# Modeling and Analysis of Noise Emission Using Data from Flight Simulators
## 1 Introduction
The downside of this type of depiction is that it lacks in accounting for abrupt and short changes in noise exposure, e.g., caused by delaying maneuvers, such as holdings or procedural turns, which are shown to inflict more negative residents' responses [21]. For the purpose of this study, only single-event noise metrics were used--\(SEL\) (sound exposure level) and \(L_{Amax}\) (the maximum A-weighted level of noise recorded by the observer). It is emphasized that although the ICAO Doc 9911 recommends that the aircraft noise calculations shall only be applied to model the long-term averaged noise metrics, the methodology for calculating the single noise event remains unchanged. During the process of creating the software that would allow for generating such noise maps, the modelers often need to rely on incomplete data sets and simplification techniques. Figure 1: Profile view—comparison of NADP1 and NADP2. With the scarcity of aircraft's trajectories and precise radar data, or even more commonly, the lack of data of thrust settings or aircraft's body angles, which can be only acquired from an on-board Flight Data Recorder, modelers often need to synthesize aircraft trajectories that are based upon simplified methods, such as default procedure profiles or steps [22] The steps are then evaluated based on statistical characteristics (e.g., Gaussian distribution) [23]. Some researchers acquire flight data through an ADS-B equipment, which is nowadays mounted onboard most large aircraft [24], while others suggest that a specially trained Neural Network might help to predict the weight, thrust, and flap settings for a given flight using only surveillance equipment (such as ADS-B) [25]. Until recently, assessing an aircraft's trajectory using simulation methods was considered a very precise, but also costly, process with a limited scope of application. Work in [26] presents a unique rapid prototyping environment, which enables an evaluation of the aircraft's noise impact. The NOSIM system, presented in this work, combines a flight simulator, a noise model, and a geographic information system (GIS). The flight simulator component of NOISIM uses advanced performance data from the Boeing 737 [27]. The Aircraft Noise Simulation Working Group (ANSWr) was established by DLR, ONERA, and NASA to compare simulation tools, establish guidelines for noise prediction, and assess uncertainties associated with the simulation [28]. ANSWr develops its own scientific simulation tools for the prediction of overall aircraft system noise. PANAM (the Parametric Aircraft Noise Analysis Module) by DLR [29], ANOPP (Aircraft Noise Prediction Program) by NASA [30], and CARMEN by ONERA [31] are advanced simulation tools developed for many years (2000--PANAM, 1970s--ANOPP, and 2008--CARMEN) [28]. Nowadays, widely available and popular flight simulators, such as X-Plane or Microsoft Flight Simulator present a possible new, cheaper way of flight data acquisition for use in Noise and Emission modeling. The difference in the simulation of aircraft dynamics in these two popular simulators is significant. Microsoft Flight Simulator software uses the classic six degree of freedom 6 DOF model of motion [32], while X-Plane uses the blade element theory developed originally to evaluate the behavior of the propeller [33]. In the blade theory method, each element of the aircraft is divided into small elements, for which the forces and moments are calculated individually. The obtained results are summed to obtain an overall result for the simulation object. Certain add-ons for various airliners made by third-party developers, such as Fenix Simulations or PMDG, allow for almost a study-level experience and very close representation of the aircraft's handling and performance characteristics, e.g., a climb gradient specific to the aircraft's mass or air density, which is of paramount importance in proper noise modeling.