Optimal Dual Schemes for Adaptive Grid Based Hexmeshing

Hexahedral meshes are a ubiquitous domain for the numerical resolution of partial differential equations. Computing a pure hexahedral mesh from an adaptively refined grid is a prominent approach to automatic hexmeshing, and requires the ability to restore the all hex property around the hanging nodes that arise at the interface between cells having different size. The most advanced tools to accomplish this task are based on mesh dualization. These approaches use topological schemes to regularize the valence of inner vertices and edges, such that dualizing the grid yields a pure hexahedral mesh. In this article, we study in detail the dual approach, and propose four main contributions to it: (i) We enumerate all the possible transitions that dual methods must be able to handle, showing that prior schemes do not natively cover all of them; (ii) We show that schemes are internally asymmetric, therefore not only their construction is ambiguous, but different implementative choices lead to hexahedral meshes with different singular structure; (iii) We explore the combinatorial space of dual schemes, selecting the minimum set that covers all the possible configurations and also yields the simplest singular structure in the output hexmesh; (iv) We enlarge the class of adaptive grids that can be transformed into pure hexahedral meshes, relaxing one of the tight topological requirements imposed by previous approaches. Our extensive experiments show that our transition schemes consistently outperform prior art in terms of ability to converge to a valid solution, amount and distribution of singular mesh edges, and element count. Last but not least, we publicly release our code and reveal a conspicuous amount of technical details that were overlooked in previous literature, lowering an entry barrier that was hard to overcome for practitioners in the field.

Recut: a Concurrent Framework for Sparse Reconstruction of Neuronal Morphology

Interpreting the influx of microscopy and neuroimaging data is bottlenecked by neuronal reconstruction's long-standing issues in accuracy, automation, and scalability. Rapidly increasing data size is particularly concerning for modern computing infrastructure due to the wall in memory bandwidth which historically has witnessed the slowest rate of technological advancement. Recut is an end to end reconstruction pipeline that takes raw large-volume light microscopy images and yields filtered or tuned automated neuronal reconstructions that require minimal proofreading and no other manual intervention. By leveraging adaptive grids and other methods, Recut also has a unified data representation with up to a 509× reduction in memory footprint resulting in an 89.5× throughput increase and enabling an effective 64× increase in the scale of volumes that can be skeletonized on servers or resource limited devices. Recut also employs coarse and fine-grained parallelism to achieve speedup factors beyond CPU core count in sparse settings when compared to the current fastest reconstruction method. By leveraging the sparsity in light microscopy datasets, this can allow full brains to be processed in-memory, a property which may significantly shift the compute needs of the neuroimaging community. The scale and speed of Recut fundamentally changes the reconstruction process, allowing an interactive yet deeply automated workflow.

Development of algorithms for constructing two-dimensional optimal boundary-adaptive grids and their software implementation

Introduction. It is noted that the use of adaptive grids in calculations makes it possible to improve the accuracy and efficiency of computational algorithms without increasing the number of nodes. This approach is especially efficient when calculating nonstationary problems. The objective of this study is the development, construction and software implementation of methods for constructing computational two-dimensional optimal boundary-adaptive grids for complex configuration regions while maintaining the specified features of the shape and boundary of the region. The application of such methods contributes to improving the accuracy, efficiency, and cost-effectiveness of computational algorithms.Materials and Methods. The problem of automatic construction of an optimal boundary-adaptive grid in a simply connected region of arbitrary geometry, topologically equivalent to a rectangle, is considered. A solution is obtained for the minimum set of input information: the boundary of the region in the physical plane and the number of points on it are given. The creation of an algorithm and a mesh generation program is based on a model of particle dynamics. This provides determining the trajectories of individual particles and studying the dynamics of their pair interaction in the system under consideration. The interior and border nodes of the grid are separated through using the mask tool, and this makes it possible to determine the speed of movement of nodes, taking into account the specifics of the problem being solved.Results. The developed methods for constructing an optimal boundary-adaptive grid of a complex geometry region provides solving the problem on automatic grid construction in two-dimensional regions of any configuration. To evaluate the results of the algorithm research, a test problem was solved, and the solution stages were visualized. The computational domain of the test problem and the operation of the function for calculating the speed of movement of interior nodes are shown in the form of figures. Visualization confirms the advantage of this meshing method, which separates the border and interior nodes.Discussion and Conclusions. The theoretical and numerical studies results are important both for the investigation of the grids qualitative properties and for the computational grid methods that provide solving numerical modeling problems efficiently and with high accuracy.

Special adaptive grids and Runge - Richardson correction in problems with layers

При решении задач с пограничными и внутренними слоями на адаптивных сетках весьма желательно пользоваться разностными схемами, которые имеют достаточно хорошую точность и сходятся равномерно по малому параметру при стремлении шагов сетки к нулю. Однако эти требования оказываются противоречивыми: схемы высокой точности не сходятся равномерно, а равномерно сходящиеся схемы имеют обычно лишь первый порядок точности. Тем не менее существует уникальная возможность разрешить это противоречие, повышая порядок точности путем применения экстраполяционных поправок Рунге-Ричардсона, представляющих собой линейные комбинации разностных решений на вложенных сетках. В данной работе на примере нескольких употребительных разностных схем изучается эффективность такого подхода к расчетам, полученным на адаптивных сетках, явно задаваемых специальными координатными преобразованиями. Исследуются две схемы противопотокового типа с диагональным преобладанием, равномерно сходящиеся, в сравнении с аналогом схемы с центральной разностью, не имеющей диагонального преобладания и не сходящейся равномерно. Кроме простых поправок применяются также двукратные поправки, еще более повышающие порядок точности результирующих решений It is highly desirable using difference schemes with high accuracy and uniform convergence in a small parameter as the grid steps tend to zero for solving the problems with both boundary and interior layers. However, these requirements turn out to be contradictory: highly-accurate schemes may not converge uniformly, and uniformly converging schemes usually have only the first order of accuracy. Nevertheless, there is a unique opportunity to resolve this contradiction by increasing the order of accuracy by applying the Richardson-Runge extrapolation corrections, which are linear combinations of difference solutions on nested grids. In this paper, using the example of several common difference schemes, we study the efficiency of such approach for calculations obtained on adaptive grids that are explicitly specified by special coordinate transformations. Two diagonal-dominated upstream-type uniformly converging schemes are investigated. They are compared with an analogue of the scheme with central difference that does not have a diagonal dominance and does not converge uniformly. In addition to simple corrections, double corrections are also used, which further increase the order of accuracy of the resulting solutions

Cartesian Mesh Generation with Local Refinement for Immersed Boundary Approaches

In this paper, an efficient and robust Cartesian Mesh Generation with Local Refinement for an Immersed Boundary Approach is proposed, whose key feature is the capability of high Reynolds number simulations by the use of wall function models, bypassing the need for accurate boundary layer discretization. Starting from the discrete manifold model of the object to be analyzed, the proposed model generates Cartesian adaptive grids for a CFD simulation, with minimal user interactions; the most innovative aspect of this approach is that the automatic generation is based on the segmentation of the surfaces enveloping the object to be analyzed. The aim of this paper is to show that this automatic workflow is robust and enables to get quantitative results on geometrically complex configurations such as marine vehicles. To this purpose, the proposed methodology has been applied to the simulation of the flow past a BB2 submarine, discretized by non-uniform grid density. The obtained results are comparable with those obtained by classical body-fitted approaches but with a significant reduction of the time required for the mesh generation.

Computational Costs of Multi-Frontal Direct Solvers with Analysis-Suitable T-Splines

In this paper, we consider the computational cost of a multi-frontal direct solver used for the factorization of matrices resulting from a discretization of isogeometric analysis with T-splines, and analysis-suitable T-splines. We start from model projection or model heat transfer problems discretized over two-dimensional meshes, either uniformly refined or refined towards a point or an edge. These grids preserve several symmetries and they are the building blocks of more complicated grids constructed during adaptive isotropic refinement procedures. A large class of computational problems construct meshes refined towards point or edge singularities. We propose an ordering that permutes the matrix in a way that the computational cost of a multi-frontal solver executed on adaptive grids is linear. We show that analysis-suitable T-splines with our ordering, besides having other well-known advantages, also significantly reduce the computational cost of factorization with the multi-frontal direct solver. Namely, the factorization with N T-splines of order p over meshes refined to a point has a linear O(Np4) cost, and the factorization with T-splines on meshes refined to an edge has O(N2pp2) cost. We compare the execution time of the multi-frontal solver with our ordering to the Approximate Minimum Degree (AMD) and Cuthill–McKee orderings available in Octave.