Parametric optimization of aircraft arrival trajectories for aviation noise mitigation using BADA4 performance model

Abstract

Successful mitigation of aviation noise is a key enabler for sustainable aviation growth. A key focus of this effort is the noise arising from aircraft arrival operations. Arrival operations are characterized by the use of high-lift devices, deployment of landing gear, and low thrust levels, which results in the airframe being the major component of noise. In order to optimize for arrival noise, management of the flap schedule and gear deployment is crucial. This research aims to create an optimization framework for evaluating various aircraft trajectories in terms of their noise impact. A parametric representation of the aircraft arrival trajectory will be created to allow for the variation of aircraft's flap schedule. The Federal Aviation Administration's Aviation Environmental Design Tool will be used to simulate the aircraft trajectory and performance, and to compute the noise metrics. Specifically, the latest performance model from EUROCONTROL called "Base of Aircraft Data - Family 4" will be used. This performance model contains higher fidelity modeling of aircraft aerodynamics and other characteristics which allows for better parametric variation.

An Experimental and Analytical Approach to Evaluate Transponder-Based Aircraft Noise Monitoring Technology

“…The Aviation Environmental Design Tool (AEDT) has been a cornerstone in aviation environmental impact assessment in the U.S., particularly at airports where The Aviation Environmental Design Tool (AEDT) has been a cornerstone in aviation environmental impact assessment in the U.S., particularly at airports where comprehensive aircraft operation and fleet mix data are readily available [11,12,17]. Researchers have explored strategies for optimizing aircraft departure and arrival procedures to enhance aviation environmental impact assessments, heavily relying on AEDT [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Notably, Behere et al [30] pioneered a data-driven approach to refining noise assessments using AEDT and openly accessible surveillance data.…”