National Academies Press: OpenBook

Enhanced Modeling of Aircraft Taxiway Noise, Volume 2: Aircraft Taxi Noise Database and Development Process (2013)

Chapter: CHAPTER 2. TAXI DATABASE INM / AEDT INTEGRATION ASSUMPTIONS

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Suggested Citation:"CHAPTER 2. TAXI DATABASE INM / AEDT INTEGRATION ASSUMPTIONS." National Academies of Sciences, Engineering, and Medicine. 2013. Enhanced Modeling of Aircraft Taxiway Noise, Volume 2: Aircraft Taxi Noise Database and Development Process. Washington, DC: The National Academies Press. doi: 10.17226/22606.
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Suggested Citation:"CHAPTER 2. TAXI DATABASE INM / AEDT INTEGRATION ASSUMPTIONS." National Academies of Sciences, Engineering, and Medicine. 2013. Enhanced Modeling of Aircraft Taxiway Noise, Volume 2: Aircraft Taxi Noise Database and Development Process. Washington, DC: The National Academies Press. doi: 10.17226/22606.
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Suggested Citation:"CHAPTER 2. TAXI DATABASE INM / AEDT INTEGRATION ASSUMPTIONS." National Academies of Sciences, Engineering, and Medicine. 2013. Enhanced Modeling of Aircraft Taxiway Noise, Volume 2: Aircraft Taxi Noise Database and Development Process. Washington, DC: The National Academies Press. doi: 10.17226/22606.
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2-1 CHAPTER 2. TAXI DATABASE INM / AEDT INTEGRATION ASSUMPTIONS In order to add taxiing aircraft modeling capability to the INM/AEDT, changes were recommended (Page, et al., 2009) to four key elements, namely: 1. NPD Taxi Reference Speed; 2. Lateral Directivity; 3. Spectral Class (current classes are based on departure or approach flight conditions); and 4. Ground Effect (air-to-ground propagation for flight vs. ground-to-ground for taxi). This chapter itemizes the assumptions that were made in the development of the NPD datasets and the rationale behind the assumptions. Longitudinal directivity was also recommended to be changed in INM/AEDT for taxi noise modeling (Page et al., 2009). 2.1. Reference Speed The current INM and AEDT flight noise-power-distance (NPD) database includes integrated metrics Sound Exposure Level (SEL, dBA) and Effective Tone-corrected Perceived Noise Level (EPNL) and maximum level metrics Maximum A-weighted sound level (Lmax, dBA) and Maximum Tone- corrected Perceived Noise Level (PNLT, dB). These in flight metric values (approach and departure) are integrated based on a dipole moving along an infinite segment at 160 knots. The duration adjustment programmed into INM and AEDT is provided in Equation 2-1: DURADJ = 10Log10(160/ASseg) Eq. 2-1 where DURADJ is the NPD adjustment in dB and ASseg is the aircraft speed along the flight segment in knots. The same duration adjustment is used for both SEL and EPNL integrated metrics. The prior taxi study conducted for ACRP (Page, et al., 2009) examined flight recorder data for one year of operations for a major European commercial carrier. For this dataset, it was found that typical taxi ground speeds ranged from 9 to 16 knots with a standard deviation of 3-5 knots. Analysis of data published by University of Madrid, (Asensio et al., 2007, Lopez et al., 2008) indicated that most measured aircraft moved at constant speed ranging from 15-23 knots with an average taxi speed of 19.8 knots. The database development process for this project established a reference speed for NPD integrated metrics of 16 knots taxi ground speed. This reflects an exact difference of 10.0 dB in SEL and EPNL from the existing INM/AEDT 160 knot flight speed based on Equation 2-1. 2.2. Lateral and Longitudinal Directivity INM and AEDT account for source lateral directivity (Boeker et al., 2008) as part of the lateral attenuation algorithms (Figure 2-1). For airborne flight this correction can be up to 3dB in the plane of the wings. We recommend the lateral directivity adjustment be disabled for taxiing aircraft mode in INM and AEDT, and that the empirical and/or and predicted taxi NPD data be used directly in the prediction of taxi noise.

2-2 SAE AIR 5662 Lateral Source Directivity -5 -4 -3 -2 -1 0 1 -90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90 Angle (Deg) En gi ne In st al la tio n Ef fe ct (d B ) Fuselage mounted Engines Wing Mounted Engines Starboard Port FIGURE 2-1 Aircraft lateral directivity from Boeker et al. (2009). Both INM and AEDT include algorithms to model longitudinal aircraft directivity (Figure 2-2). These are applied behind the start of takeoff roll and for operations where the aircraft is “on the ground”. In the prior ACRP Taxi scoping study (Page et al., 2009) it was suggested that the existing INM / AEDT directivity behind start of takeoff roll be disabled and replaced with suitable taxi directivity. Different directivity shapes have been developed for two classes of aircraft: Turbofan (Jet) and Propeller. The taxi directivity source functions also include the region in front of the aircraft and are representative of noise emissions for taxiing operations. These are described in detail in Section 7. FIGURE 2-2 Aircraft longitudinal directivity from Boeker et al. (2009). 2.3. Taxi Spectral Class INM / AEDT spectral data for departure and approach is generally provided by the aircraft manufacturer to the FAA as part of the INM data package and are based on procedures detailed in FAR Part 36. The source of the aircraft spectra is certification flight measurements captured at the maximum A-weighted level for the flight operation. Generally this is reflective of the vehicle noise spectra near the

2-3 point of closest approach to the microphone, and represents noise emission at about 90o from the nose of aircraft. In reality the vehicle spectra varies with directivity angle. Measured taxi data permits the modeling of spectra as a function of emission angle; however this level of detail is beyond the capability of the INM / AEDT modeling structure at this point in time. For consistency with the flight spectral class data, taxi spectral classes were developed using the same criteria: namely taxi spectra at the point of maximum A-weighted noise level. Spectral classes are general shapes across the one-third octave bands normalized to 70 dB at 1000 Hz. Multiple aircraft types can have the same spectral shape with the absolute levels adjusted to produce the desired level. The existing flight spectral classes (Volpe, 1999) and the processes whereby the spectral classes are determined have been previously described (Roof, Guilding, Fleming, 2003). This same process, which includes the steps summarized below, was employed in the development of the Taxi spectral classes.  Grouping of aircraft including airframe, engine type, number of engines, engine location and engine bypass ratio;  Visual inspection of the potential spectral class data including assessment of general shape, location of tones;  Verification of suitability of the spectral class by comparing actual spectra with spectral class propagated data over soft ground for a 1000 m range and several elevation angles and comparing against a +/- 1dB criteria; and  Verification of sensitivity of spectral class within 3dB to atmospheric absorption effects over the following four atmospheric conditions: 59oF, 70%RH; 77oF, 70%RH; 59oF, 55%RH; 77oF, 55%RH for all the standard distances in the NPD dataset. 2.4. Ground Effect Development of the NPD dataset was performed using taxiing aircraft geometry. The noise source was placed at the engine height above the ground, and, to be consistent with INM and AEDT, the receiver was placed 4 ft above the ground. The intervening terrain was acoustically soft, flat ground modeled with an effective flow resistivity of 150 cgs/rayls consistent with the methodology applied for INM / AEDT database development (Roof et al., 2003).

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TRB’s Airport Cooperative Research Program (ACRP) Web-Only Document 9: Enhanced Modeling of Aircraft Taxiway Noise, Volume 2: Aircraft Taxi Noise Database and Development Process documents the procedures developed and employed in the creation of a taxi noise database for the U.S. Federal Aviation Administration’s Integrated Noise Model and Aviation Environmental Design Tool (AEDT). The AEDT is currently under development.

ACRP Web-Only Document 9: Enhanced Modeling of Aircraft Taxiway Noise, Volume 1: Scoping explores ways to model airport noise from aircraft taxi operations and examines a plan for implementation of a taxi noise prediction capability into the U.S. Federal Aviation Administration's integrated noise model in the short term and into its aviation environmental design tool in the long-term.

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