Reduced Order Modeling Methods for Aviation Noise Estimation

Ameya Behere, Dushhyanth Rajaram, Tejas G. Puranik, Michelle Kirby and Dimitri N. Mavris
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Ameya Behere: Aerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Dushhyanth Rajaram: Aerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Tejas G. Puranik: Aerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Michelle Kirby: Aerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Dimitri N. Mavris: Aerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA

Sustainability, 2021, vol. 13, issue 3, 1-19

Abstract: A key enabler for sustainable growth of aviation is the mitigation of adverse environmental effects. One area of concern is community noise exposure at large hub airports serving growing population centers. Traditionally, community noise exposure is computed using noise contours around airports, which requires knowledge of a large dataset pertaining to the air traffic operations at the airport of interest. Due to the underlying variability in real-world aircraft operations, numerous assumptions need to be made which adversely affect the accuracy of the model. Reduced-Order Modeling (ROM) methods provide a new framework for the retention of a large number of these parameters, thus improving model speed and accuracy. In this work, a proper orthogonal decomposition in conjunction with a response surface methodology-based surrogate model is used to create a rapid noise assessment model. Validation is performed against results obtained from the aviation environmental design tool with quantitative error metrics and visual contour comparisons. Obtained results are encouraging and motivate further work in this area with other ROM methods. ROM based models for noise assessment expand the solution space for noise mitigation strategies which can be evaluated, and therefore can lead to novel solutions which cannot be found with traditional modeling methods.

Keywords: air transportation; reduced order modeling; community noise; airports (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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