A Density Control Based Adaptive Hexahedral Mesh Generation Algorithm

Incorporating improved refinement techniques for a grid-based geometrically-adaptive hexahedral mesh generation algorithm

Parallel adaptive tetrahedral mesh generation by the advancing front technique

This paper presents a new method for adaptive mesh generation from scattered point clouds. We develop new techniques for extracting geometric features from scattered point clouds. In addition, we present how to incorporate these geometric features into implicit mathematical models for fitting scattered, noisy point clouds. This method is also well suited for large and potentially noisy scattered point clouds. Our numerical experiments show that the new adaptive mesh generation algorithms can improve the accuracy of the mesh generation from computed tomography (CT) images.

formula omitted]MST: A robust unified algorithm for quadrilateral mesh adaptation in 2D and 3D

HybridOctree_Hex: Hybrid octree-based adaptive all-hexahedral mesh generation with Jacobian control

In this paper a fast triangular mesh based registration method is proposed. Having Template and Reference images as inputs, the template image is triangulated using a content adaptive mesh generation algorithm. Considering the pixel values at mesh nodes, interpolated using spline interpolation method for both of the images, the energy functional needed for image registration is minimized. The minimization process was achieved using a mesh based discretization of the distance measure and regularization term which resulted in a sparse system of linear equations, which due to the smaller size in comparison to the pixel-wise registration method, can be solved directly. Mean Squared Difference (MSD) is used as a metric for evaluating the results. Using the mesh based technique, higher speed was achieved compared to pixel-based curvature registration technique with fast DCT solver. The implementation was done in MATLAB without any specific optimization. Higher speeds can be achieved using C/C++ implementations.KeywordsMedical image registrationtriangular mesh generationcontent adaptive mesh generationdiffusion process

In this paper, we propose a novel algorithm of adaptive mesh generation for the nonlinear finite-element analysis of electric machines. An auto-mesh generation technique is applied at each nonlinear iterative step with the error estimation of the field distribution obtained by each nonlinear iterative calculation. The flux density and the permeability of the new elements are defined by interpolating calculations from previous mesh in order to continue the nonlinear iteration. The proposed method is applied to the analyses of several motors. It is clarified that the method gives nearly identical results of a conventional adaptive meshing method while the calculation time is reduced to less than 1/10 in most cases.

A finite element based shape optimization program has been developed for three‐dimensional shell structures which allows for multiple edge movement and large shape changes. This program automatically performs shape optimization by linking together adaptive mesh generation, substructuring and optimization algorithms with a commercial finite element analysis program, MSC/NASTRAN. This paper examines the robustness of the shape optimization program by varying the initial shape and design parameters for three examples. This is performed to determine the conditions under which similar optimum shapes will be generated for different initial conditions.

Four local error estimators used for a posteriori h-type adaptive finite-element mesh generation are presented and compared in the solution of several steady-state multiconductor eddy-current problems, encountered in electrical power transmission and distribution systems. The proposed technique combines four different criteria with the concept of Delaunay triangulation to provide finite-element triangular meshes, adaptive to the characteristics of each problem. By refining the elements with the largest errors and recomputing the solution iteratively, finite-element meshes having a uniform error density are obtained. The problems examined lead to quantitative results concerning the performance of each estimator in the accuracy of the solution, in terms of both convergence rate and quality of electromagnetic field lines.

In this paper, we introduce a novel, SPH-based(Smoothed Particle Hydrodynamics), parallel adaptive quadrilateral-dominated mesh generation method. Building upon particle-based mesh generation techniques, we propose an innovative Adaptive Smoothing Length Smoothed Particle Hydrodynamics (ASL-SPH) Method utilizing the L∞ norm metric to enhance particle relaxation. The core concept leverages the square characteristic of the L∞ norm's unit circle to facilitate the construction of high-quality quadrilateral meshes. The algorithm comprises three primary stages: (1) computation of the initial adaptive particle distribution based on target size and density fields; (2) GPU-accelerated particle relaxation guided by a direction field, employing the proposed ASL-SPH grounded in the L∞ norm metric; and (3) establishment of topological connections among particles to generate a quadrilateral-dominated mesh. Numerical experiments validate that the proposed method effectively generates high-quality quadrilateral-dominated meshes under complex boundary conditions. Furthermore, the integration of GPU parallel computing significantly enhances the algorithm's efficiency.

An adaptive accuracy-based a posteriori error estimator

State-of-the-art adaptive mesh generator implementation for dynamic asphaltene deposition in four-phase flow simulator in near well-bore region

In the adaptive mesh generation, the space mesh should be adequate to the surface mesh. When the analytical surface representation is not known, additional surface information may be extracted from triangular surface meshes. We describe a new surface reconstruction method which uses approximate Hessian of a piecewise linear function representing the discrete surface. Efficiency of the proposed method is illustrated with two CFD applications.

In part I (Guven et al 2007 Inverse Problems 23 1115–33), we analysed the error in the reconstructed optical absorption images resulting from the discretization of the forward and inverse problems. Our analysis led to two new error estimates, which present the relationship between the optical absorption imaging accuracy and the discretization error in the solutions of the forward and inverse problems. In this work, based on the analysis presented in part I, we develop new adaptive discretization schemes for the forward and inverse problems in order to reduce the error in the reconstructed images resulting from discretization. The proposed discretization schemes lead to adaptively refined composite meshes that yield the desired level of imaging accuracy while reducing the size of the discretized forward and inverse problems. We present numerical experiments to validate the error estimates developed in part I and show the improvement in the accuracy of the reconstructed optical images with the new adaptive mesh generation algorithms.

Towards adaptive kinetic-fluid simulations of weakly ionized plasmas