Average Generic Vehicle Method for Fleet-Level Analysis of Noise and Emission Tradeoffs

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Abstract
A method is proposed for modeling average generic vehicles for fleet-level analysis of technology impacts on environmental metrics. Vehicles are grouped into classes distinguished by aircraft-level metrics, which include fuel burn (a surrogate of carbon dioxide emissions), nitrogen oxide species (NOx) emissions, and sound exposure level noise contours. Target metrics are established for a subset of 94 airports by designing a series of tests of sequentially increasing complexity, with ideal average generic vehicle models minimizing the error distributions across these airports when aircraft-level performance is aggregated to fleet levels. A Latin hypercube design of experiments is employed to explore the aircraft-level design space and construct surrogate models for each metric. Monte Carlo samplings of these surrogate models are paired with desirability functions to rapidly identify locations in the design space that simultaneously match aggregate fuel burn, NOx emissions, and day/night-level contours. In general, the average generic vehicles provide greater accuracy for each of these metrics across the 94 airports for a representative six weeks of operations as compared to the traditional approach of choosing a representative vehicle per class. These average generic vehicles can be paired with a fleet-level tool, where the baseline models represent current technology aircraft and serve as virtual testbeds for projecting impacts of future technology implementation.

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