GiD 2008. 4th Conference on advances and applications of GiD

The extended use of simulation programs has leaned on the advances in user-friendly interfaces and in the capability to generate meshes for any generic complex geometry. More than ten years of development have made Gid grow to become one of the more popular pre ans postprocessing systems at international level. The constant dialogue between the GiD development team and the users has guided the development of giD to cover the pre-post needs of many disciplines in science and engineering. Following gthis philosophy, the biannual GiD Conference has become an important forum for discussion and interchange of experiences among the GiD community. This monograph includes the contributions of the participants to the fourth edition of the GiD Conference held in the island of Ibiza from 8-9 May 2008

Simplificación de mallas de triángulos

English: An algorithm has been developed to simplify triangles meshes using the uniform vertex clustering scheme coupled with an cuckoo hybrid hash table. The approach proposed by Christopher DeCoro has been used but instead of using his probabilistic octree to store the occupied cells of the 3D grid, the cuckoo hybrid hash table proposed by Dan A. Alcantara has been implemented. This hybrid hash table combines a classical sparse perfect hashing and the newly developed cuckoo hashing and is used to accumulate the quadric error functions and the vertices of the cell. A entirely multi-core cpu algorithm has been developed and tested with more than thirty models in three different platforms. The simplification algorithm has been also enhanced by forcing the simplified vertex to remain in its cell, correcting he normals of flipped triangles, getting the collapsed triangles as lines and filtering out the repeated triangles and lines. The hybrid tree has been enhanced to support with 64 bit keys. Some experiments with the hybrid hash has also been presented

Detail‐preserving mesh simplification

Mesh simplification is an important problem in computer graphics. Given a polygonal mesh, the goal is to generate another mesh which approximates the underlying shape but includes less polygons, edges and vertices. Early methods focused only on preserving the overall shape of the geometric model, whereas current methods also handle meshes with attributes (normal vectors, colors, texture coordinates) so that both the mesh shape and the mesh appearance are preserved. The goal of this master thesis is to develop, implement and test a mesh simplification algorithm able to simplify large models in‐core using a vertex clustering algorithm. Several detail‐preserving techniques will be examined and implemented and a new filter is proposed, taking into account geometry features and nodal defined attributes. We also review recent advances in spatial hash tables to achieve a more compact storage, and we analyze and evaluate their impact in the simplification process

Open tools for electromagnetic simulation programs

Purpose The aim of the paper is to propose three computer tools to create electromagnetic simulation programs: GiD, Kratos and EMANT. Design/methodology/approach The paper presents a review of numerical methods for solving electromagnetic problems and presentation of the main features of GiD, Kratos and EMANT. Findings The paper provides information about three computer tools to create electromagnetic simulation packages: GiD (geometrical modeling, data input, visualisation of results), Kratos (C++ library) and EMANT (finite element software for solving Maxwell equations). Research limitations/implications The proposed platforms are in development and future work should be done to validate the codes for expecific problems and to provide extensive manual and tutorial information. Practical implications The tools could be easily learnt by different user profiles: from end‐users interested in simulation programs to developers of simulation packages. Originality/value This paper offers an integrated vision of open and easily customisable tools for the demands of different users profiles.   &nbsp

Scalable system for large unstructured mesh simulation

Dealing with large simulation is a growing challenge. Ideally for the wellparallelized software prepared for high performance, the problem solving capability depends on the available hardware resources. But in practice there are several technical details which reduce the scalability of the system and prevent the effective use of such a software for large problems. In this work we describe solutions implemented in order to obtain a scalable system to solve and visualize large scale problems. The present work is based on Kratos MutliPhysics [1] framework in combination with GiD [2] pre and post processor. The applied techniques are verified by CFD simulation and visualization of a wind tunnel problem with more than 100 millions of elements in our in-hose cluster in CIMNE.Postprint (published version

Detail‐preserving mesh simplification

Mesh simplification is an important problem in computer graphics. Given a polygonal mesh, the goal is to generate another mesh which approximates the underlying shape but includes less polygons, edges and vertices. Early methods focused only on preserving the overall shape of the geometric model, whereas current methods also handle meshes with attributes (normal vectors, colors, texture coordinates) so that both the mesh shape and the mesh appearance are preserved. The goal of this master thesis is to develop, implement and test a mesh simplification algorithm able to simplify large models in‐core using a vertex clustering algorithm. Several detail‐preserving techniques will be examined and implemented and a new filter is proposed, taking into account geometry features and nodal defined attributes. We also review recent advances in spatial hash tables to achieve a more compact storage, and we analyze and evaluate their impact in the simplification process

Detail‐preserving mesh simplification

Mesh simplification is an important problem in computer graphics. Given a polygonal mesh, the goal is to generate another mesh which approximates the underlying shape but includes less polygons, edges and vertices. Early methods focused only on preserving the overall shape of the geometric model, whereas current methods also handle meshes with attributes (normal vectors, colors, texture coordinates) so that both the mesh shape and the mesh appearance are preserved. The goal of this master thesis is to develop, implement and test a mesh simplification algorithm able to simplify large models in‐core using a vertex clustering algorithm. Several detail‐preserving techniques will be examined and implemented and a new filter is proposed, taking into account geometry features and nodal defined attributes. We also review recent advances in spatial hash tables to achieve a more compact storage, and we analyze and evaluate their impact in the simplification process

Simplificación de mallas de triángulos

English: An algorithm has been developed to simplify triangles meshes using the uniform vertex clustering scheme coupled with an cuckoo hybrid hash table. The approach proposed by Christopher DeCoro has been used but instead of using his probabilistic octree to store the occupied cells of the 3D grid, the cuckoo hybrid hash table proposed by Dan A. Alcantara has been implemented. This hybrid hash table combines a classical sparse perfect hashing and the newly developed cuckoo hashing and is used to accumulate the quadric error functions and the vertices of the cell. A entirely multi-core cpu algorithm has been developed and tested with more than thirty models in three different platforms. The simplification algorithm has been also enhanced by forcing the simplified vertex to remain in its cell, correcting he normals of flipped triangles, getting the collapsed triangles as lines and filtering out the repeated triangles and lines. The hybrid tree has been enhanced to support with 64 bit keys. Some experiments with the hybrid hash has also been presented

Scalable system for large unstructured mesh simulation

Dealing with large simulation is a growing challenge. Ideally for the wellparallelized software prepared for high performance, the problem solving capability depends on the available hardware resources. But in practice there are several technical details which reduce the scalability of the system and prevent the effective use of such a software for large problems. In this work we describe solutions implemented in order to obtain a scalable system to solve and visualize large scale problems. The present work is based on Kratos MutliPhysics [1] framework in combination with GiD [2] pre and post processor. The applied techniques are verified by CFD simulation and visualization of a wind tunnel problem with more than 100 millions of elements in our in-hose cluster in CIMNE