大跨悬索桥MTMD抖振控制的参数优化

针对悬索桥抖振控制问题,建立有限元模型,应用神经网络和遗传算法对多重调频质量阻尼器(MTMD)进行双参数优化。以某大跨悬索桥为例,利用神经网络改进的谐波合成模拟方法(RBF-WAWS法)对脉动风速进行模拟,并换算成抖振力作用主梁上,通过时程分析及后处理获取主跨跨中横桥向响应值。将响应值的均方差作为优化目标函数,以MTMD总质量、个数及阻尼比作为优化变量和约束条件,采用神经网络拟合目标函数并应用改进的自适应遗传算法进行寻优。结果表明,优化后的MTMD能有效控制悬索桥在脉动风作用下的抖振响应,减振率达48%。提出的理论与计算方法对悬索桥中MTMD的设置及参数选取具有实际工程意义。     

基于物理规律的气垫船气液介质仿真系统研究

针对全垫升气垫船气液介质复杂场景实时再现仿真需求,基于粒子系统原理和OSG渲染平台,考虑气垫船近场波浪高程模型、气垫船垫升系统压力及流量仿真模型以及气垫船气流场特点,提出了一种基于多模型的气垫船航行时气液介质特效仿真框架和实现方法。搭建了该技术的体系结构和实现流程,解决了不同开发环境下建立的异构模型仿真问题,并通过仿真模拟计算验证了该技术的可行性和正确性。仿真结果符合气垫船航行时的气液介质外形特征、作用范围以及气液介质产生的随机性和规律性特征,能快速逼真地实现气垫船航行时气液介质视景特效,对增强气垫船模拟训练效果具有重要的意义。     

智能新能源船舶的概念及关键技术

绿色和智能是当前船舶技术的发展趋势。文章首先提出了智能新能源船舶的概念,并介绍了智能新能源船舶的内涵、特点和系统构成;然后,从船舶航行、动力和推进3个方面分析了智能新能源船舶的关键技术;最后,对智能新能源船舶的未来发展进行展望。研究结果可为船舶的创新研究提供技术支持,引领船舶技术转型升级的发展方向。     

后置定子对喷水推进泵性能的影响规律研究

本文采用RNG k-ε模型对不同后置定子叶数的喷水推进泵性能进行数值模拟,首先采用4119桨进行数值验证,计算结果误差均在3%以内,可有效预报水动力学性能。基于现有模型喷水推进泵,分析其水动力学性能,对比不同叶数定子的喷水推进泵性能,分析其推力性能,发现随着定子叶数增加到11叶,推力增大了5.7%,随着叶数继续增加,推力降低,最终只提高了2.1%;对转子推力进行傅里叶分析,发现转子的频域特性并未发生变化;同时分析尾流场压力脉动和湍动能分布,增加定子叶数可改善尾部流场的压力脉动,降低脉动值,随着叶数继续增大,脉动值又增加,其变化趋势与湍动能分布相同。通过掌握定子对喷水推进泵尾流场的影响特性,奠定喷水推进泵设计基础。     

未知时变扰动和输入饱和下的智能船舶鲁棒非线性控制

复杂海况下环境多变并且船舶具有多耦合、强非线性的特点,针对智能船舶定位控制问题,考虑在未知时变扰动和输入饱和约束之下船舶的定位控制问题,结合非线性扰动观测器提出一种带辅助动态系统的鲁棒非线性控制算法。通过Lyapunov理论证明了所提出的非线性扰动观测器与控制器结合后闭环系统的稳定性和信号的一致最终有界性。利用非线性扰动观测器对环境中存在的海浪扰动进行有效的估计处理。最后,通过仿真验证了所提出的控制算法不仅能保证船舶期望的位置和艏向,而且提高了控制速度,具有较好的控制性能。     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

Multi‐objective highway alignment optimization using a genetic algorithm

The available highway alignment optimization algorithms use the total cost as the objective function. This is a single objective optimization process. In this process, travel‐time, vehicle operation accident earthwork land acquisition and pavement construction costs are the basic components of the total cost. This single objective highway alignment optimization process has limited capability in handling the cost components separately. Moreover, this process cannot yield a set of alternative solutions from a single run. This paper presents a multi‐objective approach to overcome these shortcomings. Some of the cost components of highway alignments are conflicting in nature. Minimizing some of them will yield a straighter alignment; whereas, minimizing others would make the alignment circuitous. Therefore, the goal of the multiobjective optimization approach is to handle the trade‐off amongst the highway alignment design objectives and present a set of near optimal solutions. The highway alignment objectives, i.e., cost functions, are not continuous in nature. Hence, a special genetic algorithm based multi‐objective optimization algorithm is suggested The proposed methodology is demonstrated via a case study at the end.     

The Alternative Motor Fuels Act, alternative-fuel vehicles, and greenhouse gas emissions

The corporate average fuel economy (CAFE) standard is the major policy tool to improve the fleet average miles per gallon of automobile manufacturers in the US. The Alternative Motor Fuels Act (AMFA) provides special treatment in calculating the fuel economy of alternative-fuel vehicles to give manufacturers CAFE incentives to produce more alternative-fuel vehicles. AMFA has as its goals an increase in the production of alternative-fuel vehicles and a decrease in gasoline consumption and greenhouse gas emissions. This paper examines theoretically the effects of the program set up under AMFA. It finds that, under some conditions, this program may actually increase the production of fuel-inefficient gasoline vehicles, gasoline consumption and greenhouse gas emissions.     

超深基坑施工中实施抽降深层承压水的措施

介绍了上海轨道交通10号线龙柏新村站—龙溪路站区间隧道中间井抽降第三砂层(⑨层)承压水的试验过程,分析了试验结果,并进行了数值模拟。施工实践表明:少量、短时间、控制性抽降⑨层承压水,对周边环境影响较小。