Grid generation for the solution of partial differential equations

A general survey of grid generators is presented with a concern for understanding why grids are necessary, how they are applied, and how they are generated. After an examination of the need for meshes, the overall applications setting is established with a categorization of the various connectivity patterns. This is split between structured grids and unstructured meshes. Altogether, the categorization establishes the foundation upon which grid generation techniques are developed. The two primary categories are algebraic techniques and partial differential equation techniques. These are each split into basic parts, and accordingly are individually examined in some detail. In the process, the interrelations between the various parts are accented. From the established background in the primary techniques, consideration is shifted to the topic of interactive grid generation and then to adaptive meshes. The setting for adaptivity is established with a suitable means to monitor severe solution behavior. Adaptive grids are considered first and are followed by adaptive triangular meshes. Then the consideration shifts to the temporal coupling between grid generators and PDE-solvers. To conclude, a reflection upon the discussion, herein, is given

Solid NURBS Conforming Scaffolding for Isogeometric Analysis

This work introduces a scaffolding framework to compactly parametrise solid structures with conforming NURBS elements for isogeometric analysis. A novel formulation introduces a topological, geometrical and parametric subdivision of the space in a minimal plurality of conforming vectorial elements. These determine a multi-compartmental scaffolding for arbitrary branching patterns. A solid smoothing paradigm is devised for the conforming scaffolding achieving higher than positional geometrical and parametric continuity. Results are shown for synthetic shapes of varying complexity, for modular CAD geometries, for branching structures from tessellated meshes and for organic biological structures from imaging data. Representative simulations demonstrate the validity of the introduced scaffolding framework with scalable performance and groundbreaking applications for isogeometric analysis

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Solution-adaptive Cartesian cell approach for viscous and inviscid flows

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77313/1/AIAA-13171-269.pd

Techniques for mesh density control

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Mesh generation is crucial in computational fluids dynamic (CFD) analysis, which solves a set of partial differential equations (PDE) based on a computational mesh. To a large extent, the success of solving these equations depends on the mesh quality. In addition to the orthogonality and the smoothness, the mesh density distribution is the key to a desirable mesh. The objective of the current research is to develop methods which make the control of mesh density simple and effective. The resulting mesh is near-orthogonal but more desirable for the numerical simulation. In this study, two new techniques for mesh density control are proposed. The first one is a three-parameter stretching function which stretches the nodes along a line in two directions and control their location of the distribution. The second method is a modified RL system (Ryskin and Leal, 1983) in which the distortion function is evaluated by the averaged scale factors and the scale factors which are formulated by weighting functions of desired mesh density distribution

Automatic Generation of Near-Body Structured Grids

Numerical grid generation has been a bottleneck in the computational fluid dynamics process for a long time when using the structured overset grids. Many current structured overset grid generation schemes like the hyperbolic grid generation method require significant user interaction to generate good computational grids robustly. Other grid generation schemes like the elliptic grid generation method take a significant amount of time for grid calculation, which is not desirable for computational fluid dynamics. Herein a new grid generation method is presented that combines the hyperbolic grid generation scheme with the elliptic grid generation scheme that uses Poisson’s equation. The new scheme builds upon the strengths of the different techniques by first applying hyperbolic grid generation, which is very fast but sometimes fails in strong concavities, and then using elliptic grid generation to locally fix the problems where hyperbolic grid generation results are not acceptable for computational fluid dynamics calculation. The new technique is demonstrated in various examples that are known to cause problems for either hyperbolic or elliptic grid generation when applied alone. The computational speed of the combined scheme grid generation is also exanimated by comparing the results with hyperbolic and elliptic grid generation. The combined grid generation scheme is further implemented in Engineering Sketch Pad to get useful near-body structure grids based on the geometry of the model. Attributes in Engineering Sketch Pad are used to define the places where the surface and volume grids should be generated, while the tessellations are used to locate and project grid generation results and therefore boost grid generation speed. Three cases are tested to illustrate the implementation of the combined grid generation scheme in Engineering Sketch Pad

An algebraic homotopy method for generating quasi-three-dimensional grids for high-speed configurations

A fast and versatile procedure for algebraically generating boundary conforming computational grids for use with finite-volume Euler flow solvers is presented. A semi-analytic homotopic procedure is used to generate the grids. Grids generated in two-dimensional planes are stacked to produce quasi-three-dimensional grid systems. The body surface and outer boundary are described in terms of surface parameters. An interpolation scheme is used to blend between the body surface and the outer boundary in order to determine the field points. The method, albeit developed for analytically generated body geometries is equally applicable to other classes of geometries. The method can be used for both internal and external flow configurations, the only constraint being that the body geometries be specified in two-dimensional cross-sections stationed along the longitudinal axis of the configuration. Techniques for controlling various grid parameters, e.g., clustering and orthogonality are described. Techniques for treating problems arising in algebraic grid generation for geometries with sharp corners are addressed. A set of representative grid systems generated by this method is included. Results of flow computations using these grids are presented for validation of the effectiveness of the method

Doctor of Philosophy

dissertationVolumetric parameterization is an emerging field in computer graphics, where volumetric representations that have a semi-regular tensor-product structure are desired in applications such as three-dimensional (3D) texture mapping and physically-based simulation. At the same time, volumetric parameterization is also needed in the Isogeometric Analysis (IA) paradigm, which uses the same parametric space for representing geometry, simulation attributes and solutions. One of the main advantages of the IA framework is that the user gets feedback directly as attributes of the NURBS model representation, which can represent geometry exactly, avoiding both the need to generate a finite element mesh and the need to reverse engineer the simulation results from the finite element mesh back into the model. Research in this area has largely been concerned with issues of the quality of the analysis and simulation results assuming the existence of a high quality volumetric NURBS model that is appropriate for simulation. However, there are currently no generally applicable approaches to generating such a model or visualizing the higher order smooth isosurfaces of the simulation attributes, either as a part of current Computer Aided Design or Reverse Engineering systems and methodologies. Furthermore, even though the mesh generation pipeline is circumvented in the concept of IA, the quality of the model still significantly influences the analysis result. This work presents a pipeline to create, analyze and visualize NURBS geometries. Based on the concept of analysis-aware modeling, this work focusses in particular on methodologies to decompose a volumetric domain into simpler pieces based on appropriate midstructures by respecting other relevant interior material attributes. The domain is decomposed such that a tensor-product style parameterization can be established on the subvolumes, where the parameterization matches along subvolume boundaries. The volumetric parameterization is optimized using gradient-based nonlinear optimization algorithms and datafitting methods are introduced to fit trivariate B-splines to the parameterized subvolumes with guaranteed order of accuracy. Then, a visualization method is proposed allowing to directly inspect isosurfaces of attributes, such as the results of analysis, embedded in the NURBS geometry. Finally, the various methodologies proposed in this work are demonstrated on complex representations arising in practice and research