Method for the Impr mprove veme ment of Aircraft Take ke-off Trajectory Simu mulation using Variability Analysis Speaker: George Koudis ICRAT 2014 28 th May 2014 1/24
Speaker background • George Koudis • 2 nd year PhD student at Imperial College London • Member of the Centre for Transport Studies • Supervised by Dr. R. North, Dr. A. Majumdar, Dr. W. Schuster and Prof. W. Ochieng • Researcher in the environmental impact of airport operations • Working on the Sensor Network for Air Quality (SNAQ) at LHR project with Prof. J. Polak, Dr. R. North and Dr. S. Hu • Sponsored by the Lloyds Register Foundation 2/24
Sensor Network for Air Quality (SNAQ): Heathrow Project • Consortium of 4 UK based universities and industry partners. • Main project aim is to validate the use of sensor network for monitoring local air quality (LAQ). • Installed a network of air quality sensor nodes around London Heathrow, which is being used as a case study. 3/24
Presentation outline 1. Introduction • Identification of research area • Outline aims and objectives 2. Review of state-of-the-art • Current modelling approaches • Factors affecting aircraft operations 3. Proposed methodology 4. Analysis • Identify and quantify relationships between operations and factors 5. Application of analysis • Modification of basic trajectory models informed by research 6. Summary and conclusions • Potential modelling improvements and implications 4/24
Introduction 5/24
Introduction 5/24
Introduction • Negative impacts of aircraft activity on local air quality (LAQ) around airports increasingly constrains planning for airport growth. • Aircraft movements in the landing and take-off (LTO) cycle are among the most polluting. • Aircraft emit substantial levels of NO X , CO, HC and PM 10 (among others) and therefore require regulation and management. • To facilitate a reduction in impact on LAQ, emissions arising from aircraft activity must be accurately and realistically represented. 5/24
Research issue to be addressed • Aircraft emissions can have a considerable impact on LAQ. • Aircraft LTO activities and consequent emissions require modelling at the operational level when analysing operational impacts. • Current techniques operate at different levels of sophistication but do not capture variability in activity. • Variability may cause significant differences in the LTO trajectory and consequent emissions. • There is currently no method for modelling aircraft activity variability with low computational expense and limited data availability. 6/24
Aims and objectives • Can recorded, high-resolution data be used to enhance simple LTO trajectory models and reflect actual aircraft operations? 1. Do the currently available emissions models of aircraft LTO activity sufficiently represent actual activity? 2. Can the relationships between aircraft activity and the factors that influence aircraft operations be identified and quantified? 3. Can knowledge regarding these relationships be used to modify basic LTO trajectory models where limited data is available? • This will result in an increased level of realism reflected by models without high levels of data required and high computational expense. 7/24
ICAO modelling approaches Approach Simple Advanced Sophisticated Aircraft and Aircraft group Aircraft type Aircraft specific engine fit Operational Standard LTO time LTO time in mode, LTO time in mode, thrust profile in mode and thrust thrust per aircraft type per specific aircraft Emission indices ICAO per group ICAO per movement ICAO per movement Spatial distribution Airport aggregate Airport aggregate Aircraft movement 8/24
General modelling approach Simple'approach' General'procedure' Sophis*cated'approach' Airport'opera*ons' Time,'aircra1,'tail'number,''stand' ID,'runway'ID,'origin,'des*na*on' Recorded' Reference'cycle'and' Simulated'e.g.' aircra1'data' HIPERMTP' spa*al'elements' (QAR)' Trajectories ' Fuel'flow,'x,'y,'z,'t ' ICAO'EEDB' Engine' Engine' ICAO'EEDB' Emissions'func*ons' BFFM2' assignment' assignment' modeMspecific' Thrust,'emission'inventory' Interpola*on' database' database' EI' Emissions'inventory' Emissions'database' Aggrega*on' on'spa*al'elements'with' spa*al'and'temporally' Spa*al','temporal'aggrega*on' temporal'data' distributed' 9/24
Proposed modelling approach Airport'opera*ons' Time,'aircra1,'tail'number,''stand' ID,'runway'ID,'origin,'des*na*on' Recorded' Simulated'e.g.' aircra1'data' HIPERLTP' (QAR)' Trajectories ' Fuel'flow,'x,'y,'z,'t ' ±'Variability ' =f(Aircra1'type,'TOW,' Meteorology,'etc.)' ' Emissions'func*ons' Thrust,'emission'inventory' ICAO'EEDB' Engine' BFFM2' assignment' Interpola*on' database' Aggrega*on' Spa*al','temporal'aggrega*on' 10/24
Factors with impact on aircraft trajectory • There are many factors that introduce variability in the LTO trajectory: including operational, meteorological and human. • Improving the level of understanding of the impact of these factors on trajectory variability relies on high-resolution data availability. • This can be either actual recorded or simulated data. • Is it possible to validate generic data for use with multiple airports? Operational Meteorological Aircraft factors Human • Wind direction • Airline operations Pilot preferences • • Wind speed • Airport procedures ATC decision • • Temperature • Ageing choices • Humidity • Maintenance • 11/24
Factors with impact on aircraft trajectory • There are many factors that introduce variability in the LTO trajectory: including operational, meteorological and human. • Improving the level of understanding of the impact of these factors on trajectory variability relies on high-resolution data availability. • This can be either actual recorded or simulated data. • Is it possible to validate generic data for use with multiple airports? Operational Meteorological Aircraft factors Human • Wind direction • Airline operations Pilot preferences • • Wind speed • Airport procedures ATC decision • • Temperature • Ageing choices • Humidity • Maintenance • 12/24
Data availability • Four datasets used: • BOSS – documents all activity and provides general information (8,331 flights) • QAR – high-resolution (1-4Hz) aircraft data record containing detailed activity data (17.9 mil. aircraft data rows) • ICAO EEDB – Fuel flow and emission indices data for specific engine types • BUCHAir – Third party engine assignment information 13/24
Data availability (cont.) BOSS data QAR data Group Item Group Item Units Temporal Actual date/time Temporal Date dd/mm/yy Scheduled date/time Time hh/mm/ss Stand on/off time Time from engine start s Spatial Type of activity (arrival or departure) Ground speed kts Spatial Latitude o , ‘, “ Origin or destination Longitude o , ‘, “ Stand number Pressure altitude ft Runway number Radio altitude ft Aircraft Airline operator Information Ambient Outside air temperature o C Aircraft (type specific) Total pressure hPa Engine type Engine Fuel flow* kg/s Aircraft tail number information Engine pressure ratio* - Max. number of passengers Turbine gas temperature* - Max. take-off weight (MTOW) Thrust* % (of max) Aircraft Flight phase - information APU usage -
Data use BOSS$ QAR$ Trajectories $ Fuel$flow,$x,$y,$z,$t $ ±$Variability $ =f(AircraN$type,$TOW,$ Meteorology,$etc.)$ $ Emissions$func<ons$ Thrust,$emission$inventory$ ICAO$ BUCHAir$ EEDB$ Aggrega<on$ Spa<al$,$temporal$aggrega<on$ 14/24
Quality control • Sampling frequency • Erroneous values: • Latitudes and longitudes outside boundaries of managed airspace • Ground speed • Elevation Aircraft type Aircraft jet size Total records (pre-QC) Total records (post-QC) Percentage loss A319 Small 1412 1398 1.0 A320 Small 1145 1122 2.0 A321 Small 425 416 2.1 A767 N/A 287 0 100.0 B77A Medium 387 387 0.0 B772 Medium 67 67 0.0 B77W Medium 55 54 1.8 B747 Large 470 453 3.6 Total 4248 3898 8.2 N.B. Aircraft jet size is for analysis purposes 15/24
Take-off roll trajectory • Analysis of variability of the start, end and duration of take-off roll trajectory is conducted in this research. • The take-off roll is the phase of the LTO cycle which sees the highest rate of NO X emissions produced as thrust ranges between 80-100%. • Two cut-off points are defined for analysis purposes: Cut-off point Criteria used for the data cut Start of take-off roll Thrust = 14% (ICAO idle thrust x2) & speed >100kts 60s later Wheels off Elevation > 10ft (above datum) 16/24
Take-off roll activity: aircraft type variable Ground speed at wheels off /kts Ordered by MTOW 17/24
Take-off roll activity: aircraft type variable (cont.) 18/24
Variation in activity for nominally similar aircraft Ground speed at wheels off /kts • Blue boxplots refer to the ground speeds at wheels off of one A319 aircraft during the sample period (>40 events). • Green boxplot shows the ground speeds at wheels off for all A319 activity during the sample period. • Red boxplot shows the ground speeds at wheels off for all aircraft activity during the sample period. 19/24
Length of roll - ground speed at wheels off variation Length of take-off roll /m r = 0.853 Ground speed at wheels off /kts
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