Utilizing Regression to Optimize Key Variables for Minimizing CO2 Emissions and Fuel Consumption of Urban Air Mobility Vehicles
Open Access DepositedAs the demand for commercial aviation increases, fuel consumption and its contribution to carbon dioxide (CO2) emissions, a known greenhouse gas that negatively impacts the environment, also increases. Forecasts call for the United States air transportation system to undergo significant expansion and increased competition highlights the need to reduce direct costs such as fuel while an adverse environmental impact from these increased air operations could be realized unless carbon emissions are also reduced. Urban air mobility (UAM) is an emerging mode of aviation that foresees the use of Vertical Takeoff and Landing (VTOL) capable vehicles to ferry small groups of passengers or goods within urban and suburban areas at lower altitudes than traditional aircraft. Optimizing significant design parameters such as Disk Loading, Cruise Altitude and Hover Tip Speed can result in minimum fuel burn and minimum CO2 emissions for the UAM mission profile. Using a multilevel factorial plan, emissions and fuel burn data were generated from sets of design variables utilizing the NASA Design and Analysis of Rotorcraft (NDARC) software program for the Side-By-Side (SBS) and Quadrotor (QR) UAM vehicle configurations. These data were used in multiple linear regression analyses to generate a statistical relationship between the predictors and the response variable as a regression equation. The Side-By-Side emissions response was found to be primarily affected by Hover Tip Velocity and Cruise Altitude only. However, the Side-By-Side fuel burn response utilized all three design parameters. The Quadrotor emissions and fuel burn responses were found to be primarily affected by Disk Loading, Hover Tip Velocity and Cruise Altitude. Both regression equations predict good approximation of the optimized emission result with 1.09% error for Side-By-Side and 1.69% error for Quadrotor and as compared to the NDARC software result. Also, the regression equations for an optimized fuel burn provided good approximations with 1.41% for the Side-By-Side and 0.70% error for Quadrotor. The optimized Side-By-Side design was shown to produce 28% less emissions than a baseline Single Main Rotor (SMR) helicopter, while the optimized Quadrotor design was shown to produce 21% less emissions than the baseline SMR. Similarly, these optimized Side-By-Side and Quadrotor designs use 38% and 31% less fuel than a baseline helicopter. Future Side-By-Side and Quadrotor designs can consider using the optimized design parameters for low emission and low fuel burn flight operations.
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