the preprocessor pro*am of the CFD code STAR-CD. Keywords: mesh generation, polyhedra, hexahedra, mid-point-subdivision, computational geometry. 1. INTRODUCTION to create a mesh that satisfies the boundary constraint, while the second family does not require a quadrilateral In the past decade automatic mesh generation has received surface mesh. Its creation becomes part of the generation significant attention from researchers both in academia and process. in the industry with the result of considerable advances in The first family of methodologies such as spatial twist understanding the underlying topological and geometrical continuum STC and related whisker weaving [1], properties of meshes. hexahedral advancing-front [2], rely on a proof of existence Most of the practical methodologies that have been [3],[4] for combinatorial hexahedral meshes whose only developed rely on using simplical cells such as triangles in requirement is an even number of quadrilaterals on the 2D and tetrahedra in 3D, due to the availability of relatively boundary surface.
    [Show full text]
  • Parallel Adaptive Mesh Generation and Decomposition
    Purdue University Purdue e-Pubs Department of Computer Science Technical Reports Department of Computer Science 1995 Parallel Adaptive Mesh Generation and Decomposition Poting Wu Elias N. Houstis Purdue University, [email protected] Report Number: 95-012 Wu, Poting and Houstis, Elias N., "Parallel Adaptive Mesh Generation and Decomposition" (1995). Department of Computer Science Technical Reports. Paper 1190. https://docs.lib.purdue.edu/cstech/1190 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. PARALLEL ADAPTIVE MESH GENERAnON AND DECOMPOSITION Poling Wu Elias N. HOlISm Department of Computer Sciences Purdue University West Lafayette, IN 47907 CSD·TR·9S..()12 February 1995 Parallel Adaptive Mesh Generation and Decomposition Poting Wu and Elias N. Houstis Department of Computer Sciences Purdue University West Lafayette, Indiana 47907-1398, U.S.A. e-mail: [email protected] October, 1994 Abstract An important class of methodologies for the parallel processing of computational models defined on some discrete geometric data structures (i.e., meshes, grids) is the so called geometry decomposition or splitting approach. Compared to the sequential processing of such models, the geometry splitting parallel methodology requires an additional computational phase. It consists of the decomposition of the associated geometric data structure into a number of balanced subdomains that satisfy a number of conditions that ensure the load balancing and minimum communication requirement of the underlying computations on a parallel hardware platform. It is well known that the implementation of the mesh decomposition phase requires the solution of a computationally intensive problem.
    [Show full text]
  • Variational Tetrahedral Mesh Generation from Discrete Volume Data Julien Dardenne, Sébastien Valette, Nicolas Siauve, Noël Burais, Rémy Prost
    Variational tetrahedral mesh generation from discrete volume data Julien Dardenne, Sébastien Valette, Nicolas Siauve, Noël Burais, Rémy Prost To cite this version: Julien Dardenne, Sébastien Valette, Nicolas Siauve, Noël Burais, Rémy Prost. Variational tetrahedral mesh generation from discrete volume data. The Visual Computer, Springer Verlag, 2009, 25 (5), pp.401-410. hal-00537029 HAL Id: hal-00537029 https://hal.archives-ouvertes.fr/hal-00537029 Submitted on 17 Nov 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Noname manuscript No. (will be inserted by the editor) Variational tetrahedral mesh generation from discrete volume data J. Dardenne · S. Valette · N. Siauve · N. Burais · R. Prost the date of receipt and acceptance should be inserted later Abstract In this paper, we propose a novel tetrahedral mesh measured in terms of minimal dihedral angle. Indeed, the ac- generation algorithm, which takes volumic data (voxels) as curacy and convergence speed of a FEM-based simulation an input. Our algorithm performs a clustering of the origi- depends on the mesh objective quality. As a consequence, a nal voxels within a variational framework. A vertex replaces lot of research was carried out on the topic of mesh gener- each cluster and the set of created vertices is triangulated in ation.
    [Show full text]
  • From Triangulation to Simplex Mesh: a Simple and Efficient
    From Triangulation to Simplex Mesh: a Simple and Efficient Transformation Francisco J. GALDAMESa;c, Fabrice JAILLETb;c (a) Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, Santiago, Chile (b) Universit´ede Lyon, IUT Lyon 1, D´epartement Informatique, F-01000, France (c) Universit´ede Lyon, CNRS, Universit´eLyon 1, LIRIS, ´equipe SAARA, UMR5205, F-69622, France Preprint { April 2010 Abstract In the field of 3D images, relevant information can be difficult to in- terpret without further computer-aided processing. Generally, and this is particularly true in medical imaging, a segmentation process is run and coupled with a visualization of the delineated structures to help un- derstanding the underlying information. To achieve the extraction of the boundary structures, deformable models are frequently used tools. Amongst all techniques, Simplex Meshes are valuable models thanks to their good propensity to handle a large variety of shape alterations alto- gether with a fine resolution and stability. However, despite all these great characteristics, Simplex Meshes are lacking to cope satisfyingly with other related tasks, as rendering, mechanical simulation or reconstruction from iso-surfaces. As a consequence, Triangulation Meshes are often preferred. In order to face this problem, we propose an accurate method to shift from a model to another, and conversely. For this, we are taking advantage of the fact that triangulation and simplex meshes are topologically duals, turning it into a natural swap between these two models. A difficulty arise as they are not geometrically equivalent, leading to loss of informa- tion and to geometry deterioration whenever a transformation between these dual meshes takes place.
    [Show full text]
  • LNCS 3540, Pp
    Polygon Mesh Generation of Branching Structures Jo Skjermo and Ole Christian Eidheim Norwegian University of Science and Technology, Department of Computer and Information Science, [email protected] [email protected] Abstract. We present a new method for producing locally non- intersecting polygon meshes of naturally branching structures. The gen- erated polygon mesh follows the objects underlying structure as close as possible, while still producing polygon meshes that can be visualized efficiently on commonly available graphic acceleration hardware. A pri- ori knowledge of vascular branching systems is used to derive the poly- gon mesh generation method. Visualization of the internal liver vessel structures and naturally looking tree stems generated by Lindenmayer- systems is used as examples. The method produce visually convincing polygon meshes that might be used in clinical applications in the future. 1 Introduction Medical imaging through CT, MR, Ultrasound, PET, and other modalities has revolutionized the diagnosis and treatment of numerous diseases. The radiolo- gists and surgeons are presented with images or 3D volumes giving them detailed view of the internal organs of a patient. However, the task of analyzing the data can be time-consuming and error-prone. One such case is liver surgery, where a patient is typically examined using MR or CT scans prior to surgery. In partic- ular, the position of large hepatic vessels must be determined in addition to the relative positions of possible tumors to these vessels. Surgeons and radiologists will typically base their evaluation on a visual inspection of the 2D slices produced by CT or MR scans.
    [Show full text]
  • Lecture Notes on Delaunay Mesh Generation
    Lecture Notes on Delaunay Mesh Generation Jonathan Richard Shewchuk February 5, 2012 Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley, CA 94720 Copyright 1997, 2012 Jonathan Richard Shewchuk Supported in part by the National Science Foundation under Awards CMS-9318163, ACI-9875170, CMS-9980063, CCR-0204377, CCF-0430065, CCF-0635381, and IIS-0915462, in part by the University of California Lab Fees Research Program, in part bythe Advanced Research Projects Agency and Rome Laboratory, Air Force Materiel Command, USAF under agreement number F30602- 96-1-0287, in part by the Natural Sciences and Engineering Research Council of Canada under a 1967 Science and Engineering Scholarship, in part by gifts from the Okawa Foundation and the Intel Corporation, and in part by an Alfred P. Sloan Research Fellowship. Keywords: mesh generation, Delaunay refinement, Delaunay triangulation, computational geometry Contents 1Introduction 1 1.1 Meshes and the Goals of Mesh Generation . ............ 3 1.1.1 Domain Conformity . .... 4 1.1.2 ElementQuality ................................ .... 5 1.2 A Brief History of Mesh Generation . ........... 7 1.3 Simplices, Complexes, and Polyhedra . ............. 12 1.4 Metric Space Topology . ........ 15 1.5 HowtoMeasureanElement ........................... ....... 17 1.6 Maps and Homeomorphisms . ....... 21 1.7 Manifolds....................................... ..... 22 2Two-DimensionalDelaunayTriangulations 25 2.1 Triangulations of a Planar Point Set . ............. 26 2.2 The Delaunay Triangulation . .......... 26 2.3 The Parabolic Lifting Map . ......... 28 2.4 TheDelaunayLemma................................ ...... 30 2.5 The Flip Algorithm . ....... 32 2.6 The Optimality of the Delaunay Triangulation . ................ 34 2.7 The Uniqueness of the Delaunay Triangulation . ............... 35 2.8 Constrained Delaunay Triangulations in the Plane .
    [Show full text]
  • A Simple Mesh Generator in Matlab
    A SIMPLE MESH GENERATOR IN MATLAB PER-OLOF PERSSON AND GILBERT STRANG∗ Abstract. Creating a mesh is the first step in a wide range of applications, including scientific computing and computer graphics. An unstructured simplex mesh requires a choice of meshpoints (vertex nodes) and a triangulation. We want to offer a short and simple MATLAB code, described in more detail than usual, so the reader can experiment (and add to the code) knowing the underlying principles. We find the node locations by solving for equilibrium in a truss structure (using piecewise linear force-displacement relations) and we reset the topology by the Delaunay algorithm. The geometry is described implicitly by its distance function. In addition to being much shorter and simpler than other meshing techniques, our algorithm typically produces meshes of very high quality. We discuss ways to improve the robustness and the performance, but our aim here is simplicity. Readers can download (and edit) the codes from http://math.mit.edu/~persson/mesh. Key words. mesh generation, distance functions, Delaunay triangulation AMS subject classifications. 65M50, 65N50 1. Introduction. Mesh generators tend to be complex codes that are nearly inaccessible. They are often just used as ”black boxes.” The meshing software is difficult to integrate with other codes – so the user gives up control. We believe that the ability to understand and adapt a mesh generation code (as one might do with visualization, or a finite element or finite volume code, or geometry modeling in computer graphics) is too valuable an option to lose. Our goal is to develop a mesh generator that can be described in a few dozen lines of MATLAB.
    [Show full text]
  • Array-Based Hierarchical Mesh Generation in Parallel
    Available online at www.sciencedirect.com Procedia Engineering 00 (2015) 000–000 www.elsevier.com/locate/procedia 24th International Meshing Roundtable (IMR24) Array-Based Hierarchical Mesh Generation in Parallel Navamita Raya∗, Iulian Grindeanua, Xinglin Zhaob, Vijay Mahadevana, Xiangmin Jiaob aMathematics and Computer Science, Argonne National Laboratory, Argonne, IL 60439, USA bDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA. Abstract In this paper, we describe an array-based hierarchical mesh generation capability through uniform refinement of unstructured meshes for efficient solution of PDE’s using finite element methods and multigrid solvers. A multi-degree, multi-dimensional and multi-level framework is designed to generate the nested hierarchies from an initial mesh that can be used for a number of purposes such as multi-level methods to generating large meshes. The capability is developed under the parallel mesh framework “Mesh Oriented dAtaBase” a.k.a MOAB [16]. We describe the underlying data structures and algorithms to generate such hierarchies and present numerical results for computational efficiency and mesh quality. We also present results to demonstrate the applicability of the developed capability to a multigrid finite-element solver. c 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24). Keywords: uniform mesh refinement, hierarchical meshes, multigrid methods, parallel computation, half-facet 1. Introduction In the numerical solution of complex partial differential equations (PDE’s) using finite element methods for un- structured meshes, the two most computationally intensive steps are mesh generation and linear solvers. An initial coarse mesh representing the computational domain might not be of sufficient resolution to get meaningful results out of the discretizations for physical scales that might be present.
    [Show full text]
  • Unstructured Grid Techniques
    December 2, 1996 16:42 Annual Reviews Chapter14 AR23-14 Annu. Rev. Fluid. Mech. 1997. 29:473–514 Copyright c 1997 by Annual Reviews Inc. All rights reserved UNSTRUCTURED GRID TECHNIQUES D. J. Mavriplis Institute for Computer Applications in Science and Engineering NASA Langley Research Center, Hampton, VA 23681-0001 KEY WORDS: computational, fluid, Navier-Stokes, aerodynamic, mesh, multigrid ABSTRACT An overview of the current state of the art in unstructured mesh techniques for computational fluid dynamics is given. The topics of mesh generation and adapta- tion, spatial discretization, and solution techniques for steady flows are covered. Remaining difficulties in these areas are highlighted, and directions for future work are outlined. 1. INTRODUCTION The field of computational fluid dynamics has been evolving rapidly over the past several decades. Aerospace applications, particularly in the field of aero- dynamics, have provided the major impetus for the advances in computational fluid dynamics technology. Great strides in available computational power, coupled with rapid advances in algorithmic efficiency, have enabled computa- tional fluid dynamics to substantially reduce the amount of wind-tunnel time Annu. Rev. Fluid Mech. 1997.29:473-514. Downloaded from www.annualreviews.org by George Washington University - Medical Center on 11/10/10. For personal use only. required for airframe and propulsion system design. At the same time, computational methods are continuously being required to deliver more accurate solutions for more complex realistic configurations at lower computational cost. Traditionally, structured or block-structured meth- ods that rely on regular arrays of quadrilateral or hexahedral cells in two or three dimensions, respectively, have been employed to discretize the compu- tational domain.
    [Show full text]