中意车前围改型设计及模型制作

介绍了中意车前围改型的全部过程，对概念设计、全尺寸模型的制作、涂装评审等方面作了全面的阐述，以两个1：1模型为例介绍了三维造型中经常遇到的难点问题和解决方法。     

具有迟滞模拟记忆功能的奥迪盘式制动器可变线性模型

本文以奥迪１００盘式制动器为研究对象，建立了一种可模拟制动压力－力矩响应迟清点特性的可变线性模型，首先，介绍在ＪＦ－１３２型汽车制动器试验台上进行防抱制动器模拟试验的方法，其次，在获得大量试验数据的基础上，运用曲线拟合和优化技术，建立制动压力－力矩关系的线性模型，心滞模型为线笥模型，最后，在相同制动压力输入的前提下，把模型的理论计算民试验结果进行对比分析了模型的有效性和精确性。     

盘式制动器制动尖叫计算模型的建立

借助于有限元和模态综合技术，建立了盘式制动器制动尖叫的摩擦耦合模型。通过复特征分析，得到了对应于每阶段动模态的阻尼与频率，模态阻尼值揭示了哪 些模态不稳定并有可能产生尖叫；最后运用耦合模型研究了摩擦系数和子结构模态对制动尖叫的影响。     

轮胎垂直振动动力学模型

本文提出一种自适应轮膜模型参数的简易测定方法，给出轮胎垂直振动动力学方程及相应的求解方法，并进行计算分析与试验测定。将分析结果与通常的点接触式模型相比较，对点接触式模型及本文所建立的自适应模型做了综合评价，结果表明，本模型的计算结果是令人满意的。     

采用模型设计和模型试验方法研制大型泵

本文对采用模型设计和模型试验方法研制大型离心泵的计算理论，结构设计和材料选用等问题进行了探讨，确认了只有在正确运用相似计算公式的前提下才能保证相似设计可靠性的观点，验证了叶轮进口截面几何形状对离心泵汽蚀性能和效率值的影响，并提出了国内现有的能较好满足船艇货油泵运转条件的材料组合建议方案。     

深圳地铁14号线黄木岗站三线换乘方案研究

深圳地铁14号线黄木岗站与既有7号线、近期规划24号线黄木岗站形成地下三线换乘车站，车站周边环境复杂。如何综合各种边界条件形成建筑功能完善，结构经济安全，同时又对周边环境影响最小的方案是本站研究的重点，特别是车站站位及换乘功能的研究。方案设计过程中，通过逐一梳理地面建筑、地下建(构)筑物、地面交通、片区规划、工程地质条件、客流预测、车站换乘功能等边界条件，总结黄木岗站立交桥安全、交通疏解、管线改迁、结构工法、施工器具选择、车站站位及换乘功能等重难点，采取逐项突破的方法，最终推演出“干”字形节点换乘方案，其中14号线采用地下3层双叠侧式车站，与7号线形成平行同台换乘，与24号线形成台-台换乘。     

均匀设计在尾压浪板设计中的应用

本文利用均匀设计法设计了一个尾压浪板系列,从中优化出一个方案,解决排水型船加装尾压浪板在巡航速度附近减阻的问题,减阻效率达裸体阻力４％以上,对尾压浪板在排水型船上减阻的机理进行了初步分析,得到了船后体压力 尾压浪板对主船体阻主要原因这一结论。     

高速公路局部路线方案动态比选研究

在高速公路初步设计阶段,根据对路线方案后期运营效益定性分析,识别出不同路线方案主要后期运营成本及效益,通过效益调节因子计算路线方案综合效益,按照动态经济比选思路,对各方案工程投资及效益进行定量分析,为决策者选择更优的路线方案提供数据支撑。     

Ant Colony Routing algorithm for freeway networks

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.     

Using repeated cross-sectional travel surveys to enhance forecasting robustness: Accounting for changing mode preferences

Transportation system capacity and performance, urban form and socio-demographics define the influences and constraints conditioning the preferences of urban residents for different transport modes. Changes in characteristics of urban areas are likely to lead to changes in preferences for alternative modes of transport over time; as a consequence, statistical models to forecast mode choice need to be sensitive to both purposeful changes to urban systems as well as exogenous shocks. We make use of the 1996, 2001 and 2006 household surveys conducted in the Greater Toronto and Hamilton Area to study mode preference evolution and model forecasting performance. These repeated cross-sectional household surveys provide an opportunity to investigate aggregate structural changes in commuting mode preferences over time, in a manner sensitive to changes in the urban area. We focus on commuting mode choices because these trips are prime determinants of peak period congestion and peak spreading. We then address how to combine the three cross-sections econometrically in a robust way that allows for use of a single mode choice model across the entire period. Using independent data from 2012, we are able to compare the individual year and combined models in terms of forecasting performance to demonstrate the combined model’s more robust forecasting performance into the future.