不同类型氢燃料电池汽车全生命周期经济性分析及预测研究

氢燃料电池汽车被认为是 21世纪具有潜力的新能源清洁动力汽车之一,影响其推广应用的最重要因素是高成本,开展全生命周期经济性分析至关重要。目前国内外学者对氢燃料电池汽车的生命周期成本评价研究主要集中于零部件成本、燃料价格等因素,而考虑国家及地方补贴政策、运维和报废成本以及不同运营里程、不同车型下经济性分析的较少。从用户的角度,通过对购置成本、运营成本、维护成本、回收残值、补能和抗寒影响以及国家和地方补贴等多种因素进行综合分析,建立全生命周期成本模型,针对乘用车、客车和卡车等不同车型,分场景开展经济性成本评价,将其与传统燃油汽车和纯电动汽车的经济性进行对比分析。面向未来,作出经济性预测,并提出一系列对策建议。     

灰色系统理论在汽车生产计划中的应用

~~灰色系统理论在汽车生产计划中的应用@康永$江苏大学汽车学院!江苏镇江212013 @高翔$江苏大学汽车学院!江苏镇江212013~~~~[1]刘思峰，郭天榜，党耀国，等．灰色系统理论及应用[M]．北 京：科学出版社，1999． [2]邓聚龙．灰预测与灰决策[M]．武汉：华中科技大学出版     

高速公路路面养护管理系统开发和研究

在考虑当前路况的基础上，又考虑路龄的使用性能预测模型，预测的结果更为快速准确。探讨高等级公路道路养护管理决策系统和经济分析方法，提出了改进优劣系数法，该方法充分利用了专家经验知识，减小了决策误差。开发了高速公路路面养护管理辅助决策系统，建立高速公路基本数据库，由系统自动进行路面使用性能评价分析，并利用专家知识系统得出推荐养护方案，系统投入运行后反响良好。该养护管理系统的建立和实施进一步提高和改善了江苏省高等级公路的使用管理和养护管理模式，为高等级公路早日实现网级化系统管理奠定基础。     

灰色预测方法在沥青混合料永久变形研究中的应用

通过高温下沥青混合料蠕变试验发现,加载30min时滞后弹性变形基本完成,可以近似认为其后发生的变形为粘性变形,从而利用灰色预测模型,对加载过程中30min至60min的变形数据进行逆向拟合,得到整个加载过程中的永久变形方程。然后利用坐标变换,得到了时间序列下沥青混合料的永久变形规律。相比于常规的数据拟合方法,由于采用了加载后段的数据进行拟合,避免了开始加载过程中虚假变形的影响,使得拟合的结果更加准确。     

通渝隧道围岩变形的神经网络预测

隧道新奥法施工中，常以围岩变形量作为评判围岩稳定性和支护结构经济合理性的重要指标。公路隧道围岩变形量是随时间而变化的数据序列，因而可以建立一些实时跟踪预测模型和方法。根据通渝隧道围岩拱顶下沉位移变形的特性，采用神经网络技术来预测其变形量，结果表明该方法简易、有效。     

客车主要噪声源识别的试验研究

基于偏奇异值分析法识别噪声源的基本原理，采用了近场声压测量法，同步采集15个传感器的振动与声压信号，识别出客车车外噪声的主要噪声源，分析出各噪声源对车外噪声的贡献并做出降低车外噪声的预测分析。所采用的试验与分析方法具有计算量小、声源定位准确的特点，并且可以准确预测零部件的改进对车外噪声的影响。试验研究和实车应用表明，这种方法可以适用于复杂的工程实际。     

Tuning of transfer functions for analysis of wave–ship interactions

Transfer functions are often used together with a wave spectrum for analysis of wave–ship interactions, where one application addresses the prediction of wave-induced motions or other types of global responses. This paper presents a simple and practical method which can be used to tune the transfer function of such responses to facilitate improved prediction capability. The input to the method consists of a measured response, i.e. time series sequences from a given sensor, the 2D wave spectrum characterising the seaway in which the measurements are taken, and an initial estimate of the transfer function for the response in study. The paper presents results obtained using data from an in-service container ship. The 2D wave spectra are taken from the ERA5 database, while the transfer function is computed by a simple closed-form expression. The paper shows that the application of the tuned transfer function leads to predictions which are significantly improved compared to using the transfer function without tuning.     

基于ANN的船舶撞击高桩码头群桩损伤位置预测

本文采用有限元软件ABAQUS建立了船舶撞击高桩码头群桩的空间有限元模型。通过计算评估了撞击力、桩体刚度、撞击位置和撞击角度下对群桩结构损伤位置的影响。基于人工神经网络(ANN)方法,对不同参数组合下的群桩结构损伤位置进行了预测,并对ANN方法的可行性进行了评估。     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.