基于BP网络的球鼻首参数化优化

本文以某集装箱船为研究对象,对降速航行后的球鼻首进行优化。采用Catia建立船体三维模型,为了产生不同形状的球鼻首,选取球鼻特征参数来描述其基本结构;采用拉丁超立方试验抽样方法得到12组不同形状的球鼻首,提出运用非线性拟合能力较强的BP网络构建球鼻首参数和阻力系数之间的关系模型;采用遗传算法对训练后的网络进行极值寻优。结果显示,优化船型的阻力系数显著降低,说明该方法对球鼻首的优化有一定的借鉴意义。     

客车捎带模式下高铁快捷货物输送方案研究

以提高高铁快运当日达产品的时效性、收益率为核心,对既有载客动车组捎带模式下的快捷货物输送方案进行优化。借助时空网络以列车运行成本与时间惩罚费用之和最小为目标,同时满足货主时限、列车容量以及列车停站方案等约束,建立输送方案优化模型,通过匈牙利算法,并借助Matlab的Yalmip工具箱求解模型。以兰州西站至天水南站、宝鸡南站及西安北站部分时间段的快捷货物运输需求为背景进行算例分析,验证模型的有效性。结果表明合理估算列车装载容量及货物的延迟时限对输送方案的选择起重要作用。     

技术站间货物列车协同配流方案研究

技术站间货物列车协同作业组织模式，可实现各站获益，整体加强，对提升铁路运输生产效率具有重要意义.本文建立技术站间货物列车协同配流模型.模型以最大化两技术站的正点出发列车数作为目标函数，采用启发式遗传算法进行寻优，得到货物列车解编顺序和配流方案.最后，通过对算例进行实验分析，验证协同配流模型的实用性.结果表明，技术站间协同配流作业明显压缩车辆在站总停留时间，增加了阶段计划内正点出发列车数，进而提高了技术站内线路使用能力.     

考虑收益管理的高铁平行车次动态差别定价

基于RP(Revealed Preference)和SP(Stated Preference)调查数据，利用潜在类别模型对高铁旅客进行细分，得到旅客对平行车次不同服务属性，如列车运行时间、发车时段和舒适度的偏好程度，并对其进行量化；引入收益管理，以多列车整体收益最大为目标，构建平行车次动态差别定价模型，并设计模拟退火算法进行求解；最后，通过京沪高铁进行实例验证.结果表明：与固定票价进行客票销售相比，所提方案能够适应高峰期和平峰期不同客流特点，提高铁路客票总收益，为高铁平行车次灵活定价提供参考.     

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。     

Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.     

VTS中雷达信息与AIS信息融合的探讨

文章对VTS中雷达信息与AIS信息的来源、种类与特点进行了比较,论证了两者信息融合的可行性与必要性,同时给出了信息的融合模型与方法,也提出信息融合需要进一步解决的问题.     

Influence of AC system design on the realisation of tractive efforts by high adhesion locomotives

The main task for heavy haul railway operators is to reduce the cost of exported minerals and enhance the long-term viability of rail transport operations through increasing productivity by running longer and heavier trains. The common opinion is that this is achievable by means of implementation of high adhesion locomotives with advanced AC traction technologies. Modern AC high adhesion locomotives are very complex mechatronic systems and can be designed with two alternative traction topologies of either bogie or individual axle controls. This paper describes a modelling approach for these two types of AC traction systems with the application of an advanced co-simulation methodology, where an electrical system and a traction algorithm are modelled in Matlab/Simulink, and a mechanical system is modelled in a multibody software package. Although the paper concentrates on the analysis of the functioning for these two types of traction control systems, the choice of reference slip values also has an influence on the performance of both systems. All these design variations and issues have been simulated for various adhesion conditions at the wheel–rail interface and their influence on the high traction performance of a locomotive equipped with two three-axle bogies has been discussed.     

基于响应面和遗传算法的柴油机瞬态过程喷油参数优化

基于台架试验数据,利用响应面法建立了某工程机械用柴油机瞬态过程喷油参数与性能的近似高精度模型,基于此模型采用遗传算法对瞬态过程喷油参数分别进行离线优化研究。结果表明:采用单目标优化确定的燃油消耗率(BSFC)、NO_x比排放量和颗粒质量(PM)比排放量的优化极限分别可达180.23g/(kW·h),8.92g/(kW·h)和0.011 8g/(kW·h),相对原机可降低多达4.5%,34.0%和37.3%。双目标优化的Pareto解集表明,相比于同时优化BSFC和NO_x比排放量,BSFC和PM比排放量更容易同时得到优化。采用权重因子适应度函数的三目标优化结果对应的BSFC,NO_x比排放量及PM比排放量分别为184.70g/(kW·h),12.62g/(kW·h)和0.012 2g/(kW·h),较原机分别降低2.1%,6.6%和35.3%。改进优化模型后,性能优化Pareto解集对应的BSFC和PM比排放量水平都非常接近其优化极限,但NO_x比排放量相对其优化极限仍然较高。