保特征的自适应三角网格规范化算法

为了提高基于工业CT图像重构的三角网格质量,提出了顶点调整和特征因子相结合的网格规范化算法。引入模型特征因子和局部网格质量提高程度作为网格调整的控制条件,保留原始模型的局部细节特征;采用法矢量阈值按规律递增的方式自适应控制网格调整,实现不同曲率特征的自动识别。实验结果表明,与现有方法相比,该方法能更好地规范模型的三角网格,同时保留了原始模型的细节特征。     

基于工业CT图像重构的网格模型的保特征简化方法

对基于工业CT图像重构的网格模型进行网格简化时,大多数现有网格模型简化算法会丢失特征,出现网格质量不好的问题。因此提出一种网格模型保特征简化方法,该方法用三角形折叠法对原始模型进行简化,当简化后模型的平均二面角角度误差达到允许误差后,再使用边折叠法对模型进行简化。在三角形折叠法中提出了利用被折叠三角形的法向量、各个顶点的高斯曲率及其在周边三角形上的投影确定该三角形的折叠点,利用局部体积误差与二面角角度误差的无因次化和确定折叠代价的方法;在边折叠法中提出了将二面角角度误差引入到二次误差测度（QEM）法的折叠代价中的改进QEM法。实验结果表明：与其他算法相比,该方法能够生成保特征、高质量、低几何误差的网格模型。     

基于工业CT图像重构的网格模型的全局优化方法

针对用工业CT切片图像直接重构得到的网格模型质量不高的问题,提出一种不受拓扑结构限制的隐式曲面重构全局优化方法。该方法将三维表面模型用隐式函数来表示,通过模型提供的点云信息计算出隐式函数,提取等值面,实现曲面重构。针对隐式曲面重构数据处理量大的问题,引入FFTW快速傅里叶变换来提高效率。实验结果表明,该方法能够同时实现三角网格模型的去噪、网格平滑、简化以及孔洞修补,与保特征的均匀化网格平滑算法相比,去噪效果更好,效率更高。     

基于工业CT图像的自适应三维网格模型重建

针对X射线扫描得到的工业CT图像重建三角网格模型存在尖锐特征丢失、狭长三角形和三角面片数量多等问题,提出一种自适应三维网格模型重建算法。首先对图像进行预处理;其次采用八叉树结构确定体元;然后利用二次误差函数（QEF）构建自适应八叉树;最后剖分四边形生成三角网格。使用立方体数据和两组实际扫描的CT数据对所提算法的性能进行验证分析,实验结果表明：所提算法在简化网格的同时仍能保持物体的尖锐特征,减少了狭长三角形的数量。利用所提算法生成三角网格模型的简化率可达90%,简化后网格质量大于0.3的三角网格平均占比为99%,有效地提高了由工业CT图像重建三角网格模型的质量。     

球形阵列自适应网格分层定位算法

刚性球形传声器阵列可以在无空间模糊的条件下进行球谐域数据处理,基于球形阵列的可控波束形成器导向响应功率(SRP)算法定位精度高,但是计算量大,计算效率低。通过将球面致密的全局网格搜索替换为分层搜索策略可以有效减小SRP算法的计算量。提出MRE-SRP算法保持球形阵列SRP定位精度的同时降低计算量,首先通过球谐域MUSIC(SH-MUSIC)算法判断入射声源的数量减小搜索区域;其次将相对熵模型引入球谐域SRP(SH-SRP)定位算法中,提取网格分层前后的信息增益,设计自适应网格选择判据,实现分层多分辨率网格的精准再细分,从而降低计算量。实验验证了所提出算法的性能,结果显示在单双声源定位中,该算法可以实现较高的定位精度,精准选择分层网格,计算量减少75%以上。     

基于数据重建的三角网格模型简化优化方法

由CT切片数据重建得到的三角网格模型常存在数据量大、狭长三角形多等问题,针对这些问题,研究了一种保持特征的高质量三角网格模型的简化优化方法。该方法分为网格简化和网格优化两个阶段。首先,采用基于曲面变化的二次误差度量计算边折叠代价,并按代价值的大小进行迭代的折叠简化,可较好地保持模型表面的特征;其次,通过二阶加权伞算子对简化模型中局部存在的狭长三角形进行优化处理,改善三角网格模型的质量。实验结果表明,该方法能够较好地保持特征区域的细节信息,并可靠地生成高质量、低几何误差的简化模型。     

基于数据重建的三角网格模型简化优化方法

由CT切片数据重建得到的三角网格模型常存在数据量大、狭长三角形多等问题,针对这些问题,研究了一种保持特征的高质量三角网格模型的简化优化方法。该方法分为网格简化和网格优化两个阶段。首先,采用基于曲面变化的二次误差度量计算边折叠代价,并按代价值的大小进行迭代的折叠简化,可较好地保持模型表面的特征;其次,通过二阶加权伞算子对简化模型中局部存在的狭长三角形进行优化处理,改善三角网格模型的质量。实验结果表明,该方法能够较好地保持特征区域的细节信息,并可靠地生成高质量、低几何误差的简化模型。     

三维非均匀不稳定渗流方程的自适应网格粗化算法

将渗透率自适应网格技术应用于三维非均匀不稳定渗流方程的网格粗化算法中,在渗透率或孔隙度变化异常区域自动采用精细网格,用直接解法求解渗透率或孔隙度变化异常区域的压强分布,在其它区域采用不均匀网格粗化的方法计算,即在流体流速大的区域采用精细网格.用该方法计算了三维非均匀不稳定渗流场的压降解,结果表明三维非均匀不稳定渗流方程的三维非均匀自适应网格粗化算法的解在渗透率或孔隙度异常区的压强分布规律与采用精细网格的解非常逼近,在其它区域压强分布规律与粗化算法的解非常逼近,计算速度比采用精细网格提高100多倍.     

基于自适应非结构化网格的VOF方法

本文提出了一种基于自适应非结构化网格的VOF算法,根据相界面网格的相函数值对相界面网格进行自适应细化与合并,通过基于非结构化网格的界面构造方法构造相界面,在适量增加网格单元数量的情况下提高了计算的精度。该方法随着时间及相界面的变化无需重新整体生成网格,算法效率较高。经典算例的验证结果表明,本文自适应网格方法计算得到的结...     

Standard and goal-oriented adaptive mesh refinement applied to radiation transport on 2D unstructured triangular meshes

Standard and goal-oriented adaptive mesh refinement (AMR) techniques are presented for the linear Boltzmann transport equation. A posteriori error estimates are employed to drive the AMR process and are based on angular-moment information rather than on directional information, leading to direction-independent adapted meshes. An error estimate based on a two-mesh approach and a jump-based error indicator are compared for various test problems. In addition to the standard AMR approach, where the global error in the solution is diminished, a goal-oriented AMR procedure is devised and aims at reducing the error in user-specified quantities of interest. The quantities of interest are functionals of the solution and may include, for instance, point-wise flux values or average reaction rates in a subdomain. A high-order (up to order 4) Discontinuous Galerkin technique with standard upwinding is employed for the spatial discretization; the discrete ordinates method is used to treat the angular variable.     

Monotone finite volume schemes for diffusion equations on unstructured triangular and shape-regular polygonal meshes

We consider a non-linear finite volume (FV) scheme for stationary diffusion equation. We prove that the scheme is monotone, i.e. it preserves positivity of analytical solutions on arbitrary triangular meshes for strongly anisotropic and heterogeneous full tensor coefficients. The scheme is extended to regular star-shaped polygonal meshes and isotropic heterogeneous coefficients.     

Modelling atmospheric flows with adaptive moving meshes

An anelastic atmospheric flow solver has been developed that combines semi-implicit non-oscillatory forward-in-time numerics with a solution-adaptive mesh capability. A key feature of the solver is the unification of a mesh adaptation apparatus, based on moving mesh partial differential equations (PDEs), with the rigorous formulation of the governing anelastic PDEs in generalised time-dependent curvilinear coordinates. The solver development includes an enhancement of the flux-form multidimensional positive definite advection transport algorithm (MPDATA) — employed in the integration of the underlying anelastic PDEs — that ensures full compatibility with mass continuity under moving meshes. In addition, to satisfy the geometric conservation law (GCL) tensor identity under general moving meshes, a diagnostic approach is proposed based on the treatment of the GCL as an elliptic problem. The benefits of the solution-adaptive moving mesh technique for the simulation of multiscale atmospheric flows are demonstrated. The developed solver is verified for two idealised flow problems with distinct levels of complexity: passive scalar advection in a prescribed deformational flow, and the life cycle of a large-scale atmospheric baroclinic wave instability showing fine-scale phenomena of fronts and internal gravity waves.     

一种自适应最小范数算法

运用特征子空间方法的关键在于信号子空间或噪声子空间的估计,实际上有些信号的统计特性随时间变化,于是要求得到参数的实时估计值,为此,需要随时根据新的阵列接收数据对信号或噪声子空间进行更新。本文首先分析了一种自适应子空间估计算法,即MALASE(MaximumLikelihoodAdaptiveSubspaceEstimation)算法。然后,把MALASE算法与传统的最小范数(Mini-Norm)高分辨算法相结合,并应用零点跟踪技术,提出了一种自适应Mini-Norm高分辨算法,可用于对时变的信号波达方向(DOA)进行跟踪估计。计算机仿真结果验证了该算法的有效性。     

Efficient algorithm for optimizing adaptive quantum metrology processes

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.     

A sliding Goertzel algorithm for adaptive passive neutralizers

A common method used in tuning adaptive–passive tuned vibration neutralizers is to adjust its resonance frequency to match the excitation frequency, which has the characteristic that the phase angle between its vibrating mass and its support is ?90°. A sliding-Goertzel algorithm is presented and demonstrated for extracting the vibration signals at the frequency of interest. The benefit of using the sliding Goertzel algorithm compared to other methods when used in vibration environments with multiple tones is that additional band-pass notch filtering is not required. This algorithm could also be used for adaptive tuned mass dampers, adaptive Helmholtz resonators, and adaptive quarter-wave tubes.     

An improved algorithm for in situ adaptive tabulation

In situ adaptive tabulation (ISAT) is a proven storage/retrieval method which efficiently provides accurate approximations to high-dimensional functions which are computationally expensive to evaluate. Previous applications of ISAT to computations of turbulent combustion have resulted in speed-ups of up to a thousand. In this paper, improvements to the original ISAT algorithm are described and demonstrated using two test problems. The principal improvements are in the table-searching strategies and the addition of an error checking and correction algorithm. Compared to an earlier version of ISAT, reductions in CPU time and storage requirements by factors of 2 and 5, respectively, are observed for the most challenging, 54-dimensional test problem.     

自适应光学系统的实时模式复原算法

 分析了自适应光学系统中实时模式复原算法的基本原理，建立了一种新型的传感器本征模复原算法。与常用的直接斜率法相比，这种模式复原算法可以有效减小探测噪声对复原计算过程的影响，提高系统的闭环稳定性和校正效果。在61单元自适应光学系统上实现了这种模式复原算法，并在实际大气湍流中对传感器本征模复原算法和直接斜率法进行了实验对比研究。