The railroad industry is responsible for the mass transport of a vast numbers of goods throughout the United States. As needs and capabilities of the railroad industry have changed, the interest in reducing the resistance of locomotives and railcars has increased. This has become paramount as fuel prices have increased in recent years. Resistant forces can result from friction in mechanical components and aerodynamic drag of the moving train. As the average traveling speeds of trains have increased, aerodynamics are contributing a larger fraction of the overall resistance. For this reason, the aerodynamic profiles of trains have become a topic of research. Furthermore, current manufacturing practices of railcars provide an opportunity for research in modifications that reduce the aerodynamic drag. This thesis reports on research that has been done to reduce aerodynamic drag on automobile-carrying railcars. Data was collected by placing G-scale (1/29) models into a wind tunnel with a 0.74 m^2 test section. These models were tested for Reynolds Numbers ranging from approximately 2.05 x 10^5 to 2.79 x 10^5. Modifications were made to the models with the intention of reducing the drag. The profile features of the auto-carrying railcars were reviewed and three regions were chosen to be the focus of this study. The selected regions are the roof, side panels and structural chassis region. Special attention was given to the regulations of the railroad industry to ensure the tested modifications would be candidates for implementation. From the data, it was determined that drag could be reduced by modifying or covering the roof, side panels and chassis structure by nominally 20%, 5% and 15% respectively.

The empty railcars repositioning strategy generates no income but is crucial for a good service quality, it should then satisfy two main objectives: fullling the customer demand and generating as little expense as possible. Moreover, because of breakdown or heavy traffic, variation on travel times happens to be the main cause of uncertainty in railroad scheduling and must be taken into account to suggest a robust planning. This thesis presents the linear program used in a prototype tool for the optimization of empty railcar repositioning strategy designed for the SNCF¹. The resulting schedule is computed with CPLEX and minimizes moving cost, delay and unfulllment penalties. Substitutions of railcar categories are also permitted and eventually penalized. In addition, uncertainty on travel times is handled by considering the expected cost of a move (regarding delay probability and possible penalties) and by adding slack periods at the end of moves. The robustness can be modulated through the use of a cursor. Finally, the model enforces a decision making process previously dened by the SNCF to ensure that the suggested planning can be easily grasped and trusted by users. Schedules have then been generated based on randomly generated data and simulated. Results show a potential saves of 10% on considered costs and a good range of use of the robustness cursor is suggested. Finally, paths for improvement of this prototype are proposed to meet the eventual schedulers' further needs in order to move forward the production of this tool at the company scale. ¹Société Nationale des Chemins de fer Français (French National Railways) / Master of Science

The freight rail industry is essential to the US infrastructure and there is significant motivation to improve its efficiency. The aerodynamic drag associated with transport of commodities by rail is becoming increasingly important as the cost of diesel fuel increases. For intermodal railcars a significant amount of aerodynamic drag is a result of the large distance between containers that often occurs and the resulting pressure drag resulting from the separated flow that results due to their non-streamlined shape. This thesis reports on research that has been done to characterize the aerodynamic drag on intermodal train builds and allow their builds to be optimized for fuel efficiency. Data was obtained through wind tunnel testing of G-scale (1/29) models. Drag on these models was measured using a system of isolated load cell balances and the wind tunnel speed was varied from 20 to 100 mph. Several common intermodal scenarios were explored and the aerodynamic drag for each was characterized. These scenarios were the partial loading of containers on rail cars, the influence of the gap between containers, the use of a streamlined container near the front of the train, and the inclusion of semi-trailers on railcars. For each case multiple build configurations were tested and the drag results were compared to determine the optimal build for each scenario.

Rising fuel cost has motivated increased fuel efficiency for freight trains. At cruising speed,the largest contributing factor to the fuel consumption is aerodynamic drag. As a result of stagnationand flow separation on and around lead and trailing cars, the first and last railcars experiencegreater drag than intermediate cars. Accordingly, this work focused on reducing drag on lead locomotivesby designing and optimizing an add-on nose fairing that is feasible for industrial operation.The fairing shape design was performed via computational fluid dynamic (CFD) software.The simulations consisted of two in-line freight locomotives, a stretch of rails on a raised subgrade,a computational domain, and a unique fairing geometry that was attached to the lead locomotive ineach non-baseline case. Relative motion was simulated by fixing the train and translating the rails,subgrade, and ground at a constant velocity. An equivalent uniform inlet velocity was applied atzero degree yaw to simulate relative motion between the air and the train.Five fairing families-Fairing Families A-E (FFA-FFE)-are presented in this thesis.Multidimensional regressions are created for each family to approximate drag as a function ofthe design variables. Thus, railroad companies may choose an alternative fairing if the recommendedfairing does not meet their needs and still have a performance estimate. The regression forFFE is used as a surrogate model in a surrogate based optimization. Results from a wind tunneltest and from CFD are reported on an FFE geometry to validate the CFD model. The wind tunneltest predicts a nominal drag reduction of 16%, and the CFD model predicts a reduction of 17%.A qualitative analysis is performed on the simulations containing the baseline locomotive, the optimalfairings from FFA-FFC, and the hybrid child and parent geometries from FFA & FFC. Theanalysis reveals that optimal performance is achieved for a narrow geometry from FFC becausesuction behind the fairing is greatly reduced. Similarly, the analysis reveals that concave geometriesboost the flow over the top leading edge of the locomotive, thus eliminating a vortex upstreamof the windshields. As a result, concave geometries yield greater reductions in drag.The design variable definitions for each family were strategically selected to improve manufacturability,operational safety, and aerodynamic performance relative to the previous families.As a result, the optimal geometry from FFE is believed to most completely satisfy the constraintsof the design problem and should be given the most consideration for application in the railroadindustry. The CFD solution for this particular geometry suggests a nominal drag reduction of 17%on the lead locomotive in an industrial freight train.