基于虚拟节点连接的道路网络模型

利用传统的基于节点连接的道路网络模型来表达真实世界中的道路网络,其存在的缺陷越来越突出,大大降低了导航系统的鲁棒性。为了消除这些缺陷,提出一个新的道路网络模型,即基于虚拟节点连接的道路网络模型;其虚拟节点是汇交路段的各个端点所围成的区域,具有真实道路路口的形状。与传统模型相比,该模型更能表达真实世界道路网络中的交通流,所以它能更好地满足地图匹配理论要求。另外,该模型能很好地表达真实道路路口参数,所以它在交通规划、交通管理和交通流模拟中都有着十分广阔的应用前景。     

基于物理统一存储大规模数字导航地图道路网拓扑自动生成算法

道路网络的拓扑信息是GIS进行空间分析,如最优路径、地图匹配等算法的数据基础。目前,获取和建立道路网络的拓扑信息非常繁琐,不仅费时、费力,并且容易出错。采用逻辑分幅物理统一的存储策略,在探讨了拓扑生成的一般算法的前提下,提出了大规模超大规模数字地图自动生成道路网络拓扑关系的步骤和算法。该算法采用网格索引检索每个子图的元素,用hash索引映射实体ID和实体对象信息,并将整图的拓扑信息生成转化为对每个子图的拓扑求取,并对跨子图道路拓扑求解特别讨论。然后,对算法复杂度进行了分析,并且通过建立不同道路数的多个虚拟道路网络子图对算法性能进行了测试和比较。最后用本算法跟踪处理了南京市道路网络（部分）,并给出了结果。本算法在保留地理数据完整性的前提下,解决了常规方法的内存限制,并且具有准线性的运算代价,并能够自动恢复数据处理。     

一种桁架结构智能生成与分析系统

建立了桁架结构的智能生成与分析系统,可以自动完成桁架结构的布局选型、拓扑生成以及分析计算等系列过程. 主要通过结点布置和有选择的杆件连接实现结构的布局选型,采用拓扑变化法完成结构的自动生成,并利用基内力阵进行结构的分析计算. 经算例分析表明,该系统性能良好,使用方便,具有智能、自适应的特点,是辅助完成结构拓扑和布局优化设计的有力工具之一.     

一种RCM有限元带宽优化改进算法

应用RCM(Reverse Cuthill-Mckee)算法进行带宽优化时存在优化结果不稳定的问题,通过对算法进行系统分析发现,正序排列过程中非完全依靠节点之间的拓扑关系是问题的关键。本文在考虑层、联结度判据基础上,通过新增列高和判据进行节点正序排列,解决了RCM算法存在的问题,通过实际结构算例验证了改进后的RCM算法的稳定性,并获得了列高和更小的优化方案,实现了节省计算机内存和提高运算效率的目的。     

一种用于分散化滤波的融合算法及其在组合导航系统中的应用

提出了一种局部估计相关时的最优融合算法，其特点是加权矩阵是对角阵，即全局估计中的各分量分别是局部估计中相应分量的线性组合，在此融合算法中局部估计是最优的，并且不存在由主滤波器向子滤波器的信息反馈，因此具有较好的客措性。给出了最优加权矩阵的存在性和唯一性的证明，并通过与现有类似融合算法的对比，表明该算法具有计算量小的优点。最后，以组合导航系统为例作了仿真计算，结果表明该融合算法是可行的。     

求解拓扑优化问题的一种移动渐进-小波混合算法

利用小波多分辨分析的特征,提出了一种"移动渐进-小波混合算法",通过小波分解与回复,能够消除移动渐进算法应用于拓扑优化中产生的数值不稳定现象.数值算例表明:这种混合算法相对于传统的移动渐进算法有更好的计算稳定性和收敛性.     

一种基于应力的双方向结构拓扑优化算法

基于传统的渐进结构优化方法,提出了一种基于应力的双方向渐进拓扑优化算法。该方法是对传统方法和目前的双方向法的改进和完善。算例表明该方法能避免目前的双方向法具有的解的振荡问题,具有更高的可靠性,能获得更佳的拓扑结构。     

基于CWPA模型的最优平滑算法性能及其应用

针对线性高斯系统的平滑问题,分析了RTS固定区间平滑与双滤波器固定区间平滑两种算法,提出了一种滤波存储数据更少的RTS平滑新算法。结合平面内的运动追踪问题,基于二维CWPA模型,仿真分析了卡尔曼滤波、RTS固定区间平滑以及双滤波器平滑算法的估计性能。仿真结果表明,两种固定区间平滑算法的估计效果等效,精度均优于卡尔曼滤波,对于实际问题中固定区间平滑算法的选用具有一定的参考价值。最后,结合双滤波器结构提出了一种基于双平滑器的舰载武器惯导传递对准精度评估方法,结果表明新方法相比于单一的平滑算法,可以获取更优的综合平滑性能,特别提升了水平姿态对准误差的平滑估计性能。     

一种基于双前沿推进的边界层网格生成算法

高雷诺数粘性流动模拟对边界层内的网格正交性有特殊要求.对于复杂外形,这类问题的网格自动化生成十分困难.面向该问题,提出一种双前沿推进思想,并形成一种面向复杂几何外形的边界层网格全自动生成算法.结合多种网格技术处理局部几何特征以保证边界层网格的质量.双前沿推进思想同时适用于多块结构网格和混合网格的边界层网格生成.多个模型...     

应用于非凸设计区域桁架拓扑优化的一种基结构生成方法

为了提高桁架拓扑优化设计中非凸区域生成基结构的效率和准确性,介绍了一种基于几何和网格信息生成初始基结构的方法。通过直接将已有几何边界作为碰撞检测的标准,避免对原有边界扩展或构建限制区域,简化了基结构生成过程并可以避免杆件识别不准确的问题;通过减少需要与凹边界碰撞检测的杆件个数来提升整体基结构的生成效率;最后,针对已生成的基结构进行基于塑性准则的桁架拓扑优化。     

一种新的钢筋薄膜组合单元及其网格自动生成

提出了一种新的钢筋薄膜组合单元模型和薄膜单元结点信息生成技术。该模型能考虑任意方向的钢筋薄膜,使得单元网格的划分不必考虑钢筋的方位,克服了现有钢筋薄膜组合单元所存在的缺陷。应用本文提出的薄膜结点生成技术,只要给出钢筋薄膜四个角点的坐标,即可自动形成薄膜单元在组合单元中的信息。算例表明该模型方便可行,单元划分效率高,且能达到相当的精度。     

模型预测前向神经网络算法及其在组合导航中的应用

针对系统误差的不确定性可能会引起滤波精度降低或发散的问题,提出一种新的基于模型预测滤波的前向神经网络算法。首先,采用模型预测滤波估计网络在正向传递过程中的模型误差,并对其进行修正,以弥补模型误差对隐含层权值更新的影响;然后,利用模型预测滤波为神经网络提供精确的训练样本,学习待估计系统的非线性关系。将提出的算法应用于SINS/CNS/BDS组合导航系统,并与扩展卡尔曼滤波进行比较,仿真结果表明,提出的算法得到的姿态误差、速度误差和位置误差分别在[-0.25′,+0.25′]、[-0.05 m/s,+0.05 m/s]和[-5 m,+5 m]以内,滤波性能明显优于扩展卡尔曼滤波算法,表明该算法能提高组合导航定位的解算精度。     

A hierarchical model for rate-dependent polycrystals

A hierarchical model of a polycrystalline aggregate of rigid viscoplastic grains is formulated, and a robust and efficient computational algorithm for its solution is proposed. The polycrystalline aggregate is modeled as a binary tree. The leaves of the binary tree represent grains, and higher tree nodes represent increasingly larger sub-aggregates of grains. The root of the tree represents the entire polycrystalline aggregate. Velocity and traction continuity are enforced across the interface between the children of each non-leaf node in the binary tree. The hierarchical model explicitly models intergranular interactions but is nevertheless comparable in computational effort to the mean field models of polycrystal plasticity. Simulations of tensile, compressive, torsional, and plane strain deformation of copper lead to predictions in good agreement with experiments, and highlight the interconnection between grain deformations and intergranular constraints. It is inferred from the results that a hybrid mean field/hierarchical model represents a computationally efficient methodology to simulate polycrystal deformation while accounting for intergranular interactions.     

A micromechanically motivated diffusion-based transient network model and its incorporation into finite rubber viscoelasticity

This paper presents the development of a physical-based constitutive model for the representation of viscous effects in rubber-like materials. The proposed model originates from micromechanically motivated diffusion processes of the highly mobile polymer chains described within the formalism of Brownian motion. Following the basic assumption of accounting for the elastic and the viscous effects in rubber viscoelasticity by their representation through a separate elastic ground network and several viscous subnetworks, respectively, the kinetic theory of rubber elasticity is followed and extended to represent also the viscous contribution in this work. It is assumed that the stretch probability of certain chain segments within an individual viscous subnetwork evolves based on the movement of the chain endpoints described by the Smoluchowski equation extended in this work from non-interacting point particles in a viscous surrounding to flexible polymer chains. An equivalent tensorial representation for this evolution is chosen which allows for the closed form solution of the macroscopic free energy and the macroscopic viscous overstress based on a homogenization over the probability space of the introduced micro-objects. The resulting model of the viscous subnetwork is subsequently combined with the non-affine micro-sphere model and applied in homogeneous and non-homogeneous tests. Finally, the model capacity is outlined based on a comparison with in the literature available experimental data sets.     

A simulation of the cascading failure of a complex network model by considering the characteristics of road traffic conditions

Using the dual method, we start with a traditional road traffic network with a constructed logic network with small-world characteristics and construct the complex network of road traffic. After analyzing and comparing with other complex networks, the time delay, restorative, and other characteristics are presented for the complex network of road traffic, and then, the cascading failure model of the complex network is simulated. The simulation results show that using different time delays, an incident dissipation factor and load capacity can reasonably avoid a cascading failure, and they can remove its effects. In addition, our results provide value and guidance for building a road traffic network that prevents and removes the cascading failure of a road network.

     

A fully anisotropic hierarchical hybrid cellular automaton algorithm to simulate bone remodeling

Computational models of the bone remodeling process have been utilized to further our understanding of the adaptation of bone architecture to changes in its mechanical environment. The hierarchical hybrid cellular automata (HHCA) algorithm is a multi-scale approach for the simulation of the adaptation of bone. Currently, this remodeling algorithm utilizes the apparent material properties of the trabecular architecture. The objective of this work is to increase the fidelity of the HHCA algorithm by incorporating the local anisotropic properties of these structures. Preliminary analyses display improved efficiency and a more consistent material distribution when incorporating anisotropic properties into the HHCA methodology.     

NONSMOOTH MODEL FOR PLASTIC LIMIT ANALYSIS AND ITS SMOOTHING ALGORITHM

By means of Lagrange duality theory of the convex program, a dual problem of Hill's maximum plastic work principle under Mises' yield condition has been derived and whereby a non-differentiable convex optimization model for the limit analysis is developed. With this model, it is not necessary to linearize the yield condition and its discrete form becomes a minimization problem of the sum of Euclidean norms subject to linear constraints. Aimed at resolving the non-differentiability of Euclidean norms, a smoothing algorithm for the limit analysis of perfect-plastic continuum media is proposed. Its efficiency is demonstrated by computing the limit load factor and the collapse state for some plane stress and plain strain problems.     

A quadratic hierarchical Bayesian dynamic prediction model for infrastructure maintenance

The main objective of this study is the development of a correlation model in dynamic Bayesian belief networks (DBBNs) followed by an inverse economic analysis. This is based on a quadratic hierarchical Bayesian inference prediction method using Markov chain Monte Carlo simulations. The developed model is implemented to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given 10 years maintenance data. The simulated risk prediction under decreased serviceability conditions gives posterior distributions based on a prior distribution and likelihood data updated from annual maintenance tasks. Compared with a conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to the measured data. Finally, the developed inverse DBBN analysis method allows forecasts of future performance and the financial management of complex infrastructures by providing the sensitivity of serviceability and risky factors to the maintenance budgets of structural components and the overall system.     

A new car-following model accounting for varying road condition

In this paper, we develop a new car-following model with consideration of varying road condition based on the empirical data. Firstly, we explore the effects of road condition on uniform flow from analytical and numerical perspectives. The results indicate that road condition has great influences on uniform flow, i.e., good road condition can enhance the velocity and flow and their increments will increase when road condition becomes better; bad road conditions will reduce the velocity and flow and their reductions will increase when road condition turns worse. Secondly, we study the effects of road conditions on the starting and braking processes. The numerical results show that good road condition will speed up the two processes and that bad road condition will slow down the two processes. Finally, we study the effects of road condition on small perturbation. The numerical results indicate that the stop-and-go phenomena resulted by small perturbation will become more serious when the road condition becomes better.