Development of flight inefficiency metrics for environmental performance assessment of ATM

Tags: University of Cambridge, Institute for Aviation and the Environment, Institute for Aviation, Inefficiency, cruise altitude, cost index, altitude profile, Continuous Descent Approach, Standard cruise altitude, optimisation algorithms, FDR Total Fuel, Fuel Analysis, Radiative Forcing, Global Aviation, Nitrogen Oxide Emissions, Environmental Implications, Flight data, environmental impact
Content: Institute for Aviation and the Environment Development of Flight Inefficiency Metrics for environmental performance Assessment of ATM Tom G. Reynolds 8th USA/Europe Air Traffic Management Research and Development Seminar Napa, California, 29 June-2 July 2009
Motivation · ATM has important role in reducing environmental impacts of aviation: ATM affects ALL aircraft · Increasing efforts to quantify ATM's current impact IPCC
· Concept of "Flight Inefficiency" commonly used · This study designed to complement these activities
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Outline 1. Identify sources of Flight Inefficiency 2. Describe Flight Inefficiency metric options 3. Analyse data with key Flight Inefficiency metrics 4. Discuss insights enabled through this approach
3 © University of Cambridge, 2009
Institute for Aviation and the Environment
Outline 1. Identify sources of Flight Inefficiency 2. Describe Flight Inefficiency metric options 3. Analyse data with key Flight Inefficiency metrics 4. Discuss insights enabled through this approach
4 © University of Cambridge, 2009
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Flight Inefficiency Sources
· Constraints to aircraft flying
Adverse weather
their 4D optimal trajectory
StandaAradltnitdruodSuetpesese,dRs estricted
Departure fix
airspace
Expensive airspace Congested airspace
Arrival fix
En Route Airspace
Arrival
Holding
Departure
procedures
procedures
Landing
Take-off Taxi-out
Taxi-in Arrival Terminal Airspace
Departure Terminal Airspace 5 © University of Cambridge, 2009
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Sample Inefficiencies: Terminal Airspace Standard Procedures
50 nm terminal area
Departures Departure fix Arrivals Arrival fix
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Sample Inefficiencies: En Route Airspace
SEA SFO LAX 29 August 2005
DEN
ORD
BOS
DFW
ATL
Restricted
MCO
areas
Hurricane Katrina 7 © University of Cambridge, 2009
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· Holding absorbs delay & maximises runway capacity · Vectoring for spacing and sequencing on final approach
Sample Inefficiencies: Arrival Terminal Airspace
50 nm terminal area range ring
Heathrow
Holding stacks
Vectoring for final approach
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Outline 1. Identify sources of Flight Inefficiency 2. Describe Flight Inefficiency metric options 3. Analyse data with key Flight Inefficiency metrics 4. Discuss insights enabled through this approach
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Flight Inefficiency Metrics
· Concept commonly used as ATM
performance indicator
Flight inefficiency metric:
Quantify difference between "ideal" and "actual" performance
Actual- OptimalЧ 100%
· Focus has been on average route
Optimal
extension over great circle
Source:
(26.4 nm)
(A-D) (D-G) 10 © University of Cambridge, 2009
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Flight Inefficiency Metrics · Route extension is simple and compatible with current surveillance systems, but neglects effects in other flight dimensions · Fuel-based metrics (e.g. excess fuel burn) capture these effects and directly relate to emissions, but... · ...are more challenging due to need to determine optimum fuel burn and access to more aircraft info
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Metrics in Different Flight Dimensions
Dimension
Sample "actual"
Sample "optimal"
Advantages
Disadvantages
Lateral
Flown ground Great circle
distance
distance
Simple
No info from other flight dimensions
Vertical or Speed
Average
Optimal en
cruise altitude route altitude
or speed
or speed
Captures vertical or speed elements
More complex & No info from other flight dimensions
Fuel
Average block fuel
Optimal block fuel
Proportional to emissions
More complex
12 © University of Cambridge, 2009
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Outline 1. Identify sources of Flight Inefficiency 2. Describe Flight Inefficiency metric options 3. Analyse data with key Flight Inefficiency metrics 4. Discuss insights enabled through this approach
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European flight data Summary
· Flight data from European airline A320 family during early 2008 · Lateral & fuel-based analysis
50 nm terminal area
n=4420
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Lateral Analysis: Route Extension
Departure fix
DTurn DTO
DDepart
RTA
DEn_route_actual
En Route
DEn_route_GC
Departure TA
Sample lateral path Shortest lateral path
Arrival fix
Arrival
DHold TA
DArrival

DDownwind
DFinal DBase
GTEDepTA = (DTO + DTurn + DDepart) ­ RTA
GTEEn_route = DEn_route_actual ­ DEn_route_GC
GTEArrTA = (DArrival + DHold + DDownwind + DBase + DFinal) ­ RTA
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Fuel Analysis: Aircraft Performance Modelling · FDR data used to validate candidate aircraft performance models to determine optimum fuel burn
Fuel Burn (Relative to FDR Total Fuel)
Cruise
Taxi-in Landing Descent
Climb Take-off Taxi-out
Actual data (FDR) model data (Piano-X) Model data (BADA)
Track Distance (nm) · Piano-X can be better tailored to observed operations
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Fuel Analysis: Optimum Fuel Burn Challenges
· Function of many variables: Aircraft type Weight Route length Winds Centre of gravity Temperature Operator "cost index", i.e. ratio of time-related costs to fuel-related costs
Block Time (mins)
Block Fuel (kg)
6500 6000 5500 5000 4500 4000 45000 180 170 160 150 140 45000
Typical narrow-body mission
50000
Minimum time (high cost index) Long-Range Cruise Minimum fuel (low cost index)
55000
60000
65000
Minimum time (high cost index) Long-Range Cruise Minimum fuel (low cost index)
50000
55000
60000
Aircraft Weight (kg)
65000
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Lateral & Fuel Inefficiency Comparison Lateral analysis Average TGTE: 51 nm/13%
Arrival TA 26 nm (51%)
Dep. TA 9 nm (18%) En route 16 nm (31%)
n=1794, A320 European flights only. Average great circle distance 403 nm 18 © University of Cambridge, 2009
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Lateral & Fuel Inefficiency Comparison
Lateral analysis Average TGTE: 51 nm/13%
Fuel analysis Average excess fuel burn*: 645 kg/23%
Arrival TA 26 nm (51%)
Dep. TA 9 nm (18%) En route 16 nm (31%)
Arrival TA 161 kg (25%)
Departure TA 184 kg (29%)
En route 300 kg (46%)
n=1794, A320 European flights only. Average great circle distance 403 nm
*Relative to minimum theoretical fuel burn
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Lateral & Fuel Inefficiency Comparison
Lateral analysis Average TGTE: 51 nm/13%
Departure
Arrival procedures
procedures 9 nm
13 nm
(18%)
(25%)
Stnd rts
& res. airspc
Holding &
8 nm (16%)
vectoring 13 nm (25%)
Congestion & adverse wx 8 nm
(16%)
Fuel analysis
Average excess fuel burn*: 645 kg/23%
Taxi-in 10 kg (2%)
Arr trk ext
19 kg (3%) Taxi-out
20 kg
Holding & vectoring, sub-optimal arrival altitude profile
(3%) Departure track extension 150 kg (23%)
132 kg (20%)
Sub-opt dep alt/spd
profile 24 kg (3%)
Standard cruise altitude & speed 225 kg (35%)
Std rtes/rest airspace, adverse wx & congestion 75 kg (12%)
n=1794 (A320 European flights only), Average great circle distance 403 nm * Relative to minimum theoretical fuel burn
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Outline 1. Identify sources of Flight Inefficiency 2. Describe Flight Inefficiency metric options 3. Analyse data with key Flight Inefficiency metrics 4. Discuss insights enabled through this approach
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ATM Evolution Implications
· Helps prioritize ATM evolution concepts and technologies
ORIGIN AIRPORT
Departure/Climb
Cruise
Descent/Approach DESTINATION AIRPORT
Pushback
Take-Off TaxiOut
Landing Taxi-In
FUTURE OPERATIONAL CONCEPTS FOR IMPROVED LATERAL/FUEL- BASED ENVIRONMENTAL PERFORMANCE
· Optimised push-back time and sequence
· Single-engine optimal taxi routing with no holding
· Engine power optimisation
· Optimised lateral, vertical, speed profiles · Strategic de-confliction
· e.g. Continuous Climb Departures
· e.g. windoptimised ground track at optimal cruise altitude & speed
· e.g. full Continuous Descent Approach
· Displaced thresholds · Steeper glideslope angles · Runway allocation for optimal taxi routing
· Single-engine optimal taxi routing with no holding
ENABLING TECHNOLOGIES
· Push-back optimisation algorithms (G) · Datalink (G-A)
· Taxi optimisation algorithms (G) · Datalink (G-A)
· Take-off Power Management (A)
· 4D trajectory management algorithms (G,A) · Advanced Communication: Datalink (G-A) · Advanced Navigation: P-RNAV (A) · Advanced Surveillance: ADS-B (G,A)
· Runway allocation algorithms (G) · Datalink (G-A) · P-RNAV (A)
· Taxi optimisation algorithms (G) · Datalink (G-A)
OTHER ENABLERS
· Modified standard operating procedures · CDM/CEM
· Airspace re-design · Modified standard operating procedures · CDM/CEM
· Modified standard operating procedures · CDM/CEM
22 Key: (G) = Ground, (A) = Aircraft, (G-A) = Ground to Aircraft, ADS-B = Automatic Dependent Surveillance-BroaIndcsatsitt, uCDteM f=oCrolAlabvoiraattiivoen Decision Making, CEM = Collaborative Environ©meUntnMivaenrasgiteymoenf tC, Pa-mRNbAriVdg=eP,re2c0is0io9n Area Navigationa, nd the Environment
Environmental Implications · Findings affect aviation environmental impact predictions IPCC scaling factors of 1.15 on fuel burn, falling to 1.05 in 2015 to account for ATM inefficiencies and their improvement
Source: Sausen et al., 2005, "Aviation Radiative Forcing in 2000: An Update on IPCC", Meteorol. Z., 14 (4), pp. 555-561
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Environmental Implications
· Findings affect aviation environmental impact predictions Location of "non-CO2" emissions affects climate response
Methane
Ozone
· Radiative forcings due to changes in ozone, methane & methaneinduced ozone as a function of the altitude of a 5% NOx perturbation
Methaneinduced ozone
Net forcing
· Much more work needs to be done: Climate scientists and ATM researchers must collaborate
Source: Kцhler et al., 2008, "Impact of Perturbations to Nitrogen Oxide Emissions from Global Aviation", J. Geophysical Research, Vol. 113, D11305
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Conclusions · ATM has an important part to play in aviation environmental impact mitigation
· Flight inefficiency metrics effective at quantifying ATM performance Importance of considering terminal area operations Differences between track extension & excess fuel burn Note: not all inefficiencies should be attributed to ATM
· Analysis provides important insights into: ATM evolution priorities Environmental Impact Assessment
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Acknowledgements · Funding bodies:
· Forthcoming publications at ATIO: Effects of airspace charging on airline route selection More detailed assessment of fuel inefficiency analysis
· Further information: Tom G. Reynolds: [email protected] www.AIMproject.aero 26 © University of Cambridge, 2009
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File: development-of-flight-inefficiency-metrics-for-environmental.pdf
Title: Development of Flight Inefficiency Metrics
Author: Tom G. Reynolds
Published: Mon Jun 29 15:04:20 2009
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