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361.
H. S. Kook S. R. Shin K. D. Ih D. B. Kim D. H. Yu 《International Journal of Automotive Technology》2009,10(1):55-63
The large-scale shear flows over the sunroof opening of a mid-sized SUV measured using a PIV system were investigated. The
shear flows were measured for five different cases of deflector protrusion (one case was the baseline test without deflector)
at two different free stream flow velocities below the critical velocity where the buffeting noise level reached a maximum.
The structures of the shear flows were observed to differ, apparently depending on whether the radiated buffeting noise is
relatively strong or not. For strongly buffeting experimental cases, the momentum thicknesses of the shear layers were observed
to grow rapidly and saturated at a station near the downstream edge of the sunroof opening, where the saturation of the transverse
velocity fluctuations was also observed, and where the vortex coalescence process was presumably completed. On the other hand,
no discrete large-scale vortex structures were observed for none-buffeting or weakly buffeting cases. Streamwise growth of
the velocity fluctuations was found to be well predicted by a linear hydrodynamic instability analysis for the strongly buffeting
cases. Numerical results obtained from a linear inviscid instability analysis using a hyperbolic tangent mean velocity profile
were used to calculate the amplification factors with the initial momentum thickness and the streamwise fluctuation wavenumber.
The shear flows were found to form large-scale discrete vortices when the linear inviscid amplification factors exceeded a
threshold amplification factor. 相似文献
362.
机械电子技术在未来铁道车辆中的应用与发展(待续) 总被引:1,自引:1,他引:0
全面介绍了机械电子技术在未来铁道车辆特别是悬挂系统、牵引系统和制动系统中的应用和发展。 相似文献
363.
澳大利亚重载列车纵向动力的研究 总被引:4,自引:0,他引:4
在进行BHP铁矿石重载运输时,列车平均轴重在35t以上、整列车由220辆车组成的情况已为数不少,列车的纵向动力常常会大于1000kN。20世纪90年代中期,高的列车纵向力导致列车沿着坡度大的坡道下行和在起伏很大的路段上运行时出现列车分离。与修改车辆维修工艺和操作程序一样,对列车驾驶方法进行了研究和修正,并对列车实际操作进行了调整。通过一系列的措施,大大减少了列车延误次数。 相似文献
364.
[目的]针对无人艇协同围捕过程中逃跑目标具备智能性,现有无人艇策略难以围捕成功的问题,提出一种基于双层切换策略的多无人艇协同围捕算法。[方法]第1层围捕策略采用改进势点法,以无人艇与势点的总直线距离最小为优化目标,采用匈牙利算法为无人艇动态分配势点,并采用人工势场法实现无人艇的协同避碰;第2层围捕策略利用了阿波罗尼奥斯圆的性质,在两艘无人艇前往逃跑目标的目标点进行拦截,剩余无人艇运动方向保持与逃跑目标相同,以不断缩紧包围区域;为应对逃跑目标不同的逃跑方式,第1层围捕策略和第2层围捕策略可互相转化。[结果]仿真实验表明,该算法相较于顺序分配势点算法和极角分配势点算法,围捕时间更少或持平,证明了该算法的有效性和先进性。[结论]该多无人艇协同双层围捕算法,对具备典型智能性的逃跑目标具有围捕效果。 相似文献
365.
A fully Lagrangian algorithm for numerical simulation of fluid-elastic structure interaction(FSI) problems is developed based on the Smoothed Particle Hydrodynamics(SPH) method. The developed method corresponds to incompressible fluid flows and elastic structures. Divergence-free(projection based) incompressible SPH(ISPH) is used for the fluid phase, while the equations of motion for structural dynamics are solved using Total Lagrangian SPH(TLSPH) method.The temporal pressure noise can occur at ... 相似文献
366.
In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health
problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling).
The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing
bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle
route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities,
(4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics.
The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study
emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle
route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the
most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs,
red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle
facility on the route.
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Naveen Eluru is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. He received his M.S. degree in Civil Engineering from The University of Texas at Austin, and his Bachelors in Technology Degree from Indian Institute of Technology in Madras, India. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Naveen Eluru is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. He received his M.S. degree in Civil Engineering from The University of Texas at Austin, and his Bachelors in Technology Degree from Indian Institute of Technology in Madras, India. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
367.
Erika Spissu Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat 《Transportation》2009,36(4):403-422
In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles
drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study
explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e.,
self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive
distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components.
The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation
approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling
in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at The University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from The University of Texas at Austin. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use-transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at The University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from The University of Texas at Austin. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use-transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
368.
This study presents a unified framework to understand the weekday recreational activity participation time-use of adults,
with an emphasis on the time expended in physically active recreation pursuits by location and by time-of-day. Such an analysis
is important for a better understanding of how individuals incorporate physical activity into their daily activities on a
typical weekday, and can inform the development of effective policy interventions to facilitate physical activity. Furthermore,
such a study of participation and time use in recreational activity episodes contributes to activity-based travel demand modeling,
since recreational activity participation comprises a substantial share of individuals’ total non-work activity participation.
The methodology employed here is the multiple discrete continuous extreme value (MDCEV) model, which provides a unified framework
to explicitly and endogenously examine time use by type, location, and timing. The data for the empirical analysis is drawn
from the 2000 Bay Area Travel Survey (BATS), supplemented with other secondary sources that provide information on physical
environment variables. To our knowledge, this is the first study to jointly address the issues of ‘where’, ‘when’ and ‘how
much’ individuals choose to participate in ‘what type of (recreational) activity’. 相似文献
369.
Agent-based microsimulation models of transportation, land use or other socioeconomic processes require an initial synthetic
population derived from census data, conventionally created using the iterative proportional fitting (IPF) procedure. This
paper introduces a novel computational method that allows the synthesis of many more attributes and finer attribute categories
than previous approaches, both of which are long-standing limitations discussed in the literature. Additionally, a new approach
is used to fit household and person zonal attribute distributions simultaneously. This technique was first adopted to address
limitations specific to Canadian census data, but could also be useful in U.S. and other applications. The results of each
new method are evaluated empirically in terms of goodness-of-fit. 相似文献
370.
Stacey G. Bricka Sudeshna Sen Rajesh Paleti Chandra R. Bhat 《Transportation Research Part C: Emerging Technologies》2012,21(1):67-88
Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics. 相似文献