输入扰动对波动鳍推进性能影响的研究

输入扰动对波动鳍推进性能影响是一个新兴的研究领域。本文分析了高频率、小幅度和短波长的正弦波形叠加于波动鳍上时对波动鳍推进力的影响。建立运动学模型,利用四面体网格对计算区域进行划分,采用非耦合隐式求解器求解非定常不可压缩N-S方程和连续性方程。给出计算条件,并对算法给予验证。比较等波幅和变波幅两种正弦扰动波形下,波动鳍的无量纲阻力系数、阻力系数均值以及涡量场随周期(0.05 s、0.1 s、0.3 s、0.5 s、0.8 s)、幅度(0.0008 m、0.001 m、0.0015 m)和波长(0.002 m、0.008 m、0.012 m)的变化情况,从涡动力学角度对该影响进行解释。结果显示:输入扰动不仅影响了波动鳍前缘涡的传递,并对波动鳍周边及尾迹区域漩涡数量和强度产生了改变。除在特定条件外,输入扰动对波动鳍推进力产生负面影响,变波幅扰动的影响要大于等波幅情况。该研究可为波动鳍选择合适参数的输入扰动以提高推进力提供参考。     

车辆隔热壁K值计算中空气层的处理方法

对轨道交通车辆多层复合结构的隔热壁进行隔热性能理论计算与仿真计算的对比,对隔热壁内部空气层的隔热性能进行分析。结论为:在车辆多层复合结构的隔热壁K值计算中,当空气层厚度大于一定厚度时,流体自然对流对隔热壁的隔热性能影响较大。利用多项式密度(Polynomial Denisity)和Boussinesq模型两种算法,对所得结论进行了验证。     

水下双层圆柱壳辐射声场欠定分离评估方法研究

水下双层加筋圆柱壳振动和辐射声场的评估对其辐射噪声监测和控制具有重要工程意义。文中通过结构振动模态参与因子向量自身的稀疏特性,分析提出了一种基于结构振动的辐射噪声欠定分离评估方法,可实现有限振动测点情况下的水下复杂结构振动和辐射声场的有效评估。数值和试验结果验证了文中方法的有效性,且所需要的振动测点数目少,具有良好的工程适用性。     

基于神经网络的小时间粒度交通流预测模型

为解决传统车队离散模型基于概率分布假设和现有交通流预测时间粒度过大不能应用于自适应信号配时优化等问题.在车队离散模型的建模思路上,先分析了下游交叉口车辆到达与上游交叉口车辆离去之间的关系,基于此构建了基于神经网络的小时间粒度交通流预测模型.该模型以上游交叉口离去流量分布为输入,下游交叉口到达流量分布为输出,时间粒度为5 s.最后,通过实际调查数据标定模型参数并应用模型预测下游交叉口到达流量.结果表明,与Robertson模型相比,本文模型预测结果能够更好地反映交通流的变化特征,平均预测误差减少了8.3%.成果可用于信号配时优化.     

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.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

干线公路高模量沥青混凝土应用研究

文章试验研究了不同PR/RA改性剂掺量的高模量沥青混凝土(HMAC)的抗车辙性能,分析了层位、层厚、模量、轴载、温度和荷载次数等因素对高模量沥青混凝土路面抗车辙性能的影响程度,提出了合理的路面层位设置、模量取值范围及路面结构组合,并通过某干线公路试验路段,验证了高模量沥青混凝土的抗车辙效果。     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

悬链式单浮箱防波堤水动力特性试验研究

为研究悬链式单浮箱防波堤水动力特性的影响因素,采用二维物理模型试验方法,讨论规则波下相对吃水S/d、相对宽度B/L、锚链刚度k、锚链系泊倾角α等因素改变时浮堤模型的透射系数K_t和锚链上最大拉力F的变化规律。试验分析采用平均波高计算透射系数K_t,以前1/10最大拉力平均值作为最大拉力F。结果表明:相对宽度B/L0. 3时,相对宽度B/L是影响浮箱模型消浪性能的主要因素;相对吃水S/d0. 14时,相对吃水S/d是影响锚链上最大拉力F的主要因素。所得结果可以为悬链式单浮箱防波堤的设计和进一步研究提供参考。     

船用防污涂料浅海浸泡试验防污性能评价方法

针对防污漆样板浅海浸泡试验防污性评定,通过分析不同类别的生物污损对防污涂料涂层的影响,根据其影响程度的不同划分合适的评分比例,并选取单体污损作为主要评价标尺。参考国标《防污漆样板浅海浸泡试验方法》,结合浅海浸泡试验,在实际运用中发现现行评价体系的不足之处,并提出相应的解决方案,在此基础上建立一套行之有效的船用防污涂料防污性评价方法。