针对密集点云的快速曲面重建算法

为了能够快速地从高密度散乱点云生成三角形网格曲面,提出一种针对散乱点云的曲面重建算法.首先通过逐层外扩建立原始点云的近似网格曲面,然后对近似网格曲面进行二次剖分生成最终的精确曲面;为了能够处理噪声点云,在剖分过程中所有网格曲面顶点都通过层次B样条进行了优化.相比于其他曲面重建方法,该算法剖分速度快,且能够保证点云到所生成的三角网格曲面的距离小于预先设定容限.实验结果表明,文中算法能够有效地实现高密度散乱点云的三角剖分,且其剖分速度较已有算法有大幅提高.     

一种基于多边形剖分的有限元网格生成方法

在两步网格化过程中,待分析区域首先被剖分为具有三条或四条边的简单子区域部分.然后将利用传递模板法或映射法对这些子区域进行网格生成.本文结合计算几何和有限元网格自动生成问题,给出了一种基于简单多边形剖分的全四边形有限元网格自动生成方法.该方法分两步实现有限元网格生成首先通过权函数的引导,对待分析的简单多边形区域先进行子域剖分,得到一组三角形和凸四边形子域(大单元)的集合;然后利用中点剖分方法,将三角形和凸四边形子域单元剖分为全四边形有限元网格.实践证明,本文提出的方法实现简单、使用灵活,结果网格的质量良好.     

优化变形能的平面多边形同构剖分

平面多边形间的同构三角剖分是平面形状渐进过渡与插值的基础,降低对应三角形的变形程度是获得高质量应用的关键.文中提出一种基于变形能优化的2个平面多边形的同构剖分算法,其中包含同构剖分生成和变形能最小化2个模块.首先根据用户指定的对应特征点对多边形进行顶点重采样,得到顶点一一对应的2个多边形;然后利用带约束的Delaunay剖分对其中的一个多边形进行三角化,得到源网格;再用重心坐标将源网格的内部顶点嵌入到另一个多边形得到同构剖分(目标网格);最后逐一检查三角形的变形能,对源网格中变形能超过阈值的三角形进行细分,用同构剖分模块生成新的目标网格.实验及数据统计分析表明,该算法可以得到较好的同构三角剖分,提升网格质量,并能很好地避免纹理细节失真.     

三维地质构造的三角形格架式网格生成方法

三维油气运聚模拟计算方法需要在构造模型的约束下生成三维地质格架式网格。针对这一需求对现有网格生成方法进行了分析对比,并在此基础上提出一套三角形格架式网格生成方法。该方法采用了限定Delaunay三角剖分技术生成基础的地质层面模型,同时提出协同剖分算法来解决地层面网格在公共交线处的几何拓扑一致性问题,并表明了算法的正确性,最后给出地质层面的剖分实例。实验结果表明了该方法在工程实践中的有效性。     

基于“几何-拓扑”迭代优化的三维网格模型修复算法

针对三维网格模型孔洞保特征修复问题,提出一种基于"几何-拓扑"迭代优化的三维数据修复算法.给定残缺的三角网格模型,首先识别孔洞区域,利用动态规划方法对孔洞区域进行初始的三角剖分,赋予孔洞区域拓扑连接关系;然后识别孔洞边界一对特征点,基于特征点及其法向粗略拟合特征曲线,在特征曲线的指导下调整孔洞局部的拓扑结构,即孔洞区域拓扑连接关系优化;最后基于孔洞及其N环邻域构建保特征的局部总变分能量函数,迭代求解孔洞及其邻域的顶点几何位置,即局部顶点几何位置的优化,重复局部拓扑连接关系优化和顶点几何位置优化,直到拓扑结构优化处理中不再发生连接关系调整,即完成了三维网格模型的修复.在现有的完整三维网格模型上人为去除部分构造带孔洞的残缺模型,以此作为数据,与其他修复算法进行对比实验的结果表明,所提算法可以有效地恢复孔洞区域的显著特征,并且在修复时间和误差统计上占有明显优势.     

改进的三维ODT四面体网格质量优化算法

针对密度非均匀四面体网格,提出一种改进的三维ODT(optimal Delaunay triangulation)网格光顺算法,提高了ODT的适应性.在四面体网格中,以每一内部节点为核心节点,创建由与该节点相连接的四面体单元构成的星形结构;根据网格尺寸场把其星形结构转换到以核心点为中心的归一化空间内,然后在归一化空间内应用经典ODT光顺算法对核心点位置进行优化;通过中值重心坐标将核心点转换回物理空间;这样,通过逐一优化内部节点的空间位置达到优化四面体网格整体质量的目的.算例表明,该算法有效、健壮;对于密度非均匀的四面体网格,其光顺效果比经典的ODT算法更好.     

大规模离散点集Delaunay三角剖分加点策略的分析

局部变换法和Watson算法是离散点集Delaunay三角剖分的常用算法,算法过程中逐点添加、局部优化是三角网格生成速度的重要影响因素.按位置相邻次序逐点添加时易产生外接圆较大的扁平三角形,引起较大范围的局部优化,三角网格的生成速度下降.在位置相邻次序的点集中随机选择部分点生成相对匀称的初始三角网格,再依次添加数据点,可有效减少局部优化消耗的时间,提高三角网格的生成速度.以激光扫描测量数据为例,切分为不同数量的点集进行三角剖分测试,当数据点数大于20000点时,采用部分随机点优化策略,其三角剖分速度比直接按位置相邻次序添加的方法提高一倍以上,且数据量越大,效率越高.     

加权Catmull-Clark曲面

文中给出一种加权的 Catm ull- Clark剖分方法 .算法首先对初始多边形网格作一次不同于 Catmull- Clark方法的带权因子的剖分 ,再对生成网格实施 Catmull- Clark剖分 ,从而得到可控形状的 C1光滑曲面 .通过对第 1步中的权因子赋不同的值 ,能方便地控制生成曲面与其原始控制网格的逼近精度 .实验表明 ,本算法非常简洁 ,比传统 Catm ull- Clark方法具有更多的自由度 .     

适用于电磁场有限元计算的网格剖分算法

网格剖分是有限元法的关键,其剖分得到的网格质量决定了有限元法计算结果的准确性.提出基于Persson-Strang算法生成非结构化三角形网格的新算法.通过分析Laplacian平滑函数作用原理,提出新的平滑函数来减少迭代次数;提出一种在优化设计过程中无重构变形方法,通过定义边界网格框架利用坐标映射技术可以快速推导出网格;通过设置质量评估来解决不可终止性的可能和过度迭代,加入边界节点筛选功能,并对剖分得到的三角元进行有限元逆序编号处理.将该算法与Persson-Strang算法进行剖分效果对比,验证该算法应用于电磁场领域的有效性.     

曲面的自适应三角网格剖分

在传统的映射法基础上 ,采用自适应三角网格加密法能有效地处理带有特征约束条件的任意曲面的三角剖分问题 .在平面三角化算法中对环边统一处理 ,并且采取了一种简单有效的曲率估算方法 ,提高了运行效率 ;并在保持外观的基础上进行了网格质量的优化     

Explicit level set and density methods for topology optimization with equivalent minimum length scale constraints

The goal of this paper is to introduce local length scale control in an explicit level set method for topology optimization. The level set function is parametrized explicitly by filtering a set of nodal optimization variables. The extended finite element method (XFEM) is used to represent the non-conforming material interface on a fixed mesh of the design domain. In this framework, a minimum length scale is imposed by adopting geometric constraints that have been recently proposed for density-based topology optimization with projections filters. Besides providing local length scale control, the advantages of the modified constraints are twofold. First, the constraints provide a computationally inexpensive solution for the instabilities which often appear in level set XFEM topology optimization. Second, utilizing the same geometric constraints in both the density-based topology optimization and the level set optimization enables to perform a more unbiased comparison between both methods. These different features are illustrated in a number of well-known benchmark problems for topology optimization.

     

Feature-preserving surface mesh smoothing via suboptimal Delaunay triangulation

A method of triangular surface mesh smoothing is presented to improve angle quality by extending the original optimal Delaunay triangulation (ODT) to surface meshes. The mesh quality is improved by solving a quadratic optimization problem that minimizes the approximated interpolation error between a parabolic function and its piecewise linear interpolation defined on the mesh. A suboptimal problem is derived to guarantee a unique, analytic solution that is significantly faster with little loss in accuracy as compared to the optimal one. In addition to the quality-improving capability, the proposed method has been adapted to remove noise while faithfully preserving sharp features such as edges and corners of a mesh. Numerous experiments are included to demonstrate the performance of the method.     

Flexible G1 interpolation of quad meshes

Transforming an arbitrary mesh into a smooth G¹ surface has been the subject of intensive research works. To get a visual pleasing shape without any imperfection even in the presence of extraordinary mesh vertices is still a challenging problem in particular when interpolation of the mesh vertices is required. We present a new local method, which produces visually smooth shapes while solving the interpolation problem. It consists of combining low degree biquartic Bézier patches with minimum number of pieces per mesh face, assembled together with G¹-continuity. All surface control points are given explicitly. The construction is local and free of zero-twists. We further show that within this economical class of surfaces it is however possible to derive a sufficient number of meaningful degrees of freedom so that standard optimization techniques result in high quality surfaces.     

Regularization of shape optimization problems using FE-based parametrization

This paper introduces a general fully stabilized mesh based shape optimization strategy, which allows for shape optimization of mechanical problems on FE-based parametrization. The well-known mesh dependent results are avoided by application of filter methods and mesh regularization strategies. Filter methods are successfully applied to SIMP (Solid Isotropic Material with Penalization) based topology optimization for many years. The filter method presented here uses a specific formulation that is based on convolution integrals. It is shown that the filter methods ensure mesh independency of the optimal designs. Furthermore they provide an easy and robust tool to explore the whole design space with respect to optimal designs with similar mechanical properties. A successful application of optimization strategies with FE-based parametrization requires the combination of filter methods with mesh regularization strategies. The latter ones ensure reliable results of the finite element solutions that are crucial for the sensitivity analysis. This presentation introduces a new mesh regularization strategy that is based on the Updated Reference Strategy (URS). It is shown that the methods formulated on this mechanical basis result in fast and robust mesh regularization methods. The resulting grids show a minimum mesh distortion even for large movements of the mesh boundary. The performance of the proposed regularization methods is demonstrated by several illustrative examples.     

Solving minimum distance problems with convex or concave bodies using combinatorial global optimization algorithms.

Determining the minimum distance between convex objects is a problem that has been solved using many different approaches. On the other hand, computing the minimum distance between combinations of convex and concave objects is known to be a more complicated problem. Most methods propose to partition the concave object into convex subobjects and then solve the convex problem between all possible subobject combinations. This can add a large computational expense to the solution of the minimum distance problem. In this paper, an optimization-based approach is used to solve the concave problem without the need for partitioning concave objects into convex pieces. Since the optimization problem is no longer unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing (SA) and Genetic Algorithms (GAs) are used to solve the concave minimum distance problem. In order to reduce the computational expense, it is proposed to replace the objects' geometry by a set of points on the surface of each body. This reduces the problem to an unconstrained combinatorial optimization problem, where the combination of points (one on the surface of each body) that minimizes the distance will be the solution. Additionally, if the surface points are set as the nodes of a surface mesh, it is possible to accelerate the convergence of the global optimization algorithm by using a hill-climbing local optimization algorithm. Some examples using these novel approaches are presented.     

Complexity and Modeling Aspects of Mesh Refinement into Quadrilaterals

We investigate the following mesh refinement problem: Given a mesh of polygons in three-dimensional space, find a decomposition into strictly convex quadrilaterals such that the resulting mesh is conforming and satisfies prescribed local density constraints. The conformal mesh refinement problem is shown to be feasible if and only if a certain system of linear equations over GF(2) has a solution. To improve mesh quality with respect to optimization criteria such as density, angles, and regularity, we introduce a reduction to a minimum cost bidirected flow problem. However, this model is only applicable if the mesh does not contain branching edges, that is, edges incident to more than two polygons. The general case with branchings, however, turns out to be strongly -hard. To enhance the mesh quality for meshes with branchings, we introduce a two-stage approach which first decomposes the whole mesh into components without branchings, and then uses minimum cost bidirected flows on the components in a second phase. Received March 10, 1997; revised August 15, 1997.     

Optimization of passive microwave devices through geometrical parameterization—Application to the design of a cylindrical dual‐mode cavity filter

A new algorithm, based on geometrical parameterization and finite element method is presented for the optimization of microwave devices. Using geometrical parameterization, the field quantities are expressed as a polynomial in design parameters. Automatic differentiation is used for calculation of higher order derivatives. To ensure continuous gradients, an integrated mesh deformation algorithm is deployed to morph initial finite elements mesh into the perturbed geometry. The resulting parametric model is deployed through quadratic surface reconstruction to find local minimum at each stage. The convergence of the optimization through the reconstructed surfaces is discussed. As an example, a 5‐pole dual‐mode cavity filter is designed and optimized using the presented algorithm. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Automatic Delaunay mesh generation method and physically-based mesh optimization method on two-dimensional regions

Delaunay mesh generation method is a common method for unstructured mesh (or unstructured grid) generation. Delaunay mesh generation method can conveniently add new points to the existing mesh without remeshing the whole domain. However, the quality of the generated mesh is not high enough if compared with some mesh generation methods. To obtain high-quality mesh, this paper developed an automatic Delaunay mesh generation method and a physically-based mesh optimization method on two-dimensional regions. For the Delaunay mesh generation method, boundary-conforming problem was ensured by create nodes at centroid of mesh elements. The definition of node bubbles and element bubbles was provided to control local mesh coarseness and fineness automatically. For the physically-based mesh optimization method, the positions of boundary node bubbles are predefined, the positions of interior node bubbles are adjusted according to interbubble forces. Size of interior node bubbles is further adjusted according to the size of adjacent node bubbles. Several examples show that high-quality meshes are obtained after mesh optimization.

     

Minimum length-scale constraints for parameterized implicit function based topology optimization

This paper introduces explicit minimum length-scale constraint functions suitable for parameterized implicit function based topology optimization methods. Length-scale control in topology optimization has many potential benefits, such as removing numerical artifacts, mesh independent solutions, avoiding thin, or single node hinges in compliant mechanism design and meeting manufacturing constraints. Several methods have been developed to control length-scale when using density-based or signed-distance-based level-set methods. In this paper a method is introduced to control length-scale for parameterized implicit function based topology optimization. Explicit constraint functions to control the minimum length in the structure and void regions are proposed and implementation issues explored in detail. Several examples are presented to show the efficacy of the proposed method. The examples demonstrate that the method can simultaneously control minimum structure and void length-scale, design hinge free compliant mechanisms and control minimum length-scale for three dimensional structures.