Mean velocity and turbulence measurements in a 90 deg curved duct with thin inlet boundary layer

The experimental database established by this investigation of the flow in a large rectangular turning duct is of benchmark quality. The experimental Reynolds numbers, Deans numbers and boundary layer characteristics are significantly different from previous benchmark curved-duct experimental parameters. This investigation extends the experimental database to higher Reynolds number and thinner entrance boundary layers. The 5% to 10% thick boundary layers, based on duct half-width, results in a large region of near-potential flow in the duct core surrounded by developing boundary layers with large crossflows. The turbulent entrance boundary layer case at R sub ed = 328,000 provides an incompressible flowfield which approaches real turbine blade cascade characteristics. The results of this investigation provide a challenging benchmark database for computational fluid dynamics code development

Cruising for Parking with Autonomous and Conventional Vehicles

Parking is a cumbersome part of auto travel because travelers have to search for a spot and walk from that spot to their final destination. This conventional method of parking will change with the arrival of autonomous vehicles (AV). In the near future, users of AVs get dropped off at their final destination and the occupant-free AVs search for the nearest and most convenient parking spot. Hence, individuals no longer bear the discomfort of cruising for parking while sitting in their vehicle. This paper quantifies the impact of AVs on parking occupancy and traffic flow on a corridor that connects a home zone to a downtown zone. The model considers a heterogeneous group of AVs and conventional vehicles (CV) and captures their parking behavior as they try to minimize their generalized travel costs. Insights are obtained from applying the model to two case studies with uniform and linear parking supply along the corridor. We show that (i) CVs park closer to the downtown zone in order to minimize their walking distance, whereas AVs park farther away from the downtown zone to minimize their parking search time, (ii) AVs experience a lower search time than CVs, and (iii) higher AV penetration rates reduce travel costs for both AVs and CVs.Peer Reviewe

A Mesoscopic Simulation Model for Airport Curbside Management

Airport curbside congestion is a growing problem as airport passenger traffic continues to increase. Many airports accommodate the increase in passenger traffic by relying on policy and design measures to alleviate congestion and optimize operations. This paper presents a mesoscopic simulation model to assess the effectiveness of such policies. The mesoscopic simulation model combines elements of both microscopic simulation which provides a high level of detail but requires large amounts of data and macroscopic simulation which requires very little data but provides few performance measures. The model is used to simulate scenarios such as double parking, alternative parking space allocation, increased passenger demand, and enforced dwell times at Pearson International Airport in Toronto, Canada. Scenario analysis shows that adjusting model inputs provides reasonable results, demonstrating the value in using this approach to evaluate curbside management policies. The results show that double parking reduces the utilization ratio and the level of service of the outer curbside but cuts down the passenger and vehicle waiting time. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. Finally, reducing the allowable parking time at the curbside decreases the average dwell time of private vehicles from 89 seconds to 75 seconds but increases the number of circulating vehicles by 30%.Peer Reviewe

A complementarity equilibrium model for electric vehicles with charging

This paper presents a complementarity equilibrium model for electric vehicles (EVs). Under the equilibrium conditions, each EV takes the path that is shortest and does not violate the driving range. When the driving range has to be violated, the EVs are allowed to choose a path with a charging station to extend their driving range. To find the shortest such path, a constrained shortest path problem with replenishment (CSPP with replenishment) is formulated that considers the driving range limit of EVs. The CSPP is solved with a label-correcting algorithm with two additional steps that substantially reduce the computation time and the required memory. The first procedure is a pruning technique that eliminates exploring branches (of an enumeration tree) that can no longer become incumbent and the second procedure is an indexing technique that works as a pointer for navigating the generated (enumeration) tree when it becomes too large. Numerical experiments on a number of networks show a substantially lower computation time compared to existing algorithms and the results provide several insights into the driving patterns of EVs. When charging time is increased, the EVs shift to paths that have a longer travel time but a shorter distance. Hence, the total network distance decreases but the total network travel time increases. We also show that unregulated expansion of the charging infrastructure can actually increase the total network travel time due to the presence of Braess’ paradox