基于粒子群优化小波神经网络的行程时间预测

为使道路使用者在出发前获得具有高实时性和可靠性的行程时间预测信息,提高出行效率,需提升高速公路行程时间的预测精度。鉴于此,将生物学中粒子群优化算法（Particle Swarm Optimization Algorithm, PSO）引入小波神经网络（Wavelet Neural Network, WNN）中,构建基于粒子群优化小波神经网络（Particle Swarm Optimization Wavelet Neural Network, PSO-WNN）的高速公路行程时间预测模型。首先将高速公路原始收费数据规整化,截取其中有效字段,获取研究路段一个月的行程时间数据并对其进行数据处理。然后分别基于PSO-WNN模型和WNN模型,利用Matlab进行实验。实验结果显示,PSO-WNN模型预测结果的平均绝对误差、平均相对误差和均方误差较WNN模型分别降低了83.36%, 82.20%和98.15%。PSO-WNN行程时间预测模型不仅预测精度高,而且能较准确地预测出行程时间的走向及波动情况,在收敛速度方面也呈现出一定的优势,具有较好的适应能力。     

多学科设计优化在桁架式Spar平台概念设计中的应用(英文)

在传统的桁架式Spar设计过程中,设计人员通常只选择一个或两个学科作为某个阶段的设计目标,而没有把设计过程作为一个整体进行研究.同时,传统的设计方法没有充分考虑决定Spar性能的多个学科之间的相互影响和耦合作用.因而,传统的设计方法很难获得最优的设计方案.为解决这一问题,该文将多学科设计优化方法引入桁架式Spar的概念设计,以实现真正的设计优化.文中首先介绍了桁架式Spar的传统设计方法以及多学科设计优化的基本理论,讨论了如何将多学科优化技术应用于Spar平台的概念设计.进而提出了一种适用于Spar设计的多学科设计优化方法-基于响应面的协同优化方法,并基于该方法,建立了桁架式Spar的设计优化框架.最后,对今后的研究方向提出了几点建议.     

A module-oriented tool for optimum design of stiffened structures—Part I

The development of the LBR-5 “Stiffened Panels Software” is included in the development of a new design methodology to ease and to improve preliminary studies of naval structures and floating hydraulic structures. The ultimate target is to link standard design tools (steel structure CAD, hull form, hydrostatic curves, floating stability, weight estimation, etc.) with a rational optimization design module and a minimum construction cost (or minimum weight) objective function. This paper is the first part of a series of two articles. It focuses on the ‘Module-Oriented Optimization’ methodology and on the rational constraints. The second paper presents the optimization technique using convex linearization and a dual approach, and the optimization of an FSO unit as an example (Rigo and Fleury, Marine Stuructures, 2001). LBR-5 allows, as of the first draft, an optimization of the scantling of the structure's constituent elements. Relevant limit states of the structure are taken into account thanks to a 3D rational analysis of the structure based on the general rules of solid-mechanics and structure behaviour. The optimization module is composed of 3 basic modules (OPTI, CONSTRAINT and COST) and a group of sub-modules (in external databases). Among these the user selects a set of relevant sub-modules (i.e. geometrical and structural constraints). Since the present optimization deals with least construction costs (as objective function), and uses an explicit objective function (not empirical), the user must specify labor costs (unitary material costs, welding, cutting, etc.).     

神经网络遗传算法在缆钩优化中的应用

码头缆钩的优化设计对于保证码头系泊船舶的安全具有重要的意义。本文首先用ANSYS有限元软件计算了16种正交试验缆钩的最大应力,然后以此为样本,对BP网络进行了训练,通过训练后的BP网络的泛化能力替代遗传算法优化过程中有限元计算的巨大工作量,使得此缆钩的优化成为可能。结果证明此方法是可行的。     

Hydrogen refueling station networks for heavy-duty vehicles in future power systems

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is to use alternatively fueled vehicles (AFV). Heavy-duty vehicles (HDV) emit a large share of GHG emissions in the transport sector and are therefore the subject of growing attention from global regulators. Fuel cell and green hydrogen technologies are a promising option to decarbonize HDVs, as their fast refueling and long vehicle ranges are consistent with current logistic operational requirements. Moreover, the application of green hydrogen in transport could enable more effective integration of renewable energies (RE) across different energy sectors. This paper explores the interplay between HDV Hydrogen Refueling Stations (HRS) that produce hydrogen locally and the power system by combining an infrastructure location planning model and an electricity system optimization model that takes grid expansion options into account. Two scenarios – one sizing refueling stations to support the power system and one sizing them independently of it – are assessed regarding their impacts on the total annual electricity system costs, regional RE integration and the levelized cost of hydrogen (LCOH). The impacts are calculated based on locational marginal pricing for 2050. Depending on the integration scenario, we find average LCOH of between 4.83 euro/kg and 5.36 euro/kg, for which nodal electricity prices are the main determining factor as well as a strong difference in LCOH between north and south Germany. Adding HDV-HRS incurs power transmission expansion as well as higher power supply costs as the total power demand increases. From a system perspective, investing in HDV-HRS in symbiosis with the power system rather than independently promises cost savings of around seven billion euros per annum. We therefore conclude that the co-optimization of multiple energy sectors is important for investment planning and has the potential to exploit synergies.     

高速铁路标准简支箱梁设计优化

高速铁路标准简支箱梁已在我国高速铁路建设中得到应用推广,其经济性对高速铁路建设成本影响较大,有必要开展优化设计研究。在总结前期客运专线建设和运营经验基础上,结合大吨位锚具在实际工程中的应用成果,通过动静力计算,对既有跨度32 m标准简支箱梁进行优化设计,提出不同检算速度下的推荐梁高;对40 m简支箱梁进行系列化设计研究,分析大跨度梁体基频、梁端转角、残余变形等参数影响,提出合理的腹板厚度、梁高等关键截面参数。经设计优化后的高速铁路标准简支箱梁,降低了工程造价,节省了建设周期。     

S7000K-C转辙机防水解决方案探讨

随着防水罩的去除,在用转辙设备自身防水能力难以满足现场要求的问题日益显现,促使各设备厂家针对进水病害的防治启动了新一轮的设计改进。本文以S700K-C转辙机为例,提出了一套“防水+排水结合”为主,“重点病害区优化”为辅的系统化解决方案,为转辙机行业的防水改进提供了一种不同的发展方向和思路。     

The allocation problem of electric car-sharing system: A data-driven approach

Car-sharing is an emerging transportation mode with increasing applications of electric vehicles (EVs). One of the important issues for one-way electric car-sharing systems (ECS) is unbalanced vehicle distributions and high relocation costs. To improve its efficiency and overall profit, this research proposes a data-driven optimization model with the consideration of demand uncertainty. Firstly, a large amount of historical order data from an ECS company are analyzed to characterize the dynamics of the vehicles and the behavioral features of the users. An important observation is that the daily demand by users, i.e., pick-ups, follows Poisson distribution; and the arrival rates vary across time exhibiting four major temporal stages. Based on this observation, this research constructs the ECS reallocation problem as a data-driven optimization model which is a combination of a probability expectation model and a linear programming problem with real-time data as input. More importantly, different from existing research, this research formulates the profit as the mathematical expectation of a discrete random variable with uncertain consumer demands. This allows for a comprehensive consideration of all possible future demands. Furthermore, driving range constraint has been considered in the proposed model as EV is the focus of this paper. A linear solution method is proposed to obtain the global optimal. At the end, the model is validated using real data from 30 ECS stations. The results indicate the daily improvement of profit could be as high as 19.05% with an average of 10.16%.     

轿车前照灯配光镜注塑工艺优化

轿车前照灯配光镜采用聚碳酸酯(PC)料制作。为克服在注塑中前照灯配光镜出现缺陷,对注塑中工艺进行了优化设置,从选材、设备及工艺参数设置等进行了分析,对烘料设备改进和3台模温机的应用提出了独特想法。产品注塑位置详细计算和成型过程得到较好的控制,解决了成型配光镜中出现的缺陷。经生产实践验证,工艺参数优化设置合理,能防止产品缺陷,保证质量可行,配光镜能得到用户的认同。     

基于ANSYS的汽车后桥焊接变形优化

建立了某汽车后桥的有限元模型,确定了材料热物理性能参数和热源模型,介绍了利用生死单元方法模拟焊缝金属的填充过程,并根据实际情况制定了焊接变形合格范围.采用ANSYS有限元仿真技术对后桥焊接变形进行了预测及优化,通过改变焊缝的焊接顺序及方向实现了抑制焊接变形的目的.结果表明,优化后后桥焊接变形量最小为1.3 mm,模拟计算结果与实测结果相吻合.