线性大系统参数稳定区域的扩大

本文利用加权V函数求解线性大系统的稳定性,得到了比较好的结果; 并指出了利用向量V函数造成亏损的实质。      

Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

A mesoscopic model for large-scale simulation of pedestrian dynamics

A mesoscopic pedestrian model is proposed, considering pedestrians as individuals and describing their movement by means of aggregate density-flow relationships. The model builds on a stochastic process, describing transition rates among adjacent sites on a lattice. Each lattice can contain several pedestrians. The approach is minimal and fast to simulate, and, by construction, capable of capturing population heterogeneity as well as variability in walking behaviour and en-route path choice. The model is more efficient than microscopic models, and potentially more accurate than macroscopic ones. We calibrate and validate the model using real data and carry out several numerical experiments to present its key properties and possible applications for simulation of large-scale scenarios.     

波浪能发电装置水叶轮设计及试验

针对一种海洋波浪能轴流式叶轮波浪能发电装置,结合我国近海波浪特点,对该装置的叶轮进行结构设计、优化选型和实验验证。该海洋波浪能轴流式叶轮波浪能发电装置以水叶轮作为唯一的波浪能转化装置,能量转化效率是影响发电的重要因素。根据浮标的运动状态,结合我国近海水纹特征,得出不同设计参数下叶轮的轴向力、扭矩、相对出口压力、能量损失系数等关键数据。这些数据为制造样机提供力学及能量转化效率方面的指导作用。     

浅析水运工程勘察设计招标投标

根据目前水运工程勘察设计招标投标现状,阐述在水运工程勘察设计领域实行招标投标的必要性和招标活动必须注意的问题。     

海上导航浮标的远程监测

介绍新近研发的海上导航浮标(以下简称航标)信息的远程监测系统的软硬件设计整体方案。海上航标与陆上控制中心之间的信息交换以GSM方式实现。本系统可以监测航标的经纬度、电气参数和日光阀，判断航标是否被撞等。另外，控制中心也可以设置航标闪烁频率、航标灯的开或关及GSM工作方式等。整个系统经海上现场调试运行，证明是可行的。     

投标报价基于GM(1,1)灰色动态模型的主材价格预测

在建设工程中,建筑材料的价格是随时变化的。在工程投标报价时,如何准确预测主材价格,是施工企业研究的主题。此文基于灰色系统理论,通过建立GM(1,1)灰色预测模型,对工程主要材料(钢筋)的价格进行分析与预测,并以实际工程为例进行验证,证明基于建立的GM(1,1)灰色动态模型预测钢筋价格,精度较高、效果较好,可为工程投标和施工采购钢筋提供有益参考。     

波流联合作用下振荡浮子式波浪发电机动力学分析

参考某型振荡浮子式波浪发电装置的具体参数,结合该装置工作过程,使用水动力分析软件OrcaFlex建立了在不同海况下装置工作过程中的动力学分析简化模型,分析了流、波以及波、流联合作用对振荡浮子式波浪发电装置张力、弯矩沿缆长方向的变化规律以及装置能量转换效率的影响。结论对海洋可再生能源研究有一定的参考价值。     

The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations

Multi-agent simulation has increasingly been used for transportation simulation in recent years. With current techniques, it is possible to simulate systems consisting of several million agents. Such multi-agent simulations have been applied to whole cities and even large regions. In this paper it is demonstrated how to adapt an existing multi-agent transportation simulation framework to large-scale pedestrian evacuation simulation. The underlying flow model simulates the traffic-based on a simple queue model where only free speed, bottleneck capacities, and space constraints are taken into account. The queue simulation, albeit simple, captures the most important aspects of evacuations such as the congestion effects of bottlenecks and the time needed to evacuate the endangered area. In the case of an evacuation simulation the network has time-dependent attributes. For instance, large-scale inundations or conflagrations do not cover all the endangered area at once.These time-dependent attributes are modeled as network change events. Network change events are modifying link parameters at predefined points in time. The simulation framework is demonstrated through a case study for the Indonesian city of Padang, which faces a high risk of being inundated by a tsunami.     

Reprint of “Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event”

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.