Environmental analysis of aircraft flight trajectories based on predictions and ATTAS fly-over noise measurements

Transcription

1 Environmental analysis of aircraft flight trajectories based on predictions and ATTAS fly-over noise measurements DGLR Workshop, June 2010, Braunschweig L. Bertsch 1, G. Looye 2 German Aerospace Center (DLR) 1 Institute for Aerodynamics and Flow Technlogogy; 2 Institute of Robotics and Mechatronics

2 Outline Motivation Environmental Analysis Process Process application Flight Test & Validation Conclusions and Outlook Folie 2

3 Motivation Status Quo: Increasing air traffic & public annoyance for aircraft noise Possibilities for aircraft noise reduction: Noise source modification Noise protection measures Noise restrictions (ban, curfew, quota) Operational procedures 2009 by Spiegel.de Here: Focus on approach procedure noise reduction Procedural noise reduction measures: 1. Altitude & configuration variation (e.g.: steep approach, late gear ext.) 2. Noise dislocation (routing) Combination leads to radical 3D solutions Promises significant & large-area ground noise reduction Folie 3

4 Environmental analysis process Involves various disciplines highly interdependent Inevitable for environmental analysis: simultaneous consideration of 1) and 2) balanced approach fully automated process Aircraft ground noise & emission analysis: 1. Aircraft / engine design 2. Operational procedure DLR: specialized institutes/departments with individual tools Harmonize in/output formats: Common Parametric Aircraft Configuration Standard (CPACS) XML format File wrappers Interconnect tools: PHX ModelCenter DLR wide accessible client/server architecture Tools stay at the expert DLR internal project Folie 4

5 Environmental analysis process Process input data Aircraft / engine design Flight aerodynamics If required: Noise shielding factors CPACS Noise/Emission Prediction Flight Mechanics Environmental analysis of aircraft flight trajectories Folie 5

6 Environmental analysis process Implemented tools Mission Simulation (MisSim) Input: Aircraft & engine design, engine performance, aerodynamics Object-oriented language Modelica Single aircraft model various types of runtime code here: very same aircraft model implementation as for autopilot Inverse simulation compared to autopilot: Inputs: trajectory (waypoints), velocity, acceleration, configuration Outputs: control surface deflections, thrust requirement, attitude Definition of flight trajectories: Segmentation & waypoints Parametric definition Output: Trajectory of the aircraft Process design variables Folie 6

7 Environmental analysis process Implemented tools Parametric Aircraft Noise Analysis Module (PANAM) Input: Aircraft / engine design, engine performance map, flight path, noise shielding factors (if required) Noise prediction for conventional aircraft (3D trajectories) Source models for major noise components Semi-empirical: low CPU demand & realistic physics Parametric: noise emission re config. & operating condition changes Output: Ground noise impact R. Kuchar, DLR-RM Process design objective Folie 7

8 Environmental analysis process Process output data Noise prediction: Time integrated and maximum levels Level-time-history real-time analysis Single observers or arrays DLR noise metric: Aircraft Noise Induced Awakenings* by Basner et al. (2006) Gaseous emissions: CO, CO 2, HC, H 2 O, NO X, Soot, SO Fuel consumption Duration of flight segment Max. SPL Awakening propability Aircraft Noise Induced Awakenings + Population density *) M.Basner, A.Samel, U.Isermann: Aircraft noise effects on sleep: Application of the results of a large polysomnographic field study, Journal of the Acoustical Society of America, 119(5), pages , May 2006 Folie 8

9 Process application Radical operational solution: Helical Noise Abatement Procedure (HeNAP)* High initial approach altitude Spiraling final descent Predicted noise dislocation effects: 1. Noise impact concentration in area around the spiral Multiple flyover events per approaching aircraft Descent area: ideally low-populated region e.g. industrial zone 2. Significant (!) reduction along entire preceding flight path Increase in flight time and fuel consumption (!) PANAM realtime noise prediction Fig.: predicted noise dislocation effects along spiraling approach *) C.Hange, D.Eckenrod: Assessment of a C-17 Flight Test of an ESTOL Transport Landing Approach for Operational Viability, Pilot Perceptions and Workload, and Passenger Ride Acceptance, AIAA Folie 9

10 Process application HeNAP(3) vs. standard ILS approach Noise reduction potential & operational impacts Fuel NOx Flight time ILS approach 209 kg 1.71 kg s HeNAP + 57% + 39% + 205% Computational analysis: significant noise reduction but increased flight time and fuel consumption Folie 10

11 Flight test Scheduled DLR flight test: Evaluate new in-house autopilot with enhanced weather capabilities Flying testbed ATTAS unique in-flight simulation architecture & fly-by wire flight control system quick implementation of new flight control laws Fig.: DLR ATTAS Idea: Combination of autopilot flight test with HeNAP evaluation Autopilot test in the form of a landing maneuver Aspects for HeNAP in-flight evaluation: 1. Technical feasibility (benchmark test for autopilot) 2. Ground noise reduction potential Combined noise measurement campaign 3. ATM aspects Evaluation of air traffic controller workload and tower activities Folie 11

12 Flight test Flight mechanics Flight procedures: constant configuration (flaps 15, gear deployed) 1. ILS approach (reference procedure) 3 GS go-around at 500 ft 2. steep approach 6-7 GS ILS capture from above go-around at 500 ft 3. HeNAP 3 helical segments 7500 ft initial altitude Zero initial path angle ILS capture after third helix go-around at 500 ft Fig.: flight procedures Folie 12

13 Wind (kts) Go-arounds Results Runway EDVE Helical approaches h [m] Steep approaches X [m] Ground tracks 1000 Folie 13 Y [m]

14 Flight test Flight mechanics Results: velocities V CAS V V compl V CAS, V, V compl [m/s] time [s] Go-around Folie 14

15 Flight test Flight mechanics Results: roll and side slip angles Roll angle Side slip angle (sensor) Side slip angle (synth.) 25 20, [deg] time [s] Folie 15

16 Flight test Flight mechanics Results: crab angle Folie 16

17 Flight test Flight mechanics Results: lateral track error capture Lateral error [m] time [s] Capture ILS Folie 17

18 Flight test - Noise Approaches on RWY 26 of Braunschweig (EDVE) Flight test program: 7 test flights & 12 microphone positions 2 standard ILS (yellow), 2 steep (blue), and 3 HeNAP (red) Curved flight: noise effects Noise reduction potential Fig.: flight path, flight ground track, and observer locations M1 to M12 Folie 18

19 Flight test - Noise Noise measurement equipment: According to ICAO noise certification standards Autonomous field noise measurement system 12 plate mounted microphones & laptop-based data acquisition system Pole mounted weather sensor system Digitized sound pressure signal sampling rate of 48 khz and 16 bit resolution Correlated with GPS time and pulse-per-second (PPS) Fig.: plate mounted microphone Fig.: Pole mounted weather sensor system Folie 19

20 Flight test - Noise Noise measurements: Only available test day: adverse wind cond. 15 kts wind gusts (dir.: 255, RWY: 265 ) Similar measurements for identical procedures Constant wind/weather conditions Data valid for comparative noise level evaluation Folie 20

21 Flight test - Noise Noise measurements: Measurements confirm predicted / expected trends Noise concentration and dislocation effects along HeNAP Significant noise reduction at Mic5 & 6 Fig.: observer locations Fig.: Recorded noise level differences wrt reference approach (ILS, flight 1) Folie 21

22 Flight test ATM issues ATM aspects: DFS* R&D personal at EDVE TWR during test flights Air traffic controller: Increased workload: monitoring altitude and number of spiral Fig.: EDVE Tower Vertical and horizontal separation issues WTC medium (ATTAS): 1000 ft vertical, 3 NM horizontal 1 helix element 7 NM, however WTC behavior is unclear one aircraft at a time within the helix Negative capacity aspects *) German Air Navigation Service Provider Folie 22

23 Noise prediction: Validation Prediction vs. exp. data: Input: recorded flight data Poor agreement of absolute numerical values Caused by wind? Distorted sound field & corrupted complex shielding effects Good agreement of level differences Confirmation of initially predicted noise effects Observer locations Folie 23

24 Visualisierung Folie 24

25 Conclusions & Outlook DLR process: environmental analysis of flight procedures Process application and ATTAS flight test: HeNAP In-flight demonstration of technical feasibility Fully automated flight operation: new DLR autopilot (RM) Procedure not applicable with current technology Exp. data confirm expected and predicted effects Noise reduction up to 9 dba High noise levels concentrated into limited area No additional noise due to curved operation Increased fuel use & flight time New framework is applicable for low-noise procedure design Folie 25

26 Conclusions & Outlook Further investigation of the HeNAP Detailed analysis of test data and operational ATM aspects Variation of configuration setting for low-noise operation Variation of helix parameters Application to real-world scenario Population density and airport boundaries Fast time ATM simulation Interface to DLR Inst. of Flight Guidance Separations & delays Effect of multiple flyover events on public annoyance by aircraft noise ILS Z RWY34L HeNAP (3 Seg.) Medium-range transport aircraft approaching Tokyo Haneda International Airport RWY 34 L Folie 26

27 Acknowledgments Volunteers & staff from DLR, RWTH Aachen University, and Deutsche Flugsicherung DFS special thanks to E. Anton (RWTH Aachen), S. Schwanke (DFS), D. Leißling, and M. Press (both DLR) More information: visit news archive, 2009 Folie 27