Future air transportation demand forecasts suggest that environmental concerns such as noise will be exacerbated beyond their current level. Although detailed airport noise modeling with tools such as the integrated noise model and the Federal Aviation Administration’s Aviation Environmental Design Tool are available, these software require relatively long setup and run times due to the number of inputs available to the user and the general fidelity level of the models. A rapid, flexible, and more simplified method that reduces the input variables to a critical few and can provide results in minutes is desired to evaluate fleet-level metrics with respect to new technologies or forecasted changes in demand. Current lower-fidelity methods only calculate a change in contour area due to changes within the overall fleet composition. These methods cannot account for the shape of the contour. This paper presents a rapid airport noise computation model that leverages the fidelity of detailed models. By performing generic aircraft operations upfront, events can be rapidly recombined later to perform trades of various noise mitigating strategies. By moving the detailed noise modeling “offline” through simplifying assumptions, the fidelity of detailed models can be channeled upstream in the decision-making process. The increased speed of the model enables multiple fleet-level analyses. Verification and validation results show agreement benchmarked against integrated noise model equivalents when model assumptions are obeyed. Comparison to detailed integrated noise model equivalents when model assumptions are violated introduces certain errors, but the model retains contour area accuracy, through the basic capture of contour shape.
Development of a Rapid Fleet-Level Noise Computation Model
Journal of Aircraft ; 52 , 3 ; 721-733
01.05.2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Development of a Rapid Fleet-Level Noise Computation Model
Online Contents | 2015
|Quantification of Error for Rapid Fleet-Level Noise Computation Model Assumptions (AIAA 2018-3993)
British Library Conference Proceedings | 2018
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