具有轴承不对中的多跨柔性转子系统非线性动力学研究

主要研究了具有不对中轴承支承的柔性多转子耦合系统的动力学建模和非线性动力学行为.首先在短轴承假设、小轴承的不对中量和圆盘不平衡量等几个基本假设条件下,考虑了转子的柔度、不对中轴承的非线性油膜力和圆盘的不平衡等因素后,建立了一个具有轴承不对中的10自由度多跨转子系统非线性动力学模型；最后采用数值方法研究了系统的非线性动力...     

基于Kelvin模型的粘弹性浅拱的动力稳定性

研究了外激励作用下非线性粘弹性浅拱的动力行为．通过达朗贝尔原理和欧拉一贝努利假定建立了浅拱的动力学控制方程,其中采用Kelvin模型来表示非线性粘弹性材料的本构关系,并利用Galerkin法将方程简化用于数值分析．分析了粘弹性材料参数、浅拱矢高、外激励幅值和频率对系统分岔和混沌等非线性动力学行为的影响,结果表明各种参数条件下系统的非线性动力特性十分复杂,周期运动、准周期运动和混沌运动窗口在一定条件下交替出现．     

水下机器人粘性类水动力数值计算方法研究

通过对通用流体动力学仿真软件CFX的研究,提出了一套水下机器人粘性类水动力的数值计算方法．该方法采用标准k-ε湍流模型计算位置力系数,采用标准k-ω湍流模型计算旋转力系数及其它耦合水动力系数．对“CR-02”6000 m自治水下机器人的计算表明,通过这种方法获得的水动力系数具有较高的精度,可以满足水下机器人方案设计阶段的操纵性设计、运动预报和仿真等需求．     

基于ANSYS/LS-DYNA的加筋板入水冲击仿真

采用ANSYS/LS-DYNA有限元分析软件数值模拟入水冲击问题,重点研究平板类结构体入水冲击数值仿真和冲击荷载作用下的结构动力响应;首先采用ANSYS前处理器建立平板和十字加筋板入水冲击数值模型,然后通过LS-DYNA求解器数值计算入水冲击的动力响应,并分析比较了各个模型计算得到的位移、等效应力、压力等变量变化,最后得出以下结论,加筋板具有吸收更多人水冲击能量的作用,且耐撞性更强,结构更偏于安全.     

基础激励下桥梁斜拉索的非线性振动

在考虑拉索垂度的情况下建立了深圳湾公路大桥的动力学模型，利用有限元软件模拟了地震、风、车辆等荷载作用于桥梁结构时桥面及桥塔索锚固点的位移响应，以及全桥整体动力特性和索连接点的振动解，从而真实反映出基础激励对索的初始扰动，进而研究索在最不利荷载作用情况下谐波共振和参数振动的特性，并在此基础上分析了激励频率与拉索固有频率比的匹配关系，得出了激励幅值、初始索力、模态阻尼比、拉索倾角等参数对拉索非线性振动的影响．     

基于ANSYS/LS-DYNA的返回舱海面撞水动力研究

在理论分析刚形体垂直撞水动力特性的基础上,分别推导出基于von Karman理论和通用Wagner理论的圆球底返回舱撞水冲击过载公式.然后借助ANSYS/LS-DYNA动力显式程序中ALE算法的优势,进行返回舱海向垂直撞水动力数值仿真;针对数值仿真结果和理论分析预报结果的比较分析来验证数值仿真的可行性,同时给出数值计算弹性体模型垂直撞水的撞水冲击过载;结果表明:采用ANSYS/LS-DYNA的ALE算法可以有效地数值模拟返回舱撞水动力特性,为进一步控制返回舱海面回收以及数值分析撞水动力特性提供有力的技术支持,且大大节约了试验经费.     

长江感潮河段溢油风险评价体系构建及应用

长江感潮河段水动力条件复杂,对该水域溢油事故缺少相应的风险评价指标体系。为科学评价长江感潮河段溢油风险,从溢油事故对上游水体、敏感点、敏感区影响 3 个要素出发,构建风险评价指标体系,包括危害区特征、危害期特征共 23 个指标。应用该指标体系,基于 EFDC 二维水动力-油粒子模型,以长江南京段水域某码头溢油事故为研究对象,开展溢油风险评价,模拟计算涨急、落急、涨憩、落憩 4 种典型事故发生工况下的风险评价指标值。结果表明;事故发生在落急或涨憩时刻时不会对上游水域产生影响,发生在落憩时刻时对事故点上游水域影响距离最远,发生在落急时刻时将最快影响到下游的敏感点和敏感区,持续时间与水动力特性密切相关。建立的评价指标体系可以较为全面地刻画溢油风险影响特征,为长江感潮河段溢油风险评价提供量化指标和评价工具。     

基于模糊T-S模型的舵减摇系统的观测器设计

针对带有非线性水动力系数的船舶运动系统,基于模糊T-S模型提出了舵减摇系统的模糊观测器设计方案。将船舶运动方程中的非线性水动力系数看作随船速变化的不确定项来设计模糊规则,建立了船舶舵减摇系统的模糊T-S模型,设计了模糊观测器,并用李雅普诺夫稳定性理论和线性矩阵不等式方法得到了此观测器的误差系统渐近稳定的条件。仿真结果表明,所提出的模糊观测器适用于带有非线性水动力系数的船舶运动系统,能够获得比较理想的观测效果。     

两栖装甲车辆水上射击位移规律仿真研究

两栖战备车辆水上射击动力学模型研究,以某轮式自行火炮为例,在车辆左右完全对称、静水条件纵中面内射击等假设的基础上,对两栖武器水上射击动力学模型进行了合理简化。采用水动力无因次体系法对该自行火炮的水动力试验数据进行合理转换,近似得到轮式自行火炮的水动力参数;运用MATLAB对轮式自行火炮水上射击动力学方程进行了解算,获得了静水条件下某轮式自行火炮水上射击位移特性数值计算结果,最后对计算结果进行了评估与分析,得到了火炮的水上射击位移规律。     

船舶运动仿真模型

船舶运动形成复杂的流场,难以建立准确的数学模型,因此对其操纵性能进行仿真和理论十分困难。该文根据实验数据,用神经网络辨识方法建立了某船的水动力模型,实践证明：用神经网络辨识方法所建立的数学模型能够真灾地反映船舶的运动特性。该文为研究非线性的水动力问题提供了一种行之有效的方法。     

基于多尺度空间划分与路网建模的城市移动轨迹模式挖掘

针对城市移动轨迹模式挖掘问题展开研究, 提出移动全局模式与移动过程模式相结合的挖掘方法, 即通过移动轨迹的起始位置点--终点位置点 (Origin-destination, OD点) 与移动过程序列分别进行移动全局模式与过程模式的发现. 在移动全局模式发现中, 提出了弹性多尺度空间划分方法, 避免了硬性等尺度网格划分对密集区域边缘的破坏, 同时增强了密集区域与稀疏区域的区分能力.在移动过程模式发现中, 提出了基于移动轨迹的路网拓扑关系模型构建方法, 通过路网关键位置点的探测抽取拓扑关系模型.最后基于空间划分集合与路网拓扑模型对原始 移动轨迹数据进行序列数据转换与频繁模式挖掘. 通过深圳市出租车历史 GPS 轨迹数据的实验结果表明, 该方法与现有方法相比在区域划分、数据转换等方面具有更好的性能, 同时挖掘结果语义更为丰富, 可解释性更强.     

Effectiveness of a participatory modeling effort to identify and advance community water resource goals in St. Albans,Vermont

Natural resource managers face complex challenges in addressing non-point source water pollution. A participatory modeling approach was applied in the St. Albans Bay watershed to identify the most effective phosphorus control options to achieve the load reductions required by the Lake Champlain Phosphorus Total Maximum Daily Load (TMDL). Stakeholders participated in the collection of data in the watershed, model creation, development of policy scenarios, and interpretation of model results. The participatory modeling approach employed in this study led to the identification of new solutions to an old water resource problem regarding phosphorus loads to streams and St. Albans Bay. The modeling process provided a perceived neutral atmosphere for discussing water pollution issues that have historically been divisive and provided participants with greater understanding of local environmental issues and reduced historic conflict among actors. This study highlights the importance of considering the dynamics of social and technical factors in the use of modeling in natural resource planning processes. The approach led to stakeholder agreement about problems and potential solutions generated in the modeling process. As the process ended, local decision makers were moving forward to implement solutions identified to be most cost-effective.     

基于整数规划的单贝多港(SBMP)配载模型研究

大型集装箱船舶配载问题是一个复杂的组合优化问题，而且是一个NP问题。单贝多港(SBMP)配载体现了船舶配载的基本特性，是船舶配载的主要组成部分，也是本文讨论的重点。本文首先简化问题，归纳单贝单港配载特点，采用整数规划方法描述单贝单港配载问题，并以最少阻塞箱为目标，建立单贝单港配载规划模型；然后基于所建立的模型，对不同港口的配载产生不同配载方案，使整个船舶单贝在航行中以最少倒箱数为目标，提出了针对不同港口的多阶段决策模型。本文最后给出了一个模型实例。     

Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases

Spatio-temporal databases deal with geometries changing over time. In general, geometries cannot only change in discrete steps, but continuously, and we are talking about moving objects. If only the position in space of an object is relevant, then moving point is a basic abstraction; if also the extent is of interest, then the moving region abstraction captures moving as well as growing or shrinking regions. We propose a new line of research where moving points and moving regions are viewed as 3-D (2-D space+time) or higher-dimensional entities whose structure and behavior is captured by modeling them as abstract data types. Such types can be integrated as base (attribute) data types into relational, object-oriented, or other DBMS data models; they can be implemented as data blades, cartridges, etc. for extensible DBMSs. We expect these spatio-temporal data types to play a similarly fundamental role for spatio-temporal databases as spatial data types have played for spatial databases. The paper explains the approach and discusses several fundamental issues and questions related to it that need to be clarified before delving into specific designs of spatio- temporal algebras.     

Motion interaction field for detection of abnormal interactions

This paper proposes a new method for the modeling of interactions among objects and for the detection of abnormal interactions in a video. To model interactions among multiple moving objects, we design a motion interaction field (MIF) that is similar to a water waveform generated by multiple objects moving on the surface of water and that describes the intensity of motion interaction in a video. Using the MIF, we establish a framework to detect abnormal interactions, which consists of rule-based decision about regions of interest and dictionary learning-based anomaly decision for these regions. The regions of interest are determined as the regions remaining after filtering out collision-free regions that are recognized clearly to be normal by a rule-based decision based on the shape of MIF. The MIF values in these regions are then used to construct spatiotemporal features for the detection of abnormal interactions by a dictionary learning algorithm with sparse representation. In the experiments, the effectiveness of the proposed method is validated through quantitative and qualitative evaluations with three datasets containing typical abnormal interactions such as car accidents, crowd riots, and uncontrolled fighting.     

Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection

Foreground segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated video surveillance, human-machine interface, and optical motion capture. Many models have been introduced to deal with the problems of modeling the background and detecting the moving objects in the scene. One of the successful solutions to these problems is the use of the well-known adaptive Gaussian mixture model. However, this method suffers from some drawbacks. Modeling the background using the Gaussian mixture implies the assumption that the background and foreground distributions are Gaussians which is not always the case for most environments. In addition, it is unable to distinguish between moving shadows and moving objects. In this paper, we try to overcome these problem using a mixture of asymmetric Gaussians to enhance the robustness and flexibility of mixture modeling, and a shadow detection scheme to remove unwanted shadows from the scene. Furthermore, we apply this method to real image sequences of both indoor and outdoor scenes. The results of comparing our method to different state of the art background subtraction methods show the efficiency of our model for real-time segmentation.     

Modeling and querying moving objects in networks

Moving objects databases have become an important research issue in recent years. For modeling and querying moving objects, there exists a comprehensive framework of abstract data types to describe objects moving freely in the 2D plane, providing data types such as moving point or moving region. However, in many applications people or vehicles move along transportation networks. It makes a lot of sense to model the network explicitly and to describe movements relative to the network rather than unconstrained space, because then it is much easier to formulate in queries relationships between moving objects and the network. Moreover, such models can be better supported in indexing and query processing. In this paper, we extend the ADT approach by modeling networks explicitly and providing data types for static and moving network positions and regions. In a highway network, example entities corresponding to these data types are motels, construction areas, cars, and traffic jams. The network model is not too simplistic; it allows one to distinguish simple roads and divided highways and to describe the possible traversals of junctions precisely. The new types and operations are integrated seamlessly into the ADT framework to achieve a relatively simple, consistent and powerful overall model and query language for constrained and unconstrained movement.     

Oporto: A Realistic Scenario Generator for Moving Objects

The spatio-temporal database research community has just started to investigate benchmarking issues. On one hand we would rather have a benchmark that is representative of real world applications, in order to verify the expressiveness of proposed models. On the other hand, we would like a benchmark that offers a sizeable workload of data and query sets, which could obviously stress the strengths and weaknesses of a broad range of data access methods. This paper offers a framework for a spatio-temporal data sets generator, a first step towards a full benchmark for the large real world application field of smoothly moving objects with few or no restrictions in motion. The driving application is the modeling of fishing ships where the ships go in the direction of the most attractive shoals of fish while trying to avoid storm areas. Shoals are themselves attracted by plankton areas. Ships are moving points; plankton or storm areas are regions with fixed center but moving shape; and shoals are moving regions. The specification is written in such a way that the users can easily adjust generation model parameters.     

Modeling sediment resuspension and transport induced by storm wind in Apalachicola Bay, USA

Sediment is an important environmental factor for aquatic ecosystem and oyster productivities of Apalachicola Bay located in Florida, USA. Based on the data analysis in this study, surface wind speed is highly correlated to the turbidity of water column, which results from sediment resuspension and transport in the Apalachicola Bay. In this paper, an application of a 3D sediment transport model to predict the wind-induced sediment transport in Apalachicola Bay is described. The sediment model is coupled with a 3D hydrodynamic module in the Environmental Fluid Dynamics Code (EFDC) model that provides information on estuarine circulation and salinity transport under normal temperature conditions. The hydrodynamic model was calibrated with field observations of water levels and salinity. The sediment transport model solves the transport equation with sources and sinks terms to describe sediment deposition and resuspension. The coupled hydrodynamic and sediment transport models were used to investigate wind-induced total suspended sediments (TSS) resuspension and transport in the bay. For the period June 1–July 30, 2005 two storm events with strong winds gave model results of TSS concentrations that compared well with the field observations. Model simulations reasonably reproduce the sudden increase of sediment concentrations during the storm events. Maximum sediment concentrations in the bay during the two storm events were 10 times or more than those in the pre-storm conditions. Spatial sediment transport from model simulations indicate active sediment resuspension and transport near areas of highly productive oyster beds. The model predictions of TSS and salinity can be used as inputs to an oyster dynamic model (Wang, H., Huang, W., Harwell, M., Edmiston, L., Johnson, E., Hsieh, P., Milla, K., Christensen, J., Stewart, J., Liu, X., 2008. Modeling eastern oyster population dynamics in response to changing environment in Apalachicola Bay, Florida. Journal of Ecological Modeling 211, 77–89) to support the ecological study of oyster growth and mortality in the aquatic ecosystem of Apalachicola Bay.     

Entropy based region selection for moving object detection

This article addresses a problem of moving object detection by combining two kinds of segmentation schemes: temporal and spatial. It has been found that consideration of a global thresholding approach for temporal segmentation, where the threshold value is obtained by considering the histogram of the difference image corresponding to two frames, does not produce good result for moving object detection. This is due to the fact that the pixels in the lower end of the histogram are not identified as changed pixels (but they actually correspond to the changed regions). Hence there is an effect on object background classification. In this article, we propose a local histogram thresholding scheme to segment the difference image by dividing it into a number of small non-overlapping regions/windows and thresholding each window separately. The window/block size is determined by measuring the entropy content of it. The segmented regions from each window are combined to find the (entire) segmented image. This thresholded difference image is called the change detection mask (CDM) and represent the changed regions corresponding to the moving objects in the given image frame. The difference image is generated by considering the label information of the pixels from the spatially segmented output of two image frames. We have used a Markov Random Field (MRF) model for image modeling and the maximum a posteriori probability (MAP) estimation (for spatial segmentation) is done by a combination of simulated annealing (SA) and iterated conditional mode (ICM) algorithms. It has been observed that the entropy based adaptive window selection scheme yields better results for moving object detection with less effect on object background (mis) classification. The effectiveness of the proposed scheme is successfully tested over three video sequences.