基于IWO算法的轴系—基座—壳体系统声辐射优化研究

针对轴系-基座-壳体结构复杂,对于激振力引起的结构表面声辐射尚未形成一套有效的计算和优化方法,文章尝试通过功率流有限元和声学边界元方法,对系统声优化问题进行了研究。在直线校中状态下,用相应单元模拟系统中减振器、隔振器和吸振器,建立轴系-基座-壳体系统有限元模型,利用有限元转子动力学,计算系统某特定工况下频率响应。在此基础上采用IWO算法,以各减振单元参数为设计变量,以传递路径的总功率流为目标函数进行优化,最后利用声学边界元方法对优化结果进行对比分析。结果表明,将流经路径总功率流替代场点声压为目标函数,不仅能将问题简化大大减少计算量,还能为系统减振降噪提供有效的优化计算方法,具有重要工程应用价值。     

某平台机舱进排风口气流组织的CFD模拟

某自升式平台的机舱进、排风口位置比较接近,机舱排出的热气受大气风影响可能返回进风口,进而提高进风温度,成为一个设计隐患。文章应用CFD技术,通过模拟一定的大气环境条件下进排风口处的气流组织,找出最不利工况,并提出切实可行的改进建议。     

一种简易核磁共振实验教学仪器

自制了一种连续波核磁共振教学实验仪器,包括设计了简单的背景磁场,以及自制实验仪器探头。用示波器观测信号,经过简单的水样品的实验,结果证明,仪器能够得到高质量的连续波核磁共振信号。     

Achieving transport modal split targets at intermodal freight hubs using a model predictive approach

The increase of international freight commerce is creating pressure on the existing transport network. Cooperation between the different transport parties (e.g., terminal managers, forwarders and transport providers) is required to increase the network throughput using the same infrastructure. The intermodal hubs are locations where cargo is stored and can switch transport modality while approaching the final destination. Decisions regarding cargo assignment are based on cargo properties. Cargo properties can be fixed (e.g., destination, volume, weight) or time varying (remaining time until due time or goods expiration date). The intermodal hub manager, with access to certain cargo information, can promote cooperation with and among different transport providers that pick up and deliver cargo at the hub. In this paper, cargo evolution at intermodal hubs is modeled based on a mass balance, taking into account hub cargo inflows and outflows, plus an update of the remaining time until cargo due time. Using this model, written in a state-space representation, we propose a model predictive approach to address the Modal Split Aware – Cargo Assignment Problem (MSA–CAP). The MSA–CAP concerns the cargo assignment to the available transport capacity such that the final destination can be reached on time while taking into consideration the transport modality used. The model predictive approach can anticipate cargo peaks at the hub and assigns cargo in advance, following a push of cargo towards the final destination approach. Through the addition of a modal split constraint it is possible to guide the daily cargo assignment to achieve a transport modal split target over a defined period of time. Numerical experiments illustrate the validity of these statements.     

Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

Port-city exhaust emission model: An application to cruise and ferry operations in Las Palmas Port

Exhaust emissions cause air pollution and climate change. The exhausts of shipboard fuel combustion are equally damaging particularly, so close to the environmentally sensitive mainland and island coasts, as well as at ports due to their urbanized character. This paper estimates, for the first time, exhaust pollutants related to cruise and ferry operations in Las Palmas Port and, in an island context. Emission assessment is based on a full bottom-up model and messages transmitted by the Automatic Identification System during 2011. Results are described as a breakdown of NOx, SOx, PM_2.5, CO and CO₂, according to ship classes, operative type and time, providing valuable information to environmental policy makers in port-city areas and islands under similar conditions. It is generally concluded that vessel traffic and passenger shipping in particular are a source of air pollution in Las Palmas Port. Emission maps confirm location of hot spots in quays assigned for cruise and ferry operations. Policy recommendations encourage regular monitoring of exhaust emissions and market-based incentives supported by details on polluting and operative profiles. On the other hand, feasibility studies are suggested for automated mooring, LNG bunkering facilities and also shore-side energy services, prioritizing berthing of shipping sectors (or sub-sectors) with the highest share of exhaust emissions once their local effects have been confirmed by a dispersion, exposure and impact assessment.     

Extending Time to Collision for probabilistic reasoning in general traffic scenarios

Vehicle-to-vehicle communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty and to loss of communication links. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method to calculate Time to Collision for unconstrained vehicle motion. This metric is gated using a novel technique based on relative vehicle motion that we call “looming”. Finally, these ideas are integrated into a probabilistic framework that accounts for uncertainty in vehicle state and loss of vehicle-to-vehicle communication. Together this work represents a new way of considering vehicle collision estimation. These algorithms are validated on data collected from real world vehicle trials.     

共享单车现金奖励调度策略的用户接受意愿研究

为探讨共享单车用户在不同影响因素下对现金奖励调度策略的接受程度，设定了针对共享单车用户的调查问卷，并在济南市进行了调查，得到519份有效问卷。基于扩展的计划行为理论（Extended Theory of Planned Behavior, ETPB）模型和相关结构方程模型（Structural Equation Model, SEM），设定了社会规范、支付能力、体力、感知服务、感知绿色价值5个构念以及5个相关假定，以测度不同奖励调度情境下共享单车用户对奖励策略的接受程度。结果显示，支付能力和体力对用户接受调度的影响程度最大，其次是社会规范和感知服务，感知绿色价值的影响程度最小。在此基础之上，提出了增大站点密度、实行现金奖励及提高车辆质量等建议，以降低运营成本、提升用户满意度与车辆使用率。     

基于BIM技术的轨道交通桥梁工程量计算

为了解决轨道交通桥梁工程数量计算中存在的工程量信息传递次数多、效率不高等问题,提出一种基于结构化数据的工程量计算方式,省去了基于二维图纸进行工程量计算方式中的信息传递过程,避免了三维模型成熟度不高或信息缺失造成的数量丢失。具体做法为:采用数量项类型划分、分类计算、混合方式编码等方法,建立构件与工程数量、工程数量与清单的映射关系,可跳过图纸或模型进行工程量计算和工程量清单的输出。在智能京张BIM设计过程中,通过编写桥梁工程数量计算软件进行工程数量的计算和输出,验证了该方法的可行性。     

基于深度强化学习的智能船舶航迹跟踪控制

[目的]智能船舶的航迹跟踪控制问题往往面临着控制环境复杂、控制器稳定性不高以及大量的算法计算等问题。为实现对航迹跟踪的精准控制,提出一种引入深度强化学习技术的航向控制器。[方法]首先,结合视线(LOS)算法制导,以船舶的操纵特性和控制要求为基础,将航迹跟踪问题建模成马尔可夫决策过程,设计其状态空间、动作空间、奖励函数;然后,使用深度确定性策略梯度(DDPG)算法作为控制器的实现,采用离线学习方法对控制器进行训练;最后,将训练完成的控制器与BP-PID控制器进行对比研究,分析控制效果。[结果]仿真结果表明,设计的深度强化学习控制器可以从训练学习过程中快速收敛达到控制要求,训练后的网络与BP-PID控制器相比跟踪迅速,具有偏航误差小、舵角变化频率小等优点。[结论]研究成果可为智能船舶航迹跟踪控制提供参考。