SESAR P Project WVDSS First XP1 CDG Trials Results

Transcription

1 SESAR P Project WVDSS First XP1 CDG Trials Results Frédéric Barbaresco & Philippe Juge Thales Air Systems

2 2 / Agenda SESAR P scope System description XP1 campaign Next steps

3 3 / Agenda SESAR P scope System description XP1 campaign Next steps

4 4 / SESAR P Project Objectives Safely reduce wake vortex separations for arrival & departures Define, analyze, develop and verify a Wake Vortex Decision Support System WVDSS shall process weather & surveillance information, airport specific climate conditions, aircraft characteristics, runways layout WVDSS shall support the 3 phases in SESAR story board TBS (Time Based Separation) step 1 Acquisition and processing of wake vortex information in significant head-wind WDS (Weather Dependent Separation) step 2 Real time assessment of wake vortex to allow separation reduction PWS (Pair Wise Separation) step 3 Capability to dynamically deliver separation per aircraft pairs WVDSS iterative design supports the validation of the SESAR wake-vortex operational concept

5 5 / Wake-Vortex Detection, Prediction & Decision Support Tool Main functions Propose the separation mode to the supervisor (ICAO or Reduced separations) and time of applicability Process wind data including turbulence information and air traffic situation to propose spacing to Controllers Monitor air traffic situation against spacing and/or wake vortex predictor output Provide advisories in case of actual or predicted danger Handle Wake Vortex Sensors outcomes to check WakeVortex predictor reliability Thales Air Systems 17/05/2013

6 6 / Agenda SESAR P scope System description XP1 campaign Next steps

7 7 / WVDSS Architecture

8 8 / Separation Mode Planner (SMP) Separation Mode Planner The Separation Mode Planner (SMP) advises on the applicable aircraft separation mode ( ICAO or Reduced mode) and the minimum separation based on weather forecast data. This advice is used at strategic level by the ATC Supervisor to decide on the arrival and departure rates to be applied. Forecasting of Cross-Wind Reduced Reduced ICAO separation Time Frames (in Red) and reduced separation Time Frames (in Green) Thales Air Systems Division

9 9 / WAKE4D (3-D spatial + temps): Wake Vortex Predictor Characteristic of Wake-Vortex Predictor Computation of Wake-Vortex positions and strength (circulation in m2/s) in perpendicular planes of glide slope. Probabilistic runs (multi-runs with variation of initial conditions according to input data accuracy) Computation plan plane Thales Air Systems 28/03/2012

10 10 / Weather: MHRPS (Mesoscale High Resolution Prediction System) MHPRS provides by web requests 50 profiles (one every 0.5 NM) in the glide slope MHRPS: Domain: 100km x 100km Spatial Res.: 500m x 500m Global Model Inputs: 500km x 500km at 2.5km Format: Grib 2 WVDSS Profile Request: Lat x Long (interpolated profiles) 113 levels in altitude 50 levels under 3000m Data (per MET point) MHRPS EDR in horizontal Plane HEADER Parameter Unit Description Class REQ type - Type of REQ («Predictor», «SMP», ) XML Note : the parameters for Data type - Origin of data («MHRPS» or «SINTEF» ) XML DATA (per Met. Point) Parameter Unit Description Class T s Time where Met. data is needed of the point #1 XML (POSIX timestamp) (2) LAT #1 degre WGS84 latitude coordinate of the point #1 XML e LONG #1 degre WGS84 longitude coordinate of the point #1 XML e HEIGHT #1 m WGS84 height coordinate of the point #1 XML U m/s West wind component of the point #1 XML Sigma_U m/s Accuracy of previous parameter of the point #1 XML V m/s South wind component of the point #1 XML Sigma_V m/s Accuracy of previous parameter of the point #1 XML W m/s Upward wind component of the point #1 XML Sigma_W m/s Accuracy of previous parameter of the point #1 XML Tv K Virtual potential temperature of the point #1 XML Sigma_Tv K Accuracy of previous parameter of the point #1 XML TKE m 2 /s 2 Turbulent kinetic energy of the point #1 XML Sigma_TKE m 2 /s 2 Accuracy of previous parameter of the point #1 XML EDR m 2 /s 3 Eddy Dissipation Rate of the point #1 XML Sigma_EDR m 2 /s 3 Accuracy of previous parameter of the point #1 XML PS Pa Surface Pressure of the point #1 XML Sigma_PS pa Accuracy of previous parameter of the point #1 XML Hu % Humidity of the point #1 XML Sigma_Hu % Accuracy of previous parameter of the point #1 XML

11 11 / Wind Forecast Model : MHRPS ARPEGE GLOBALE MODEL CYCLE 6:00 7:00 8:00 9:00 10:00 11:00 12:00 AROME FRANCE FOREACST 8:00 COUPLING FILES observations 8:00 MHRPS_1 ANALYSIS 8:00 MHRPS_1 FORECAST & COUPLING FILES 8:00 MHRPS_2 FORECAST 9:00 COUPLING FILES observations 9:00 MHRPS_1 ANALYSIS 9:00 MHRPS_1 FORECAST & COUPLING FILES 9:00 MHRPS_2 FORECAST observations MHRPS limits Mesoscale High Resolution Prediction System : 100 x 100 km² centered on the airport with more than 100 levels in vertical (50 levels below 3000 meters) 500 meters horizontal gridpoint size Thales Air Systems 09th of October 2012

12 12 / Turbulence Calculation Mechanical turbulence is caused by Airport buildings, or other large obstructions. When strong winds are bent around the obstacles, this creates the turbulence. When the wind is strong and pass over a large enough obstacle they can create turbulent areas as high as 10 time obstacle height. 10 x 10 km² centered on the airport 100 meters horizontal gridpoint size to better assess ground effect 12

13 13 / WVDSS Sensors Multi-Function Electronic scanning X-band Radar Multi-Function Low-Cost 1.5 micron 3D Scanner Lidar UHF Radar Wind Profiler Anemometers

14 14 / WVDSS Technical HMI

15 15 / WVDSS Simulation platform

16 16 / Wake-Vortex Scenario Generator (based on UCL WAKE4D) «Wake-Vortex Predictor» Input parameters : Aircraft characteristics : weight, wing span, speed Weather data : 50 profiles provided by Weather Forecast Model (MHRPS from Meteo-France in SESAR) : wind/windshear, ambient air turbulence (EDR: Eddy Dissipation Rate), stratification from MHRPS Computation of Wake-Vortex positions and strength (circulation in m 2 /s) in glide slope.

17 17 / Agenda SESAR P scope System description XP1 campaign Next steps

18 18 / Installation on the two sites of CDG Public Zone Restricted Zone

19 19 / XP1 Trials at Paris CDG Airport September-October 2012 Operational environment Focus on aircrafts arrivals

20 20 / SESAR P XP1 first results Wake vortex sensors Complementarity between X-Band radar and lidar allows wake vortex detection in all weather conditions Good detections results for each sensor in its weather conditions domain. See dedicated presentation No more lack of detection just after the rain Weather sensors Thales multifunction radar: wind and rain rate measurements consistant with Selex radar outputs Lidar and UHF wind profiler results confirmed Radar, Lidar and wind profiler data assimilation in real time demonstrated. Local sensors data improve the forecast. Multifunction X-band radar and Lidar, plus UHF wind profiler are the adequate sensors set to ensure system performances

21 21 / SESAR P XP1 first results Weather models MHRPS characteristics compliant with WVDSS needs MHRPS calculation in real time demonstrated Weather data cube exchanges validated Turbulence calculation not yet used in real time. First results available System Wake 4D calculation in real time demonstrated Global system real time capacity demonstrated Wake vortex data fusion was not working during XP1. Sensors data have been used separately First results confirm the defined architecture Positive results in TBS verification and beyond

22 22 / Agenda SESAR P scope System description XP1 campaign Next steps

23 23 / Next steps Take into account the regulation evolution Validate the WVDSS Optimise the performances Enlarge the scope

24 24 / Take into account regulation evolution Time Based Separation (TBS) Weather Dependant Separation (WDS) Pair Wise Separation (PWS) Phase 0 P Phase 1 P Phase 2 P Phase 3 Dynamic Pair Wise

25 25 / Take into account regulation evolution Weather based V3 V3 Time based RECAT EU V3 V3 System V3 RECAT - 3 Dynamic Pair Wise RECAT

26 26 / Next steps Take into account the regulation evolution Validate the WVDSS Optimise the performances Enlarge the scope

27 27 / Validate the WVDSS Validation in platform System performances assessment Validation on specific scenario Human factors 2 validation exercises planned Time Based Separation + RECAT 1 : 2014 Weather Dependant Separation + RECAT2 + RECAT3 partial : 2015 Large scale on field validation Need to get enough data to build a statistic assessment Preparation of Safety Assessment One year trials campaign : mid 2014 mid 2015 Thales Air Systems 18/12/2012

28 28 / Next steps Take into account the regulation evolution Validate the WVDSS Optimise the performances Enlarge the scope

29 29 / UFO Executive Summary Studies of new Ultra Fast Lidar/Radar Wind & EDR monitoring sensors, usable for Wake-Vortex Hazards Mitigation severe Cross-Wind, Air Turbulence and Wind-Shear. High update rate and accuracy Sensors: 2D electronic scanning antenna based on low cost X-band tile New high power laser source of 1.5 micron Lidar 3D scanner New design tools developed through simulators, able to couple: Atmosphere models Electromagnetic, Radar and LIDAR models. Advanced Doppler signal processing algorithm developed and tested for 3D wind field and EDR monitoring, including sensors resources management Comparison with existing sensors: C band meteorological radar / S band ATC radar, ADS-B Downlink Calibration of the ground sensors and the simulators achieved through a set of experimental trials in Munich and Toulouse Thales Air Systems 18/12/2012

30 30 / UFO : Weather data downlink Airspeed-Vector GPS Airspeed-Vector Inertialnavigation Temperature, Humidity Radaraltimeter Airspeed-Vector Airspeed-Vector Measuring Accuracy Parameter Accuracy Remarks 5-Hole-Probe Fast and slow Thermometer Dewpointmirror 3 0 Airspeed 0,3 m/s Radar Altimeter 1 m Thales Thales Air Air Systems Systems 11/12/ /05/2013 Altitude 5 m based on ISA Temperature 0,2 C Humidity Dewpoint 0,25 C 2% rel. Hum. capacitive Sensor GPS 10 m / 0,1 m GPS Standalone / Carrierfreq. Groundspeed 0,2 m/s Heading 0,4 Attitude 0,2 Wind direction 2 Wind speed Vertical wind 0,3 m/s 0,15 m/s Thales Air Syste ms Pressuretransducer (Air Data System) Capacitive Lyman-Alpha Pressuretransducer (Humidity System) Humidity Sensor Hygrometer

31 31 / Next steps Take into account the regulation evolution Validate the WVDSS Optimise the performances Enlarge the scope

32 32 / Runway Occupancy Time Contributory Factors to ROT Increase & Runway Excursion Risk Runway contaminated by water, ice, snow or slush Wind velocity headwind/crosswind components exceptionally variable Poor and fluctuating forward visibility whether or not this status correctly advised in advance Solutions : Accurate/Timely ATC reporting of ambient /future conditions wind strength, direction and variation runway surface state (runway contaminant) 80 s (+ 50% versus B747) - 50% with BTV and Accurate/ Timely ATC reporting of runway conditions Super-Heavy Thales Air Systems 17/05/2013

33 33 / Runway Occupation Time: Brake-To-Vacate (BTV) Control braking to a predetermined point on a runway in preparation for exiting Lower and more consistent ROT factored into separation on final approach for AMAN BTV Thales Air Systems 17/05/2013

34 34 / Rainfall Retreival/Forecasting for Airport Real-time measurement by A-CALAMAR CALAMAR enables rainfall retrieval around airport based on following probes and sensors THALES High Resolution (5 m) / High Speed (45 s) X-band Electronic-scanning Solid-State Radar (Range: 30 km) Rain gauges data deployed around the airport Water level probes installed on runways and potential additional sensors THALES Advanced Doppler Weather Channel of PSR STAR 2000 S-band Radar (Range: 150 km) for early warning THALES FODetect Dual technology (77GHz radar and optic sensor) with Full runway visual coverage Probes/Sensors ingested Real-time forecasting Cumulative Forecast Rainfall based on data in 1 min increments for periods up to 30 mn /1 hour ahead Runway contamination forecast for periods up to 30 mn /1 hour ahead Thales Air Systems 17/05/2013

35 35 / WVDSS: Potential Gain with AMAN (with WV constraints) Non coordinated arrivals could grealty truble Airport Capacity Bad Sequence : A380 (7 NM) - A320 (3 NM) - A380 (7 NM) - A320 = 17 NM Good Sequence : A380 (3 NM) - A380 (7 NM) - A320 (3 NM) - A320 = 13 NM WVDSS/BTV + AMAN (with Wake- Vortex/ROT contraints) WVDSS will help controller to optimise safety aircraft separations RECAT II: Pairwise Separations RECAT III: Dynamic Wheather Dependent Separations AMAN should take into account Wake- Vortex separations constraints (RECAT I/II/III, weather), ROT (according to runway contamination and Braking system performances) to optimize Airport throughput Thales Air Systems 17/05/2013

36 36 / Point-Merge Point Merge is designed to replace radar vectoring, and to enable: extensive use of lateral guidance by the FMS continuous descent, even under high traffic load. Point Merge is based on a specific P-RNAV route structure that is made of a point (the merge point) and pre-defined legs (the sequencing legs) equidistant from this point (and vertically separated). Sequencing is achieved with a direct-to instruction to the merge point at the appropriate time. The legs are only used to delay aircraft when necessary. Thales Air Systems 09th of October 2012

37 37 / Deployment of Point Merge Procedure at Paris CDG by DSNA Thales Air Systems 09th of October 2012

38 38 / Conclusion We are on the right track Architecture confirmed Real time demonstrated Environment evolution taken into account We will offer an actual increase in airport capacity if we think to a global solution Thales Air Systems 18/12/2012

39 39 / Questions?