Real-time Wind Uplinks for Predication of the Arrival Time and Optimization of the Descent Profile

Transcription

1 Real-time Wind Uplinks for Predication of the Arrival Time and Optimization of the Descent Profile Nancy Bienert, Prof. Dr.-Ing. habil. Hartmut Fricke February 21 th, 2013

2 Outline 1 Status quo 2 Concept Realtime Wind Uplinks & Testing 3 Theoretical Analysis of gained Wind Data 4 Results from Data Evaluation & Testing 5 Conclusion & Outlook

3 1 Status quo Wind information at short and middle range flights FPL system Lido/Flight - 1,5 h 0 h Up to 5 h February 21, 2013 slide 3 of 15

4 1 Status quo Wind information on short and middle range flights WAFC forecast (UTC +6, +12, +18, +24, +30, +36 h) every 6 hours FPL system Lido/Flight - 1,5 h 0 h bis zu 5 h February 21, 2013 slide 4 of 15

5 2 Concept & Testing Ground Station Aircraft Meteorological Data Relay (AMDAR) February 21, 2013 slide 5 of 15

6 2 Concept & Testing Wind Uplink (PWI Format) Ground Station Predicted Wind Information (PWI) automated wind uplink for long range flights February 21, 2013 slide 6 of 15

7 2 Concept & Testing Conditions airport Frankfurt/Main (Germany) aircraft type A320 fleet (short and mid range flights) 6 test pilots Testing working schedule of test pilots similar arrival direction for prior a/c wind fields, jet streams briefing, test protocol, debriefing output different wind forcast data for this arrival route February 21, 2013 slide 7 of 15

8 3 Theoretical Analysis 1) No winds 2) OFP forecast 3) Predicated Wind Info 4) Real-time wind data 5) Real wind data s c e n a r i o s Enhanced Jet Performance Model (EJPM) Flight planning system Lido/Flight February 21, 2013 slide 8 of 15

9 3 Theoretical Analysis February 21, 2013 slide 9 of 15

10 4 Results a) EJPM calculation (example MXP-FRA) high precision fuel calculation and time prediction based on measured flight data (database OFP and measured a/c-positions) scenario time [min] OPF forecast 50:47 PWI uplink 51:37 Real-time wind uplink 52:37 Real wind data 51:51 Increase of speed to reduce delay costs!!! February 21, 2013 slide 10 of 15

11 4 Results b) Lido/Flight (example) displacement ToD around 7 NM increased fuel consumption 6,1kg 19kg CO 2 displacement ToD around 3 NM increased fuel consumption 2,6kg 8kg CO 2 profile with real wind data real head wind uplink wind data scenario OFP scenario PWI scenario real-time wind uplink February 21, 2013 slide 11 of 15

12 4 Results b) Lido/Flight calculation test trail of 10 flights OFP forecast: 79,9kg jet fuel PWI: 93,2kg jet fuel real-time wind data: 36,1kg jet fuel CO 2 CO 2 CO 2 252kg 294kg 114kg February 21, 2013 slide 12 of 15

13 4 Results ) Testing high failure rate 10 out of 67 data sets were 100% complete 6% 4% 10% 48% 16% 16% technical failures human failures DWD others ATC no prior aircraft Introduce an automation with smart algorithm and request real-time wind data from more (prior) aircraft! February 21, 2013 slide 13 of 15

14 5 Conclusion Enhanced Jet Performance Model (TU Dresden) ETA could be predicted more accurate adjustments of Cost Index possible to avoid delay costs in case of more tail wind or saving fuel in case of more tail wind flight planning system Lido/Flight (Lufthansa Systems) displacement of T/D results in significant fuel savings for both wind cases test findings from flight trails could be used for developing automation Direct achievements of this study: technical issues got solved enabling of PWI requests/uplinks for A320-fleet of Lufthansa February 21, 2013 slide 14 of 15

15 5 Outlook 2025? greenishweb.blogspot.com Today February 21, 2013 slide 15 of 15

16 End