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

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3-1 CHAPTER 3. DATA SOURCES In our previous taxi noise study for ACRP (Page et al., 2009b) we conducted a comprehensive search for noise data measured at the low thrust levels associated with the taxiing operation. As expected, the amount of available data is extremely limited because taxi noise has not received the same attention as has the noise from flight operations. The most useful data was from the following sources:  Taxi noise spectral and directivity data measured for a wide variety of aircraft by the Universidad Politenica de Madrid (Arsensio et al, 2003), (Lopez et al., 2004);  In-house turboprop noise and directivity data measured by Wyle; and  Taxiing noise data collected by Wyle in 2008 at Providence Airport as part of a PARTNER study on emissions, headed by Harvard University. In order to supplement this database for the ACRP project, Wyle conducted three sets of measurements, namely:  Taxi idle and acceleration noise measurements at Ronald Reagan National Airport (DCA); and  Taxi “breakaway” noise and noise directivity measurements at Washington Dulles International Airport (IAD) in 2008; and  Taxiing noise directivity measurements targeting large international aircraft (B777) at Washington Dulles International Airport (IAD) in 2010. The resulting noise database in terms of aircraft types measured is shown in Table 3-1. TABLE 3-1 Available Noise Data for Aircraft Taxi Operations Organization Airport Aircraft Measured University of Madrid Madrid Barajas (MAD) A-310-300, A319, A320, A321, A340-300, B717 (-300, -400, -500), B737 (-600, -700, - 800), B747, B757-200, B767, MD-82, MD-83, MD-87, MD-88, ATR-72-500, CRJ, DHC8Q3, Fokker 50 Wyle DCA CRJ, B737 (-300, -700, -800), E145, MD88, A319, B727 Wyle IAD A320, B757-222, B777 Wyle PVD CRJ, B737-700, Embraer 170, ERJ-145 HMMH ANC B747, B767 Boeing -- B737-300 3.1. Measured Taxi Noise Data Under ACRP Project 02-27 a procedure was developed by which the acoustic power description of the taxiing aircraft described in Lopez (2004) can be processed into free-field spectral sources with longitudinal directivity using the appropriate ISO standards. These free field noise sources may then be utilized in a simulation model such as the Advanced Acoustic Model (AAM) (Page, et al., 2009a) in order to develop NPD curves for the reference 16 knot taxi condition. This section describes the technical basis for noise sphere creation from the acoustic measurement data and highlights its application to specific aircraft. Comparisons between processed taxi acoustic power data and independent measurement data demonstrate the validity of this process and justify further use of the Wyle and Madrid empirical data in the development of an INM/AEDT NPD dataset.

3-2 The “Madrid data” presented by Lopez et al. (2008) provides acoustic sound power output and directivity indices of several aircraft types measured while taxiing at the Madrid-Bajaras airport in Spain. The authors calculated sound power by utilizing two different methods. One of the methods employed, method B in the report, used the ISO standards (ISO, 1993) (ISO, 1996), to depropagate the measured sound levels to calculate the sound power level of the taxiing aircraft. This methodology is a good approximation of the acoustic repropagation technique (Page et al., 2009a), (Hobbs, Page, Schultz, 2010) and accounts for air absorption. The sound power levels resulting from method B were chosen for creating noise spheres representing taxiing aircraft. Further details about this process have been described by Hobbs, Sharp and Page (2010b) but are repeated in Appendix A for completeness. The taxi noise measurements conducted by Wyle and itemized in Table 3-1 were also processed using the acoustic repropagation technique in order to develop taxi NPD data. A comparison of the various taxi B737 longitudinal directivity empirical noise source data is provided in Figure 3-1. Appendix B contains a description of the B777 2010 IAD Taxi measurements and data processing used to obtain empirical taxi noise NPDs. FIGURE 3-1 Comparison of B737 measurement noise directivity data. Table 3-2, taken from Hobbs et al. (2010b), itemizes the comparison of the measured SEL levels (Ave. Meas. SEL column) with those predicted SEL data based on the noise spheres created from the Madrid dataset (Madrid SEL column). The difference in SEL has been averaged for each vehicle category and listed in the Average SEL Difference column. Overall the SEL comparisons are within 3dB, while individual events might vary more. Given the number of variables in the measurement taxi operations, this demonstrates the suitability of use of the Madrid dataset for taxi noise NPD dataset development. 60 65 70 75 80 85 90 95 100 0 20 40 60 80 100 120 140 160 180 O A SL (d B ) Directivity Angle (deg) Ground Idle for 20-40k T Engine B737 13kts Evt2 B737 12kts Evt6 B737 14kts Evt15 B737 8kts Evt19 A319 17kts Evt14 HMMH B737-300 Madrid Data

3-3 TABLE 3-2 Comparison of SEL Levels AC Type Speed (knots) Distance From Source (m) Ave. Meas. SEL Madrid SEL Average SEL Difference (Pred-Meas) A319 18 78 91 94 -1 A319 17 22 104 99 B737-300 12 22 107 101 -3 B737-300 8 78 97 103 B737-700 14 22 102 101 B737-800 2 22 104 101 B737-800 13 22 104 95 CRJ 16 22 98 99 2 CRJ 12 78 88 93 CRJ 15 78 96 92 CRJ 200 10 22 99 101 CRJ 200 15 22 99 100 CRJ 200 14 22 98 100 CRJ 200 12 78 92 93 CRJ 200 14 78 89 93 E135 9 22 101 102 E145 6 78 88 94 B717-200 18 78 100 94 -2 MD88 8 22 104 105 MD88 12 78 91 95 MD88 23 22 105 100 3.2. Taxi Operational Data None of the empirical taxi noise data collected under ACRP funding or itemized in Table 3-1 have concurrent information on engine operating state. This necessitates an assumption of the engine operating condition under which the acoustic data was gathered. Under ACRP Project 11-02 Task 8, analysis of one year of flight data recorder (FDR) data1 was performed (Page et al., 2009b) in order to obtain nominal taxi conditions. The results of this analysis are presented in Table 3-3. Additional sources of taxi data include results from a number of surveys of power settings used during normal taxi operations currently under consideration by the International Civil Aviation Organization - Committee on Aviation Environmental Protection Alternative Emissions Methodology Task Group (ICAO/CAEP7, 2006). One additional source of data identified under the current effort and leveraged in this study includes the ICAO Best Practices Certification Database (BPDB) (ICAO/CAEP8) which lists aircraft takeoff gross weight, and maximum thrust, from which nominal percentage taxi thrust may be assumed. 1 The Event Measurement System (EMS) developed by Austin Digital performs uses a combination high-frequency component from “dead reckoning” with a low-frequency component from GPS latitude and longitude or inertial lat/long. Positions are determined by combining position estimates based on ILS deviations, marker over flight times and recorded lat/long. The altitude component combines estimates from runway elevation + height AGL, integrated vertical speed and barometric corrected altitude. http://www.ausdig.com.

3-4 TABLE 3-3 FDR Data Engine Operating Parameters from Page et al. (2009b) Aircraft Average  N1average Standard  Dev.  N1avg Average  %Thrust Standard  Dev.  %Thrust Average  EMS  Thrust (lbs) Standard  Dev. EMS  Thrust (lbs) Average  EMS  Enhanced (lbs) Standard  Dev. EMS  Enhanced (lbs) A319 19.42 1.41 8.41 1.18 1975.24 278.42 1975.24 278.42 A320 19.16 1.29 7.45 1.34 2011.77 361.01 2011.77 361.01 A321 20.06 1.55 6.15 1.15 1843.61 345.29 1843.61 345.29 A330 21.16 3.08 3845.13 2484.18 A340 19.50 4.73 2210.70 1027.83 B757 20.47 1.51 2.68 0.67 1077.75 268.58 1077.75 268.58 B767 23.78 2.66 5.74 1.24 3565.83 772.65 B777 19.89 2.30 4.86 0.86 5615.41 989.00 RJ100 22.67 1.80 1518.85 139.30 RJ85 22.32 1.59 1500.55 120.04 Aircraft Average  N1average Standard  Dev.  N1avg Average  %Thrust Standard  Dev.  %Thrust Average  EMS  Thrust (lbs) Standard  Dev. EMS  Thrust (lbs) Average  EMS  Enhanced (lbs) Standard  Dev. EMS  Enhanced (lbs) A319 17.37 3.98 8.62 2.20 2026.47 516.45 2026.47 516.45 A320 17.51 3.04 7.72 1.89 2083.66 510.60 2083.66 510.60 A321 18.21 3.82 6.78 1.97 2034.77 591.40 2034.77 591.40 A330 21.51 4.25 5.80 4.46 3947.56 3035.05 2815.96 3582.02 A340 19.20 3.93 6.38 2.85 2590.74 839.84 1516.45 1520.39 B757 19.64 2.50 1.39 0.87 560.33 347.74 1077.75 347.74 B767 26.79 1.56 6.09 4.47 3781.28 2777.72 560.33 B777 21.41 0.91 5.46 0.43 6312.84 496.08 RJ100 17.51 6.52 1157.97 449.01 RJ85 18.03 5.69 1194.22 381.76 Aircraft Average  N1average Standard  Dev.  N1avg Average  %Thrust Standard  Dev.  %Thrust Average  EMS  Thrust (lbs) Standard  Dev. EMS  Thrust  (lbs) Average  EMS  Enhanced  (lbs) Standard  Dev. EMS  Enhanced  (lbs) A319 19.56 3.34 9.20 1.92 2162.66 451.69 2162.66 451.69 A320 19.71 3.29 8.22 1.85 2220.48 498.40 2220.48 498.40 A321 20.32 3.13 6.89 1.43 2066.97 429.37 2066.97 429.37 A330 22.28 3.30 4261.80 2792.32 A340 20.45 4.08 2407.10 1008.95 B757 23.28 2.20 3.73 1.01 1500.41 405.07 1500.41 405.07 B767 26.14 1.71 6.58 1.14 4085.75 708.94 B777 20.08 1.94 5.16 0.77 5960.23 884.92 RJ100 25.49 2.45 1722.56 172.80 RJ85 24.59 2.11 1669.50 166.76 Aircraft Average  N1average Standard  Dev.  N1avg Average  %Thrust Standard  Dev.  %Thrust Average  EMS  Thrust (lbs) Standard  Dev. EMS  Thrust (lbs) Average  EMS  Enhanced (lbs) Standard  Dev. EMS  Enhanced (lbs) A319 19.94 1.05 9.89 0.70 2323.04 164.33 2323.04 164.33 A320 19.62 1.38 8.70 1.32 2350.22 355.09 2350.02 355.09 A321 20.72 1.55 7.62 0.57 2284.95 169.66 2284.95 169.66 A330 23.15 2.23 6.56 4.26 4459.99 2892.61 2886.91 3742.11 A340 20.03 2.55 7.04 2.54 2862.06 555.44 1672.96 1585.15 B757 22.29 1.79 3.34 0.61 1341.46 245.01 1341.46 245.01 B767 27.17 2.13 6.65 0.89 4130.46 549.77 B777 21.53 0.45 5.48 0.31 6336.52 359.74 RJ100 23.84 6.19 1599.68 431.08 RJ85 23.44 6.67 1578.58 463.89  Note: Some A330, A340, RJ100 and RJ85 values were erroneous in the FDR dataset and removed. Blank fields indicate  that specific data is not available.  Engine Operating Parameters ‐ Stationary, Departure Taxi Operations Engine Operating Parameters ‐ Stationary, Arrival Taxi Operations Engine Operating Parameters ‐ Stationary, Arrival Taxi Operations Engine Operating Parameters ‐ Stationary, Arrival Taxi Operations

3-5 3.3. ANOPP First Principles Modeling Data The Aircraft Noise Prediction Program (ANOPP) developed by NASA (Zorumski, 2006) may be used to physically model engine noise as a function of thrust, and exercise the engine noise model to develop noise-thrust sensitivity curves for low thrust levels. The ANOPP software was used to develop noise spheres for use in the Advanced Acoustic Model (AAM). The inputs required for ANOPP are extensive and generally proprietary for specific engine models, but public domain input decks have been developed as part of the Environmental Design Space (EDS) project (Kirby & Mavris, 2008), (Barros, Kirby & Mavris, 2008) funded by the FAA, and were made available to the ACRP researchers by Georgia Tech for the following aircraft/engine combinations:  B737-800/CFM56-7B27 (Narrow Body, 2-Engine);  B777-200ER with GE90-94B and PW4090 engines (Wide Body, 2-Engine);  B747-400/PW 4056 (Wide Body, 4-Engine);  B767-300 ER (CF6-80C2); and  CRJ-900/CF34-8C5 (Narrow Body, 2-Engine, Tail Mounted). The EDS estimated performance maps for these engines have not been validated at extremely low thrust levels, but were acquired believing that they could be used in the range of 8 to 10 percent of maximum rated power for the purposes of developing level and spectral trends with thrust changes (Berton, 2009). Fortunately, the predicted taxi noise levels from ANOPP are remarkably close to measured values, supporting our assessment of the suitability of such data in the development of the taxi NPD noise-thrust relationship. (Comparisons of ANOPP and empirical data are provided in Appendix C, Figures C-1 through C-5 and also in Section 4 of this report). The process employed utilizes a physics-based approach based on ANOPP modeling to build noise spheres representative of the taxi condition to account for physical elements: thrust changes, and spectral content and directivity changes. ANOPP was also used to explore the following areas and ensure proper modeling application to the taxi condition. The following modeling concepts were explored using ANOPP. Ground vortex ingestion: A narrow band analysis of measured taxi data was performed to determine the fan rotation speeds and infer engine operating state. A vortex ingestion model based on stream tube was developed to determine relationship between engine operating state, forward speed and presence of ground vortex ingestion. It was determined that for aircraft with wing mounted engines a ground vortex is ingested under nearly all taxi and idle operating conditions. Therefore ANOPP modeling applied inflow distortion for the taxi condition. As part of this investigation the taxi team recommends that NASA develop an improved empirical vortex ingestion model for ANOPP. An empirical approach, which leverages a planned static engine test, could utilize “Ground Vortex Destroyers” and Turbulence Control Screens separately and together to identify vortex ingestion impact. The empirical data may then be used to develop an additional vortex ingestion source noise model for the Fan method of ANOPP. Additional details may be found in Appendix D. Extrapolation of engine state tables to idle/low thrust using NPSS. Engine state tables were extended to idle conditions (5% thrust) by Georgia Tech and utilized in the ANOPP analyses. Comparisons of the default ANOPP extrapolation procedure with the use of the extended engine state tables showed that ANOPP extrapolation provides a reasonable approximation. Exploration of airframe noise using ANOPP and removal of airframe noise from INM approach NPDs. This process is detailed in Section 4.1 of this report and includes both geometric and logarithmic methods for airframe noise subtraction. Unfortunately, this technique was not particularly successful. A complete set of comparison plots are also provided in Appendix E.

3-6 Component “balancing” using general suppression tables. See Appendix C for a discussion of the general suppression tables in the flight ANOPP input files and the justification for removing them when using ANOPP to model the taxi condition.