管道输煤工程中煤浆沉降特性的应用分析

文中对煤浆沉降特性进行了详细分析,结论表明质量分数和粒度是影响煤浆颗粒沉降速度的主要因素。然后,从煤浆沉降特性角度出发,总结了管道输煤工程中煤浆制备、筛检超粗颗粒、煤浆储存、长距离管道输送以及终端脱水等环节需采取的相应措施及要求,为煤浆输送管道安全、经济、可靠地运行提供参考。     

海缆巡线管理系统的设计与实现

为提高海缆的通信保障能力,采用全球定位系统（GPS）和地理信息系统（GIS）技术,设计海缆巡线管理系统。该系统分为数据采集、数据传送和数据中心管理三个组成部分,对巡线员和数据管理员的工作流程进行了具体描述,并分析了系统实现的关键技术;对数据传送的前端硬件系统组成和实现原理进行了详细设计和分析,实现了规范化管理和科学化监督的预期目标。     

内覆防水吸声层的海水消声器声学性能计算

对内部进行了防水吸声处理的海水消声器进行了声学计算,提出了分析此类问题的数值仿真方法：首先运用分层媒质理论对含空腔防水吸声层的声学性能进行计算,利用ANSYS建立海水消声器的三维有限元模型,导入SYSNOISE软件中并将防水吸声层的吸声系数及表面声阻抗作为边界阻抗计算消声器内部声场的声压分布以及传递损失。对某内覆防水吸声层的海水消声器的声学特性进行了数值计算和分析,研究结果对提高含防水吸声结构海水消声器的降噪效果具有重要意义。     

Workspace analysis to generate a collision-free torch path for a ship welding robot

We propose a method to determine the optimal initial location and to generate torch paths for a ship welding robot with 6 degrees of freedom (DOF). The optimal initial location is determined using an objective function, which is set up by combining constraints on the torch posture, manipulability, and the range of each joint angle to avoid collisions. A genetic algorithm (GA) is used to optimize the objective function because it does not require additional derivatives. After the initial location is determined, torch paths are generated by interpolating the starting point, endpoints and torch postures using inverse kinematics. Our method can be applied to automate the welding job for each block during ship building, irrespective of the shape of the robots, by changing the objective function.     

浙江省公路网车辆OD数据库管理系统开发研究

基于公路网OD调查数据现状管理与深度利用的需要,结合浙江省公路网车辆OD数据库管理系统的开发．对系统设计原则与开发平台、功能与框架、技术流程以及关键技术进行详细阐述,可为今后建立更科学和完善的数据库系统提供参考。     

动力定位船舶全速度路径跟踪CB导引自适应闭环控制

针对仅使用槽道推进器提供横向推力的动力定位船舶路径跟踪控制问题,建立慢变环境干扰影响下的非线性船舶数学模型,设计带有自适应干扰补偿的反步控制算法来消除环境干扰的影响。引入平行目标接近(CB)导引算法为跟踪控制生成期望速度矢量信号,通过与所提出的自适应反步控制算法相结合,得到不受船舶驱动特性限制的全速度范围动力定位船舶导引跟踪控制算法,应用李雅普诺夫稳定性理论证明系统跟踪误差渐进收敛到零。仿真结果表明通过调整导引算法参数可以调节船舶跟踪过程表现,并可以得到较好的控制精度。     

透空式防波堤透浪的数值模拟

基于FLUENT求解器,以N-S方程为控制方程,采用动量源方法及追踪自由面的VOF方法,建立了同时具有造波和消波功能的数值波浪水槽。利用该数值水槽对透空堤的透浪、越浪问题进行数值模拟,并与物理模型试验进行比较,结果表明:数值模拟能较好地复演防波堤越浪、透浪过程。     

Aggregating mobile object trajectories: cumulative time geographic density estimation for GPS data

We present new approaches that expand upon the time geographic density estimation (TGDE) framework previously employed to estimate potential path trees. In the past, TGDE metrics have identified possible locations an individual moving object may have passed between, given known origin and destination points. This paper utilizes a new form of TGDE to investigate taxicab GPS traces over a specified time horizon with position ‘gaps’. To this end, we propose a new extension to the TGDE framework, TGDE-C, which is used to determine the cumulative TGDE values for a group of GPS traces, at a given location. These metrics are applied to multiple taxis and allow for time of day analysis. Additionally, we combine these new extensions with existing TGDE metrics that allow us to determine how accessible individual or groups of vehicles are to urban opportunities.     

The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations

In this paper, the crowding effect in a transit vehicle is modeled in a time-expanded network that considers the daily variation in passenger flows. The study models the daily variation of in-vehicle crowding in a real large-scale transit system. A transit assignment for this real network is modeled and implemented by constructing a crowding cost function that follows the valuation of crowding and by using the reliable shortest path finding method. The direct application of the crowding model to a real network for the Utah Transit Authority indicates that crowd modeling with multi-user classes could influence public transportation system planning and affect the revenues of transit agencies. Moreover, the addition of the disutility factor, crowding, does not always appear to cause an increase in disutility for transit users.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.