Impact of ride-sharing on mobility trends and vehicle stock

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018Cataloged from PDF version of thesis.Includes bibliographical references (pages 137-141).North American transportation industry is on the verge of a revolutionary change. With the advent of car-sharing services and ride-sharing companies, the transportation industry is experiencing a fundamental way in which people choose to travel. This particular thesis looks at the impact of these disruptive changes in transportation on the way people choose to travel, the car based vehicle miles travelled (VMT) and the national vehicle stock. In particular, this work tries to look at how people choose to make a travel decision when embarking on a particular trip and how that translates to an effect on the national level vehicle stock. When presented with a particular mode of travel, the most relevant aspects associated with that particular mode of travel were explored and evaluated. Each mode was evaluated based on cost, time and comfort associated with the mode.Multi-attribute utility theory was used to study and evaluate how people make decisions about mode choice when choosing a particular mode for a trip. This work tries to look at the impact of ride-sharing on modal changes and shits that result in a less or more use of personal car travel. Apart from the travel behavior associated with modes, this work also estimates impact of ride-sharing on the total vehicle usage in urban areas. Once the modal share of different modes was estimated, an overall passenger trip demand was generated at the national level. This trip demand was broken down into car-based trips and non-car based trips from the modal share result. Combined with occupancy assumptions, this passenger trip demand was converted into a car based VMT estimate. Finally, combining the car based vehicle miles travelled with the average vehicle utilization, the national vehicle stock was calculated.In order to measure the impact of these futuristic technologies on modal share, VMT and the national vehicle stock, scenario analysis was the method chosen. In order to have a reference case, a base case scenario was designed assuming the world remains as it is today and nothing changes. A series of progressive scenarios related to ride-sharing were then tested to gauge the impact of ride-sharing. It was found that ride sharing has the most significant impact in the urban areas for short trips. The national level vehicle stock in the year 2050 declined by approximately 1.0% in the improved ride-sharing scenario. Higher-electrification of vehicles along with improvements in ride-sharing did not decrease the stock further by much, as compared to just the improved ride-sharing scenario. In an aggressive scenario, with improved ride-sharing, improved transit and anti-car policies, the national level stock value in year 2050 declined by approximately 6% compared to the base case scenario.Finally, in the scenario with improved ride-sharing and higher autonomy, the national level VMT increased by 1.3%, but the vehicle stock declined by 9.9%. The results from this work can be further used to inform certain decisions regarding changing travel behaviors or explore questions related to higher level policy analysis.by Suhrid Deshmukh.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineerin

Software for fault detection in HVAC systems in commercial buildings

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 135-138).The building sector of the United States currently consumes over 41% of the United States primary energy supply. Estimates suggest that between 5 and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate those building system faults that cause large building energy inefficiencies. The technical contributions of this work include expert rules that adapt to HVAC equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. These contributions are particularly unique in their treatment of model and the careful consideration of user interests in fault evaluation. As a first step to developing this general framework for fault detection, first order faults such as simultaneous heating and cooling and imbalanced airflows within several large air-handling units were targeted. Savings of around $22,500 were predicted when the fault of simultaneous heating and cooling was occurring over the entire month in an air handler. Some of the other faults include the heat recovery pump operation in air handling units. An example of the potential energy savings in a large hospital that has been monitored is presented. Yearly savings of around$24,000 were predicted by correcting the operation of the heat recovery pump. Faults in the cooling system were also explored. The paper presents a strategy to load multiple centrifugal chillers with a monotonically increasing COP vs. partload ratio curve. The paper also considers the optimization of cooling tower fan operation coupled with the chiller operation. Models for cooling tower performance and chiller performance are used to define the operation that yields the minimum energy consumption of the system. The number of cooling tower fans and the use of multiple towers with a single chiller are considered. The optimum operation is compared to actual operations to signal operating faults. User testing and experiments show that embracing uncertainty within HVAC fault detection and evaluation is not only paramount to judicious fault inference but it is also central to gaining the trust and buy-in of system users who ultimately can apply fault detection information to actually fix and improve building operations.by Suhrid Avinash Deshmukh.S.M

Langevin Simulation of Aggregate Formation in the Transition Regime

<p>We study the aggregation of monomers in an aerosol via non-dimensional Langevin simulations, in which particles remain in point contact upon collision, and report the hydrodynamic radii and projected areas of the formed aggregates with less than 300 primary particles. Unique from prior studies, in examining aggregation we monitor the evolution of the distributions of two Knudsen numbers: the traditional Knudsen number (Kn) and the diffusive Knudsen number (Kn_D), which both shift to smaller mean values as aggregation proceeds. As Kn transitions from large to small values, momentum transfer changes from a free molecular to a continuum process; analogously, as Kn_D decreases, aggregation is altered from occurring ballistically to diffusively in a dilute system. During simulations, the change in drag coefficient with both changing Kn and changing aggregate structure is accounted for. We find that as compared to completely coalescing particles (spheres), non-coalescing aggregates with the same initial Kn and Kn_D have Kn_D values, which decrease more rapidly due to aggregation; hence, aggregates are more likely to collide with one another diffusively when compared with their spherical counterparts of the same Kn distribution. Further, we find that aggregation with evolving Knudsen numbers does not lead to strong scaling between the number of monomers in a formed aggregate and the aggregate radius of gyration for aggregates composed of 300 or fewer primary particles. In spite of this, aggregate hydrodynamic radii and orientationally averaged projected areas are found to scale well with the number of monomers per aggregate.</p> <p>Copyright 2015 American Association for Aerosol Research</p