基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

全回转起重船多系统耦合运动响应仿真分析

根据运动学基本原理,计入船-吊物、船-锚链耦合影响以及起重船自身受到的外环境载荷,建立船舶多系统运动模型。利用MATLAB/Simulink软件进行仿真,对多系统耦合作用下的船舶运动进行数值分析。分别在不同的遭遇浪向角和不同的吊臂回转角下,对船舶在各自由度上的位移进行比较,得到其对船舶运动参数的影响规律。结果表明:起重船运动过程中因计入多系统耦合影响,自身运动也表现出更为符合实际的运动特性,为准确预报全回转起重船多系统的运动响应提供了更为科学的理论依据。     

可变截面涡轮增压瞬态性能仿真研究

为研究可变截面涡轮喷嘴开度对发动机的性能影响,建立GT-power和MATLB/Simulink联合仿真模型。采用GT-power软件建立的TBD234V12可变截面涡轮增压柴油机仿真模型研究发动机稳态性能,联合MATLB/Simulink软件建立的可变截面涡轮增压柴油机控制模型,对发动机瞬态性能进行仿真计算。结果表明:在发动机瞬态工况下,可变截面涡轮增压系统可以明显改善常规增压柴油机的动力性、经济性和动态响应特性,并且有效降低增压柴油机的时滞性,涡轮迟滞时间的降幅约为30%。     

考虑螺旋桨体积力的水下机器人水动力特性仿真

为在初步设计时预测ROV的水动力性能,使用CFD技术进行分析,以ROV模型为例,最大程度地保留ROV内部的构件,使模型更加接近真实状况。分别计算ROV在不同工况下的阻力、不同漂角下的横向力和转艏力矩以及添加体积力模型之后的螺旋桨流场状况,很好地模拟出ROV周围的流场,并与已有的试验数据进行对比。结果验证了CFD水动力仿真的可行性和准确性,并还可预测ROV在螺旋桨作用下的进速等,对ROV设计具有实际的参考价值和指导意义。     

Shared autonomous electric vehicle (SAEV) operations across the Austin,Texas network with charging infrastructure decisions

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet.This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.     

Mechanism of Tunnel Water Inflow Caused by Bending Failure of a Waterproof Dike Based on a Circular Thin Plate Mechanical Model北大核心CSCD

Water bursts during tunnel construction endanger construction, and it is therefore necessary to reserve a waterproof dike with the required thickness to avoid water bursts and to take reinforcement of the dike and treatment of the structure liable to trigger a water burst. Using the water burst at K5+398 of the Mingyueshan tunnel of the Shanghai-Chengdu expressway as an example, and considering the type of tunnel section and the upright mudstone of the dike, the waterproof dike at the work face is simplified as a round thin plate. A formula for the calculation of a minimum safety thickness for the critical waterproof dike is deduced by analyzing the force applied on the water-proof dike, and the minimum safety thickness for the water burst section at K5+398 of the Mingyueshan tunnel is cal-culated. The numerical simulation analysis demonstrates the critical thickness of waterproof dike at K5+398 of the Mingyueshan tunnel is 1.4-1.55 m, and the calculated water inflow and water burst basically agree with the actual condition. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved.     

Emission rates of intermodal rail/road and road-only transportation in Europe: A comprehensive simulation study

Intermodal rail/road transportation combines advantages of both modes of transport and is often seen as an effective approach for reducing the environmental impact of freight transportation. This is because it is often expected that rail transportation emits less greenhouse gases than road transportation. However, the actual emissions of both modes of transport depend on various factors like vehicle type, traction type, fuel emission factors, payload utilization, slope profile or traffic conditions. Still, comprehensive experimental results for estimating emission rates from heavy and voluminous goods in large-scale transportation systems are hardly available so far. This study describes an intermodal rail/road network model that covers the majority of European countries. Using this network model, we estimate emission rates with a mesoscopic model within and between the considered countries by conducting a large-scale simulation of road-only transports and intermodal transports. We show that there are high variations of emission rates for both road-only transportation and intermodal rail/road transportation over the different transport relations in Europe. We found that intermodal routing is more eco-friendly than road-only routing for more than 90% of the simulated shipments. Again, this value varies strongly among country pairs.     

Situation analysis forecasting: the case of European maritime clusters

Within the literature concerned with aspects of competitiveness, innovation and strategic management of industrial clusters, the body regarding forecasting of strategic management is still nascent. This work aspires to render a contribution within the domain of strategic management forecasting, through the indicative case of European maritime clusters. For this end, a two-tier model is formulated. A quantitative strengths, weaknesses, opportunities, and threats (SWOT) methodology that derives from the fusion of a quondam situation analysis and crosstabs’ theory is generated. This leads to the utilization of the crosstab’s conditional probabilities as transition probabilities; the latter are called to compile the transition matrix of a Markov chain. Through this methodology, and the successive Markov chain’s transition matrices, strategic forecasts are extracted for a devised European maritime cluster case. This work relinquishes a novel application with respect to strategic management forecasting, that provides a dichotomy of practical interpretations and scenarios for quantitative situation analysis. Thus, it may enable effective real-time decision-making for strategic management and/or policy drafting. The situation analysis forecasting model may find applicability in a plethora of practical and theoretical cases, wherein forecasting may be desirable. It may as well pertain to an intrinsic methodology for situation analysis forecasting of maritime clusters.     

Genetics of traffic assignment models for strategic transport planning

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.