-
1
Abstract
This paper describes the development of anIntelligent Ice
Protection System (IIPS) forintegration into an aircraft
trajectoryoptimisation framework developed as part of theClean Sky
programme. The IIPS is developed inMATLAB incorporating features of
moreelectric systems and future air navigationenvironment. A
typical flight from LondonAirport Heathrow (EGLL/LHR) airport
toAmsterdam Airport Schiphol (EHAM/AMS) wasused as a case study.
Initial results show thatfurther savings on fuel burn and flight
timecould be achieved if icing phenomenon isconsidered in aircraft
trajectory optimizationscheme.
Nomenclature
݉ : Aircraft massܸ : Aerodynamic speedܶ : Thrust magnitudeℎ :
Altitudeܮ : Lift magnitudeܦ : Drag magnitude݃ : Gravity
accelerationߛ : Flight path angle߯ : Heading angleܿ : Specific fuel
consumptionφ : Geodetic latitudeߣ : Geodetic longitudeܴா : Earth
radiusߤ : Bank or roll angleܲܪ : High Pressureܲܫ : Intermediate
Pressureܲܫܫ ܵ : Intelligent Ice Protection Systemܦ&ܴ : Research
and Development
1 Introduction
The growing trend of air travel has madeaviation the fastest
growing source of globalwarming and climate change [1]. Based on
thecurrent annual projection of about 5%, theannual passenger total
is expected to increasefrom 3.1 billion in 2013 to 6.4 billion by
2030[2]. CO2 emission is by far the largest amongpollutants from
air transport. In Europe alone, itis estimated that more than
300,000 tonnes ofCO2 is generated from aircraft operations perday
[3]. As a result, EU initiated three streamcomprehensive
projects/measures to mitigatethe impacts of aviation on the
environment andfuel resources. These are R&D for
greenertechnology, modernised air traffic managementsystems and
market based measures.
The Clean Sky Joint Technology Initiative (JTI)is the flagship
of the R&D projects for thegreening of air transport in EU. The
Clean Skyis a private/public research partnership atEuropean level
in the field of aviation todevelop the technologies necessary for a
clean,innovative and competitive system of airtransport. This would
be done through theachievement of ACARE targets of reducing
theemissions of CO2, NOx and unburnthydrocarbons by 50%, 80% and
50%respectively by 2020 referenced to 2000standard [4]. One of the
Clean Sky activities isthe Management of Trajectory and
Mission(MTM) work package which is under Systems
AN INTELLIGENT ICE PROTECTION SYSTEM FORNEXT GENERATION AIRCRAFT
TRAJECTORY
OPTIMISATION
Ahmed Shinkafi, Craig Lawson, Ravinka Seresinhe, Daniele Quaglia
andIrfan Madani
Cranfield University, Aerospace Department, MK43 0AL,
[email protected]
Keywords: Aircraft ice protection, next-generation, trajectory
optimisation
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A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I
MADANI
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for Green Operations (SGO) - IntegratedTechnology Demonstrators
(ITD).
This research was carried out under the SGOITD and proposes a
controllable ice protectionsystem for aircraft fitted with future
airnavigation systems. The research aimed atinvestigating ways to
operate aircraft at lowpower levels through changes in
aircraftoperation strategy that takes into accountoptimised flight
routings due to icingconditions. Conventional approaches
totrajectory optimisation do not take the airframesystems penalty
into account in contrast to realaircraft operation. This research
has developed atool capable of sizing and simulating
aircraftanti-icing performance for trajectoryoptimisation which
would enable developmentof decision making processes dependent
onweather within the flight management system;thus transforming the
conventional IceProtection System (IPS) to a more
intelligentsystem.
1.1 Objectives of the ResearchWhile continuing to have safety as
a primaryobjective, this work was about utilising futureaircraft's
Performance-Based Navigation (PBN)concept to investigate possible
ways ofminimising IPS operational demand power thatwould lead to
greater efficiency and capacity.The primary objectives of this
work, therefore,are to:
develop a consistent and cohesivestrategy of managing in-flight
icing in afuture ATM environment that enablesefficient flight
planning,
investigate the response of today'scutting edge aircraft icing
technologiesin the future 4D navigation environment,
use optimised aircraft trajectories toinvestigates ways to
operate aircraft atlow power levels in icing conditions and
Conceptualise controllable ice protectionsystem for intelligent
aircraft operation.
The secondary objectives are to build IPS modelfor the total
system burden, and algorithm forintelligent operation so that
potential savingsassociated with routing and other operational
issues may be explored. This would includeassessment of the
cost, benefit and riskassociated with using routing for aircraft
iceprotection.
1.2 Performance Based Navigation SystemGround-based systems have
served the aviationcommunity well since inception; however asdemand
for air transportation services increases,they do not permit the
flexibility of point-to-point operations required for the future
ATMenvironment [5]. Hence, ICAO has adopted thePerformance Based
Navigation (PBN) system toaddress these challenges. PBN
definesperformance requirements for aircraftnavigating on an Air
Traffic Service (ATS)route, terminal procedure or in a
designatedairspace. Through the application of AreaNavigation
(RNAV) and Required NavigationPerformance (RNP) specifications,
PBNprovides the means for flexible routes andterminal procedures
[1]. The International AirTransport Association (IATA) estimated
thatshorter PBN routes globally could cut CO2emissions by 13
million tonnes per year [6].Flying PBN routes eliminates 3.19 kg of
CO2emissions for every kg of fuel savings [6]. Oneof the major
advantages of PBN system is that itpermits optimal trajectory based
operations byproviding very precise lateral and vertical
flightpaths. From 2030 and beyond, aircraft areexpected to fly
optimal trajectories that aredefined in the form of three
dimensionalwaypoints plus associated required times (4D)of overfly
[3].
1.3 Concept of Next generation AircraftThe next generation
aircraft is considered to beextremely efficient by design, light
weight(composites structures) with lower maintenanceand overall
operating costs, and reduced impacton the environment. There is a
lot of progresstowards the development of new and moreefficient
de-icing systems that are compatiblewith next generation composite
airframestructures. This includes the heater mattechnology, smart
IPS and icephobic coatingsamong others. Projects such as Clean
Sky,NextGen/SESAR, ON-WINGS, ERAST,
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An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
Boeing CFD and EADS VoltAir are all projectspursued towards more
efficient aircraft systemsenergy management and cleaner
environment.
The next generation aircraft is conceptualisedutilising the
All-Electric Aircraft (AEA)technology. Though the transition to
AEAconfiguration is still far away, there is a greatachievement in
a More Electric Aircraft (MEA)technology. In the MEA configuration,
it isassumed that the majority of the airframesystems will be
powered electrically retainingpresent form of hydraulic, mechanical
orpneumatic power [7] whereas, the AEA conceptassumes that the all
aircraft systems will bepowered electrically.
1.4 Recent Progress in Next Generation IPSThe classical
pneumatic anti-icing systemtypically has a very simple on/off
controlimplementation. It taps bleed air power from theengine which
reduces the overall engineperformance. This causes high fuel
consumptionand, CO2 and NOx emissions which affects airtransport
costs and the environment impactalong the route. On the other hand,
electricalanti/de-icing power could be provided bygenerators
on-board. Goodrich Corp testedelectro-thermal technology on a
Cessna 303Twing leading edge during the 2003/4 winter, andreported
that between 20-50% energy was savedcompared to conventional
anti-icing system [8].Boeing 787 is the first large fixed wing
aircraftto use electro-thermal de-icing system onaircraft wings.
The Boeing 787 powerconsumption was reduced to 45-75 kW usingthis
technology compared to 150-200 kWneeded with classical technology
[9]. Anotherrecent technology is the pulse electro-thermalde-icing
system developed by Prof. VictorPetrenko [10]. According to
Petrenko [11],when a pulse de-icer is fully optimised, only 1%of
the energy requirement of a conventionalthermal de-icer would be
demanded.
Conventional methods of protecting aircraftagainst inflight
icing involve using Active IceProtection Systems (AIPS). However,
AIPS arecharacterised by complexity and high fuel
consumption. Hence, there are on-goingresearch efforts aimed at
developing Passive IceProtection Systems (PIPS) that are easy
andrequire less energy. One prominent feature ofPIPS is the use of
icephobic coatings on aircraftparts prone to inflight icing to
reduce iceadherence to the surfaces.
Previous experiments have shown thaticephobic coatings have the
potential tosignificantly reduce power consumption [12].However,
these coatings must have certainchemical and physical properties to
withstandaircraft harsh operating environment. Thus,
theirdurability and expected service life have not yetbeen
established so also their overall cost ascompared to AIPS. Erosion,
corrosion andreaction with atmospheric substances vis-à-visits
effects to the environment are a greatchallenge to understand at
the moment.Response to lightening, electrostatic
properties,interference with electromagnetic signals andavionic
components are all issues that are notyet resolved [13].
2 Research Approach and Method
There are basically three methods of managingaircraft in-flight
icing. These are throughdevelopment of efficient anti/de-icing
systems,effective weather avoidance strategy, and or icetolerant
aircraft designs. In the past, the effortsto reduce air transport
cost and its negativeimpacts on the environment were mainlyfocused
on aircraft and engine designs. Whileachieving safety, fuel
consumption vis-à-viscosts could be minimised through
climatecompatible operations by better exploitingfuture real time
weather information.
Today, aircraft may deviate from its flight planonly for reasons
of safety and weatherperturbations. However, due the growingimpacts
of commercial aviation on theenvironment and oil resources, smart
routingsare required to operate aircraft at low powerlevels to save
fuel consumption. Recentadvances in intelligent ATM, digital
control andcomputer based automation have provided
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A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I
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4
opportunities to investigate use of optimal flightroutes around
disturbances which have thepotential to decrease safety and
increase theenvironmental impact on aviation.
This approach, which is applicable to allairframe systems, is
applied to the aircraft icingproblem in this work. In real sense,
to achieveautonomous operation in icing conditions, acontrollable
intelligent ice protection systemwhich enables the development of a
decisionmaking process dependent on weather must beintegrated into
the on-board FMS; thustransforming the conventional IPS into a
moreintelligent system. The idea is to combine theeffect of
technology and smart routing inaircraft anti-icing problem as
illustrated inError! Reference source not found. Smartrouting is an
avoidance strategy involvingweather-dependent optimized
trajectories forefficient and low power levels operations.
Fig. 1 A Systematic Approach to ControllableAircraft Anti-icing
in Future ATM Systems
In this approach, the IIPS takes only that muchpower required
based on the icing inputs fromthe sensors or remote weather source.
Thisapproach is an improvement on theconventional approach of
representing only theaircraft dynamics and engines
system;neglecting aircraft systems impacts.
3 Trajectory Optimisation Method
3.1 GATAC Simulation FrameworkGreener Aircraft Trajectories
under ATMConstraints (GATAC) is a multi-objectiveoptimization
framework for planningenvironmentally efficient trajectories.
Thesoftware is co-developed between University ofMalta and
Cranfield University. The Universityof Malta developed the
infrastructure codewhile Cranfield University developed
theoptimizer and models.
Fig. 2 GATAC Integration Framework Architecture[14]
The current GATAC v3 release allows the userto use four
different optimisers to solvetrajectory optimization problems. The
overalloptimisation block diagram of a genericproblem using GATAC
is shown in Fig. 2.Further reading on GATAC can be obtained
in[15].
3.2 Optimiser ChoiceThe following optimisers are available
inGATAC v3:
Non-dominated Sorting GeneticAlgorithm Multi-Objective
(NSGAMO)
Multi-Objective Tabu Search (MOTS) Hybrid Optimiser (HYOP)
The NSGAMO optimiser is capable ofperforming multi-objective
optimisation underconstraints and is based on Genetic
Algorithm(GA). The properties of GA algorithms tooptimise problems
with local minimumsperfectly fit to the needs of this work. A
bi-objective optimisation scheme which results in acreation of
Pareto front was used to optimise thefuel and time. The theoretical
optimal Paretofront solutions are ଵݔ ∈ ݔ,[0,1] = 0 where
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An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
i=2,…,n. The development processes andcapabilities of these
models and the GATACsimulation framework are discussed below.
4 Models
To enable the simulation of aircraft operation inicing
condition, an aircraft dynamics model andmajor airframe systems
models were developedand integrated in the GATAC framework
asillustrated in Fig. 3. The framework wasdeveloped and validated
by using Cranfieldmodels.
Fig. 3 Models Interface Architecture
The model suite generates a representation ofairline operating
costs in order to be able toprovide a realistic indication of the
overall costsassociated with the flight time, fuel burn, noiseand
emissions of the flight mission beingoptimized.
4.1 Aircraft ModelA representative model similar to Airbus
A320based on EUROCONTROL BADA datasetparameters was used to model
aircraft motion.The A320 aircraft was chosen because it is oneof
the Clean Sky baseline aircraft for technologydemonstration. In
addition, the A320 aircraftoffers advanced navigation technology
such asRequired Navigation Performance (RNP)capability and Future
Air Navigation System(FANS) which are part of the requirements
forthe use of this methodology. The RNP reducesapproach distances
for landing while reducingfuel consumption and CO2 emissions,
whileFANS optimizes flight path and reduces aircraftspacing. Airbus
A320 is an aircraft of the futureand therefore suitable for this
research.
4.2 Ice Protection System PerformanceModellingThe essence of
modelling the IPS operation wasto estimate the system power
requirement undervarious icing conditions. The IPS is modelledbased
on the Messinger [16] method utilizesconvection, sensible
heating,evaporation/sublimation, kinetic energy andviscosity terms
in the conservation energyequation to find the equilibrium
temperature ofan unheated icing surface. Based on thismethod, the
anti/de-icing energy estimated isequal to the energy resulting from
the heatbalance which includes sensible heating,(ሶ௦௦ݍ) convective
cooling (ሶ௩ݍ) andevaporative cooling .(ሶ௩ݍ) However, heatgains due
to kinetic energy of the impingingdroplets and air must be
accounted by thekinetic heating (ሶݍ) and aerodynamic heating(ሶݍ)
terms, respectively. The anti-icingenergy equation is thus given
as:
=ሶ௧ݍ ሶ௦௦ݍ + ሶ௩ݍ + ሶ௩ݍ − −ሶݍ
ሶݍ (1)ሶ௦௦ݍ = ݉ሶ.ܥೌ( ௦ܶ − ஶܶ ) (2)
ሶ௩ݍ = 0.7ℎܮቂோೞೠିಮ
ಮቃ (3)
ሶݍ = ݉ሶ௩ಮమ
ଶ(4)
ݍ = ܴℎ௩ಮమ
ଶುೌ൨ (5)
Where the local heat transfer coefficient, ℎ iscalculated from
the following empirical relation:
ℎ = .ݑܰబ
௫(6)
The Nusselt (Nu), Prandtl (Pr) and Reynolds(Re) numbers are
dimensionless quantities thatare calculated from the following
relationships:
ݑܰ = 0.0296.ܴ ௫݁.଼.ܲݎ.ସ (7)
=ݎܲ .ఓ
బ(8)
ܴ݁=ఘಾ ೄಽ..
ఓ(9)
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A.
The A320 anti-ice system uses both hot air andelectrical heating
to protect the critical areas ofthe aircraft. During flight, hot
air from theengine IP and HP bleed ports is used to heat theengine
nacelle and, slats 3, 4 and 5energy is used instead for de-icing
windscreen,probes and waste water drain mast.the parameters used
are shown in Table 2model however, is reconfigurablemedium to large
fixed wing aircraftanti/de-icing modelling processes were coveredin
[17]. The electrical power requirement isgiven in shaft kW and the
bleed requirement isgiven in bleed kg/s; the two of which
areconnected to the engine offtake model. Thedifference between
this model and the baselineaicraft AI is in the operation. In this
model, theIPS algorithm penalises the engine based onicing inputs
from sensors/weather data andaircraft mission parameters such as
air speedand altitude. In this way, the system demandsonly the
amount of power required for thecurrent operation in contrast to
the baseline AIsystem whereby a fixed value of kg/s and kWare
provided for ice protection.
Table 1. Modelling and Simulation Parameters
InputsParameter ValuesAltitude (h) User defineAmbient
temperature ( ஶܶ ) User defineSurface heat transfer area (S0) User
defineFlight speed (VTAS) User defineClouds liquid water content
(LWC) User define
Internal constantsMean aerodynamic chord (LMAC) 2.2Slat length
(ySLAT) 3.14Leading edge sweep (߮ா) 27.5Skin temperature ( ௦ܶ) 5MVD
(dmed) 20Pressure (P) f(h)Saturation pressure (e) f(T)Relative
humidity (Rh) 100Specific heat of air (ೌܥ) 1005
Specific heat of water ೢܥ) ೌ) @ 0°C 1859
Specific density of water (ρwater) 1000Latent heat for water
evaporation (Le) 2257Latent heat of fusion (Lf) of ice 332.5Air
density (ρ) f(h)Absolute viscosity of air (µ) 1.5636x10Thermal
conductivity of air (k0) 0.0228
OutputsHeat flux (௧̇ݍ) ResultBleed mass flow rate (݉̇ ௗ)
Result
A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I M
6
ice system uses both hot air andelectrical heating to protect
the critical areas of
During flight, hot air from theand HP bleed ports is used to
heat the
engine nacelle and, slats 3, 4 and 5. Electricalicing
windscreen,
probes and waste water drain mast. Summary ofshown in Table 2.
The
rable for anymedium to large fixed wing aircraft. The
ling processes were coveredThe electrical power requirement
is
given in shaft kW and the bleed requirement isthe two of which
are
connected to the engine offtake model. Thedifference between
this model and the baselineaicraft AI is in the operation. In this
model, the
penalises the engine based onicing inputs from sensors/weather
data and
ft mission parameters such as air speedand altitude. In this
way, the system demandsonly the amount of power required for
thecurrent operation in contrast to the baseline AIsystem whereby a
fixed value of kg/s and kW
Modelling and Simulation Parameters
Values UnitsUser define ftUser define °CUser define m2
User define ktUser define g/m3
mm°°CµmhPahPa%J/kg.K
J/kg.K
Kg/m3
kJ/kgkJ/kgkg/m3
1.5636x10-5 kg/s.mW/m.K
kWkg/s
4.2.1 IPS Model ValidationThe model performance was evaluated
based onan icing experimental test conducted by AlKhalil et al.
[18] on the engine intake of aturbine aircraft. The same test
caseKhalil experiment was run with the modeldeveloped in this work
and the results comparedvery well with the experimental result as
shownin Fig. 4.
Fig. 4 IPS Model Validation with Experimental Data
The percentage deviation of the model resultfrom the
experimental resultpower plotted in Fig. 5discrepancies in all the
six cases.
Fig. 5 Percentage Deviation from Experimental Data
Although, there is value in investigating morecases, with the
satisfactoryand the fact that the range here covers theAppendix C
temperature range (0 tothe most severe CM icing condition makes
theresult of the model valid.
4.3 Aircraft Dynamics ModelThe Aircraft Dynamics Model (ADM)
isintegrated model in GATAC which is
R. SERESINHE, D. QUAGLIA AND I MADANI
alidationhe model performance was evaluated based on
an icing experimental test conducted by Al-on the engine intake
of a
The same test case as the Al-was run with the model
developed in this work and the results comparedvery well with
the experimental result as shown
IPS Model Validation with Experimental Data
percentage deviation of the model resultfrom the experimental
result in terms of total
5 shows less than 20%in all the six cases.
Percentage Deviation from Experimental Data
Although, there is value in investigating morecases, with the
satisfactory sensitivity resultsand the fact that the range here
covers theAppendix C temperature range (0 to -30°C) forthe most
severe CM icing condition makes theresult of the model valid.
Dynamics ModelThe Aircraft Dynamics Model (ADM) is anintegrated
model in GATAC which is in charge
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An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
of the aircraft trajectory generation of a genericaircraft
between two pre-defined positions in a3D space [19]. The generic
aircraft is modelledusing the rigid body idealisation with
varyingmass under aerodynamic, propulsive andgravitational forces
with assumption ofsymmetrical aircraft with thrust force parallel
tothe motion. In addition the assumptions ofspherical, non-rotating
Earth and no windatmosphere are also introduced to simply
theproblem. The aircraft motion is described byusing point mass
with three degrees of freedomand the resulting differential
algebraic equationsare listed below:
݉ௗ
ௗ௧= ܶ− ܦ − ݉݃ sin(ߛ) (10)
ܸ݉ (ߛ)ݏܿௗஎ
ௗ௧= ݅ݏܮ (ߤ݊) (11)
ܸ݉ௗఊ
ௗ௧= −(ߤ)cosܮ ݉݃ cos (ߛ) (12)
ௗ
ௗ௧= −ܿܶ (13)
(ܴா + ℎ)ௗఝ
ௗ௧= ܸ (ߛ)ݏܿ cos(χ) (14)
(ܴா + ℎ)ௗఒ
ௗ௧= ܸ sin(ߛ)ݏܿ ( )߯ (15)
ௗ
ௗ௧= ݅ݏܸ (ߛ݊) (16)
The aerodynamic forces are modelled by dragpolar characteristic
provided by BADA dataset[20] and the gravitational forces are
modelledusing the International Standard Atmosphere(ISA) datum
value (9.81 ms-2). The ADMgenerated 3D trajectories based on
variablesprovided by the optimiser regarding waypointpositions and
altitude and airspeed informationalong the trajectory. Several
input parameterssuch as initial and final positions and speed
andaircraft initial mass are required to support theoptimal
variable to generate the trajectory andevaluate the overall fuel
consumption and flighttime, and emission indexes. The
optimisationprocess will evaluate many possible trajectoriesby
varying the trajectory variables previouslyintroduced and refine
the search by minimizingthe imposed objectives.
4.4 Engine ModelThe engine model is based on performance
dataobtained by Cranfield’s in-house developed gasturbine
performance code called Turbomatch.
The Turbomatch model is similar to the CFMInternational
CFM56-5B4 turbofan engine. Themodel was validated using EASA [21]
andICAO [22] data. The engine was simulated for avast envelope of
off-design conditions and aperformance database was created
inMatlab/Simulink. Fig. 6 shows the performancedata obtained from
the engine model.
Fig. 6 SFC vs net thrust at SL, ISA
Fig. 7 shows the Simulink model of the engine.
Fig. 7 Turbofan engine Simulink model – Inputs &Outputs
4.5 Off-take ModelIn current large commercial aircraft,
thesecondary power system which includes the IPSis powered from
energy extracted from theaircraft engines. As per the nature of the
sub-systems the extractions can be divided intobleed air power and
shaft power. Typically thebleed air provides power for ECS and IPS.
Theshaft power extractions from the engines areused to operate the
hydraulic pumps whichenable the hydraulic system to operate
theconventional actuators for the flight controls.Moreover the
shaft power extraction from theengines also provides power for the
electrical
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A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I
MADANI
8
generators mounted on the engines which runthe electrics of the
aircraft.
The Off-takes model provides the interfacebetween the aircraft
systems and the engine.The model is based on the methodology in
[23].The off-takes model takes the fuel flow for anoperating
condition of the aircraft as an inputand then corrects it to
represent the penalty dueto the operation of aircraft systems. In
this casethe IPS is operated with bleed air extracted fromthe HPC
of the engine. The model wasdeveloped in Matlab/Simulink and shown
inFig. 8.
Fig. 8 Off-takes model – Inputs & Outputs
4.6 Emissions ModelThe emissions model was based on the
P3T3methodology in SAE International, 2009. It is acorrection based
method where emissionsmeasurements taken in accordance of ICAO
Annex 16 certification engine testing arecorrected to required
altitude, using combustoroperating parameters at both ground level
andthe required altitude. The baseline emissionsindices were
extracted from. The model also hasthe capability to correct the
emissions as per thehumidity, but for this study the
relativehumidity was set at 60%. The model wasdeveloped in
Matlab/Simulink and theschematic with the inputs and outputs as
shownin Fig. 9.
Fig. 9 Emissions model –Inputs & Outputs
Further reading on the emissions model can befound in [24].
5 Discussions and Analysis of Results
5.1 Case OverviewThe selected test case is a flight from
LondonAirport Heathrow (EGLL/LHR) to AmsterdamAirport Schiphol
(EHAM/AMS). Fig. 10 showsa graphical projection of a typical real
flighttrajectory obtained from FlightAware, a privateflight
tracking company [25].
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Fig. 10
The British Airways and KLM Royal Dutchairlines are the major
airlines operating thisroute. The BA uses A319 and A320
aircraftwhereas KLM uses F70 and E190 on this route.This analysis
considers an A320this route in the presence of icinThe degree of
protection provided was basedCS25 Appendix C icing envelopeTable
2.
Table 2. Appendix C Icing Envelope
Case T0(°C)
LWC(g/m3)
MVD(µm)
1 0 0.63 202 -10 0.43 203 -20 0.22 204 -30 0.15 20
Typically, aircraft encounters icing during climbto cruise
altitude, and descent and hold. This isdue the fact that the clouds
that contain supercooled water droplets exist at lower
flightlevels, and during descent and climb, aircraftreduces speed
which lessens the effects ofkinetic heating on the airframe.
Between 7,000ft and 13,500 ft during departure and arrival,
thealgorithm simulates icing condition whichautomatically puts on
the IPS and penaliseengine accordingly.
5.2 Fuel vs TimeCase 3 which represents the most probable
icingcondition was used for optimising aircrafttrajectory with a
view to minimisfuel consumption and total flight time
9
10 London – Amsterdam Flight Route [25]
The British Airways and KLM Royal Dutchairlines are the major
airlines operating thisroute. The BA uses A319 and A320
aircraftwhereas KLM uses F70 and E190 on this route.
A320 aircraft flyingthis route in the presence of icing
conditions.The degree of protection provided was based on
icing envelope shown in
Altituderange (ft)0-120000-170000-220000-22000
Typically, aircraft encounters icing during climbto cruise
altitude, and descent and hold. This isdue the fact that the clouds
that contain super-cooled water droplets exist at lower
flightlevels, and during descent and climb, aircraft
ch lessens the effects ofBetween 7,000
ft and 13,500 ft during departure and arrival, thealgorithm
simulates icing condition whichautomatically puts on the IPS and
penalises the
which represents the most probable icingor optimising
aircraft
a view to minimising the totaltime.
5.2.1 DepartureFig. 11 shows the Pareto fronts for
theconventional method and the enhanced methodafter optimisation of
the departure.the loop optimisation approachfuel savings over
usingmethod. The two resultsrespect to minimum timeexpected in the
case oflike this one.
Fig. 11 Difference in Pareto Front betweenConventional and Icing
in the Loop Approaches
5.2.2 London - Amsterdam RouteThe Pareto front for Coptimisation
is shown in
shows the Pareto fronts for theconventional method and the
enhanced methodafter optimisation of the departure. The icing inthe
loop optimisation approach gave a 2.1 %fuel savings over using the
conventional
results are quite close withrespect to minimum time trajectory.
This is
n the case of a short flight segment
Difference in Pareto Front betweenConventional and Icing in the
Loop Approaches
Amsterdam RouteCase 3 for full routeFig. 12.
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A.
Fig. 12 Pareto Front for London-Amsterdam RouteOptimisation
The Pareto shows a difference of 252between the minimum time and
minimum fueltrajectories within 3 minutesarrival time. A further
simulation was carriedout using Case 1 which represents a condition
ofhighest liquid water concentration andhighest icing. The two
Pareto fronts arecompared in Fig. 13. A difference of 66 kg
fuelburn was realised between the two cases.
Fig. 13 Pareto Front of Fuel vs Time Simulation
The minimum fuel and minimum timetrajectories for Case 3 were
compared with atypical trajectory flown by commercial aircraftfrom
EGLL/LHR to Amsterdam AirportSchiphol EHAM/AMS as shown inminimum
time trajectory was very close to thetypical trajectory of
commercial aircraft. One ofthe things learnt from this result is
that thetypical aircraft finishes climb earlier than thesimulated
baseline aircraft. This could beassociated with ATM operational
requirement atLHR.
A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I M
10
Amsterdam Route
The Pareto shows a difference of 252 kgimum time and minimum
fuel
s difference ofmulation was carried
represents a condition ofhighest liquid water concentration and
the
The two Pareto fronts areA difference of 66 kg fuel
realised between the two cases.
Pareto Front of Fuel vs Time Simulation
The minimum fuel and minimum timewere compared with a
typical trajectory flown by commercial aircraftfrom EGLL/LHR to
Amsterdam AirportSchiphol EHAM/AMS as shown in Fig. 14. The
time trajectory was very close to theercial aircraft. One of
things learnt from this result is that thetypical aircraft
finishes climb earlier than thesimulated baseline aircraft. This
could beassociated with ATM operational requirement at
Fig. 14 Trajectory for Min Time and Min Fuel
Fig 17 shows the speed variation with distance.As usual, the
minimum fuel trajectory has lowerspeed profile than minimum time
and typicaltrajectories. Because of this difference in
speedprofile, the fuel flow requirementinvestigated.
Fig. 15 TAS Comparison
Theoretically, IPS power demand is a functionof air speed and
altitude. Hence it can be seenthat 200 km from LHR, the IPS
wasautonomously operated with rest to theminimum time
trajectory.flow comparison.
R. SERESINHE, D. QUAGLIA AND I MADANI
ctory for Min Time and Min Fuel
Fig 17 shows the speed variation with distance.As usual, the
minimum fuel trajectory has lowerspeed profile than minimum time
and typicaltrajectories. Because of this difference in
speedprofile, the fuel flow requirement was
TAS Comparison
IPS power demand is a functionof air speed and altitude. Hence
it can be seenthat 200 km from LHR, the IPS wasautonomously
operated with rest to theminimum time trajectory. Fig. 16 shows
bleed
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An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
Fig. 16 Bleed Flow due to IPS
Fig. 17 shows the response of the fuel flow tothe engine based
on the trajectory choice.Although, the initial mass of the aircraft
in thetypical trajectory is not known, same conditionhas been
applied to it as the study case for easeof comparison.
Fig. 17 Fuel Flow vs Distance
The result is as expected with minimum fueltrajectory having
lower fuel flow requirement asthe minimum time or the low altitude
typicaltrajectory.
Fig. 18 Fuel Consumption Comparison
In can be noted in Fig. 18 that min fueltrajectory saves more
fuel (10.3%) than thetypical and min time trajectory. This is
because
11
An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
Bleed Flow due to IPS
shows the response of the fuel flow toed on the trajectory
choice.
Although, the initial mass of the aircraft in thetypical
trajectory is not known, same condition
dy case for ease
low vs Distance
result is as expected with minimum fueltrajectory having lower
fuel flow requirement asthe minimum time or the low altitude
typical
Fuel Consumption Comparison
that min fueltrajectory saves more fuel (10.3%) than the
. This is because
the min time trajectory is achieved throughhigher TAS as can be
seen inthe typical trajectory is at similar TAS with theminimum
fuel trajectory, it is at a lower altitudewhere air drag is
highest.
6 Summary and Conclusions
This work enabled the developand tools contributing to the
evaluation ofaircraft fuel burn under various
environmentalconditions. These models and tools enabled adependable
assessment of fuelpollutant emissions reduction thrprofile
optimization.quantitative estimates of the energymajor airframe
systemsdevelopment process for the GATACAircraft Trajectories under
ATM Constraintssoftware.
Although, operation depends solely on theroute, operator and
aircraft type, it can be notedfrom the solution of the GATAC
trajectoryoptimization that there are fuel savings andenvironmental
benefits in flying optimizedroutes in comparison to a typical
flight routebetween LHR to EGLL. Both BA anairlines fly the route
between 22,000 ft and25,000 ft. Meanwhile, the most economic
flightprofile obtained from the GATAC optimizationis a steady climb
from LHR to 37,000 ft andcruising for about 5 minutes before
gradualdescent to EGLL. The perslower altitudes by the regular
operators of theroute could be explainedminimum time trajectory.
ATM constraintalso likely to be a factor.
7 Future Work
Future work involves the integration of theweather model and
impstrategy in aircraft icing problem.avoidance strategy is only
applicable in aPerformance-Based Navigation (PBN)environment. It is
therefore basednetwork-enabled informationdesigned for future ATM
environment where
An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
the min time trajectory is achieved throughhigher TAS as can be
seen in Fig. 15. Althoughthe typical trajectory is at similar TAS
with the
fuel trajectory, it is at a lower altitudewhere air drag is
highest.
Conclusions
This work enabled the development of modelsand tools
contributing to the evaluation of
under various environmental. These models and tools enabled
a
dependable assessment of fuel consumption andpollutant emissions
reduction through a mission
The models providequantitative estimates of the energy used
bymajor airframe systems and were partdevelopment process for the
GATAC (GreenerAircraft Trajectories under ATM Constraints)
Although, operation depends solely on thete, operator and
aircraft type, it can be noted
from the solution of the GATAC trajectoryoptimization that there
are fuel savings andenvironmental benefits in flying
optimizedroutes in comparison to a typical flight routebetween LHR
to EGLL. Both BA and KLMairlines fly the route between 22,000 ft
and25,000 ft. Meanwhile, the most economic flightprofile obtained
from the GATAC optimizationis a steady climb from LHR to 37,000 ft
andcruising for about 5 minutes before gradualdescent to EGLL. The
persistence of usinglower altitudes by the regular operators of
theroute could be explained considering theminimum time trajectory.
ATM constraints arealso likely to be a factor.
work involves the integration of theweather model and
implementing avoidance
aircraft icing problem. Theavoidance strategy is only applicable
in a
Based Navigation (PBN)is therefore based on shared
enabled information capability. It isdesigned for future ATM
environment where
-
A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I
MADANI
12
flight operators must have access to current andplanned
strategies to deal with congestion andother airspace constraints.
These constraintsmight include scheduled times of use for
specialactivity airspace for military, security or spaceoperations.
They could also include changes toprocedure due to current or
forecastweather/congestion as well as the status of areaproperties
and facilities, such as out-of-servicenavigational aids. In
summary, flight operatorsmust continuously obtain up-to-date
regular aswell as potential ATM limitations, from groundoperations
to the intended flight trajectory.
In the future work, smart routing techniques willbe used to
optimize aircraft trajectory in thepresence of icing condition. The
majority ofcommercial aircraft nowadays carry an AirborneWeather
Radar (AWR) system that is most oftenbuilt into the aircraft nose.
These on-boardweather radars provide the pilots with a localweather
picture ahead of the aircraft whichallows them to identify and
avoid specific,undesirable weather formations.
Ice is detected inflight using devices that candetect the
presence of ice and send advisorysignal to pilot in some cases
trigger a de-icingaction. Ice detectors can be used in a primary
oradvisory role. Currently, there are no remote oron-board sensors
that can reliably and routinelyquantify liquid water content or
drop size. Atthe moment, the AWR system on-boardcommercial aircraft
is used for the detection ofthunderstorms, areas of strong
precipitation andturbulence only. Some AWR systems have amaximum
range of up to 180 nm with 360-degree coverage. However, since
precipitation isassociated with clouds formation, and cloudsand
moisture are the main factors for aircrafticing, it can be used in
combination withtemperature, LWC and droplet size models
forpredicting and avoiding icing areas.
To achieve autonomous capability, the IIPSmust be connected with
the FMS whose primaryfunction is the management of the
flyingaircraft. The FMS using both the GPS and INSto determine the
aircraft position can guide the
aircraft along a new path safely andconveniently. When icing
hazard is detected,two methods are involved in solving theproblem.
First is to activate the de-icingsequence through electrically
powered thermalheating of the critical surfaces; second, to
avoidthe icing condition by modifying the trajectoryto fly in a
less severe icing condition. Thecontrol law governing the decision
process overthe efficient operation between the nominal
andoptimised trajectories is given by:
ݑ = [ܶℎ, ∅, ]݊ (17)
Where ܶℎ = thrust, ∅ = bank angle and n =vertical load factor;
all of which are function oftime. The thrust component affects
thelongitudinal motion mainly, whereas ∅ affectsthe lateral motion
and n affects the verticalmotion of the aircraft.
Copyright Statement
The authors confirm that they, and/or theircompany or
organization, hold copyright on allof the original material
included in this paper.The authors also confirm that they
haveobtained permission, from the copyright holderof any third
party material included in thispaper, to publish it as part of
their paper. Theauthors confirm that they give permission, orhave
obtained permission from the copyrightholder of this paper, for the
publication anddistribution of this paper as part of theICAS2014
proceedings or as individual off-prints from the proceedings.
References
[1] ICAO (2014), ICAO Performance BasedNavigation Programme,
available at:http://www.icao.int/safety/pbn/Pages/default.aspx
(accessed 02/25).
[2] ICAO (2014), 2013 ICAO Air TransportResults Confirm Ruburst
PassengerDemand, Sluggish Cargo Market,
availableat:http://www.icao.int/Newsroom/News%20D
-
13
An Intelligent Ice Protection System for Next Generation
Aircraft Trajectory Optimisation
oc%202013/COM.43.13.ECON-RESULTS.Final-2.en.pdf
(accessedJan/15).
[3] SESAR (2012), The Roadmap forSustatinable Air Traffic
Management:European ATM Master Plan, Edition 2,EU.
[4] Advisory Council for AeronauticsResearch in Europe
(2008),Addendum to the Strategic ResearchAgenda, 2008. Brussels,
ACARE.
[5] Schumann, U. (2012), "Volcanic, Weatherand Climate Effects
on Air Transport", 28thInternational Congress of the
AeronauticalSciences, 23-28 September, 2012, Brisbane,Optimage Ltd,
Brisbane, Australia, .
[6] ICAO (2009), Performance BasedNavigation, Document 9613,
ICAO.
[7] Laskaridis, P. (2004), PerformanceInvestigations and Systems
Architecturesfor the More Electric Aircraft (PhD thesis),Cranfield
University, Cranfield University.
[8] Botura, G. C., Sweet, D. and Flosdorf, D.(2005), Development
and Demonstration ofLow Power Electrothermal De-icingSystem, AIAA
2005-1460, AIAA,Washington.
[9] GKN (2010), Composite Aircraft WingResearch gets underway at
GKNAerospace, available
at:http://www.gknaerospace.com/newsarticle.aspx?page=S633463542178688750&ArchiveID=5&CategoryID=33&ItemID=313&src=
(accessed 05/15).
[10] Petrenko, V., ( 2006), Method forModifying Friction Between
an Object andIce or Snow (Patent No. US 7,034,257),219/482 ed.,
H05B 3/02, Hanover/US.
[11] Petrenko, V. F., Sullivan, C. R.,Kozlyuk, V., Petrenko, F.
V. and
Veerasamy, V. (2011), "PulseElectrothermal De-icer (PETD)",
ColdRegions Science and Technology, , no. 65,pp. 70-78.
[12] Fortin, G. (2013), "Considerations on theUse of
Hydrophobic, Superhydrophobic orIcephobic Coatings as a Part of the
AircraftIce Protection System", SAE 2013 AerotechCongress and
Exhibition, 24-26 September2013, Montereal, Canada, .
[13] Shinkafi, A. and Lawson, C. (2013),"Evaluating Inflight Ice
Protection Methodsfor Applications on Next GenerationAircraft",
Journal of AerospaceEngineering and Technology, , no.
2231-038X.
[14] Sammut, M., Xuereb, M., Chircop, K.,Camilleri, W., Dimech,
E., Karumbaiah, D.and Pervier, H. (2013), System for
GreenOperations (SGO) ITD: GATAC V3 UserManual, , Clean Sky.
[15] Chircop, K., Xuereb, M., Zammit-Mangion, D. and Cachia, E.
(2010), "AGeneric Framework for Multi-parameterOptimization of
Flight Trajectories",Internationla Congress of the
AeronauticalSciences, vol. ICAS2010.
[16] Messinger, B. L. (1953), "EquilibriumTemperature of an
Unheated Icing Surfaceas a Function of Air Speed", Journal of
TheAeronautical Sciences, vol. 20, no. 1, pp.29-42.
[17] Shinkafi, A. and Lawson, C. (2014),"Enhanced Method of
Conceptual Sizing ofAircraft Electro-Thermal De-icingSystem", ICAA
2014 : InternationalConference on Aeronautics andAstronautics, Vol.
9, 5-6 June 2014, WorldAcademy of Science, Engineering
andTechnology, New York, .
[18] Al-Khalil, A., Hitzigrath, R., Philippi, O.and Bidwell, C.
(2000), "Icing Analysis and
-
A. SHINKAFI, C. LAWSON, R. SERESINHE, D. QUAGLIA AND I
MADANI
14
Test of a Business Jet Engine Inlet Duct",38th Aerospace
Sciences Meeting &Exhibit, 10-13 January 2000, .
[19] Quaglia, D., Ramasamy, S. and Gardi,A. (2014), Systems for
Green Operations(SGO) ITD Software Design Description:Aircraft
Dynamics Model (ADM) for3D/4D Trajectories, SGO-WP
3.1-C-U-OUT-0327, Clean Sky, CranfieldUniversity, UK.
[20] EUROCONTROL (2009), Base ofAircraft Data (BADA)
AircraftPerformance Modelling Report, 2009-009,EUROCONTROL,
Bretigny-sur-Orge.
[21] EASA (2012), E.003 CFM InternationalS.A. - CFM56-5B and 5C
Series Engines,TCDS E.003, EASA, Cologne, Germany.
[22] ICAO (2013), ICAO Engine ExhaustEmissions Data Bank
Subsonic Engines, ,EASA, Cologne, Germany.
[23] Scholz, D., Seresinhe, R., Staack, I. andLawson, C. (2013),
Fuel Comsumption dueto Shaft Power Off-take from the Engine, ,AST,
Hamburg, Germany.
[24] Seresinhe, R., Shinkafi, A., Quaglia, D.,Lawson, C. and
Madani, I. (2014),"Airframe Systems Power Off-takeModelling in
More-Electric LargeCommercial Aircraft for use in
TrajectoryOptimisation", 29th Conference of theInternational
Council of the Aerospace, 7-12 Sep, St. Petersburg, IACAS,
St.Petersburg, Russia, .
[25] FlightAware (2014), Live FlightTracking, available
at:http://uk.flightaware.com/live/flight/BAW442/history/20140418/1700Z/EGLL/EHAM(accessed
May/23).