Multi-Aircraft Trajectory Optimization for Continuous Descent Arrivals

85 Pages·2017·3.55 MB·English

Multi-Aircraft
Trajectory
Optimization for
Continuous Descent
Arrivals
Arjun Puttabakula
;

MULTI-AIRCRAFT TRAJECTORY OPTIMIZATION
FOR CONTINUOUS DESCENT ARRIVALS
by
ARJUN PUTTABAKULA
toobtainthedegreeofMasterofScienceinAerospaceEngineering
attheDelftUniversityofTechnology,
tobedefendedpubliclyonTuesdayJanuary26,2017at14:00.
Specialization: AirTransportandOperations
Studentnumber: 4419987
Projectduration: April1,2016–January25,2017
Thesiscommittee: Prof.dr.ir.DriesVisser, TUDelft,CommitteeChair
Prof.dr.ir.SanderHartjes, TUDelft,Supervisor
Prof.dr.ir.MarkVoskuijl, TUDelft,ExternalCommitteeMember
ThisthesisisconfidentialandcannotbemadepublicuntilJanuary25,2017.
Anelectronicversionofthisthesisisavailableathttp://repository.tudelft.nl/.

P
REFACE
TheMasterthesisatTUDelfthasbeenoneofthebestexperiencesofmyprofessionalcareer.The
past10monthshavebeennothingshortofarollercoasterride.Thisthesishasbeenatremen-
douslearningexperiencewithrespecttoexpandingmyknowledgebase,timemanagement,
findingtherightbalancebetweenprofessionalandpersonallife. Inshort,Iamnotthesame
personIwaswhenIfirstjoinedtheMastersprogram.
Thisthesiswouldnotbecompletewithoutthehelpofmysupervisordr. ir. SanderHartjes.
Sanderhasbeenveryhelpfulandmoreimportantlyextremelypatientwithmeduringallthe
meetingsand,isnevershortofideasandreasoning.Formehewillalwaysbetheperfectguide.I
wouldalsoliketothankdr.ir.DriesVisserforbeingaconstantpresenceinalloftheprogress
meetingsandpitchinginusefulideastoimprovemythesis.
Outsideofwork,myfriendsatDelfthavebeenmybiggeststressbustersandwereinstrumental
inkeepingmesane.FirstandforemostIwouldliketothankir.SrikarYadalaVenkata.Without
ashadowofdoubthewasagreatsupport,alwayslendinghisearformythoughts,frustration
andaddingfueltomyideaseverysingledayinthepast10months.SrikarwasalsomyMATLAB
teacher;everythingIknowinMATLABtodayisbecauseofhim.Iwillalwaysbegreatfulforhis
helpandsupport. ManythankstoMannat,MarcoandSalilforallthegameandtalknights,
barbeques,drinksandmakingsureIdon’ttalkaboutmythesisduringthesewonderfultimes
weallspenttogether.ThankstoShravanforclarifyingmydoubtsallthewayfromSwitzerland.
SpecialthankstoKashmiraforallthemotivation,workingwithme,lunchbreaktalksandfor
allthethingswedidoutsideofwork.IwouldliketothankRadeshforbeingagreatsupporter,
alwaysbelievingthatIcandoit. IwouldalsoliketoextendmythankstoApeksha,Ashwathi,
KiranandPrasobhforallthegettogethersanddinners.
NowordscantrulyexpressmygratitudeformygirlfriendSnehaSrigiri.Manythankstoherfor
beingsopatientwithmethesepast2years,constantlyremindingmeaboutwhatIamcapable
ofandforjustbeingtherealwayswheneverIneededyou.
Finally,Iwouldliketothankmyparentsandsisterwithoutwhomnoneofthiswouldhaveever
beenpossible.Itisbecauseoftheirimmensesacrificefromthepast24yearsthatIstandhere
today.Nothingcaneverreplacewhatthethreeofyouhavedoneforme.
ArjunPuttabakula
Delft,January10,2017
iii

S
UMMARY
ITisaknownfactthataircraftnoiseandfuelemissionsarethemostconstrainingfactorsforthe
growthofaviation.ContinuousDescentArrivals(CDAs)providesignificantreductionsinfuel
consumptionandnoisefootprintontheground,byfollowingidlethrustdescentandeliminating
lowaltitudelevelingoff. However, limitationssuchasunpredictabilityofthetrajectoryand
separationmanagementforCDAspreventwide-spreadimplementation.Thethesisfocuseson
overcomingsomeoftheselimitations.Optimalcontroltheoryisusedtooptimizethedescent
trajectory of the aircraft by using fuel and time as the performance index. The problem is
formulatedasamulti-phaseoptimalcontrolandissolvedbyusingapseudospectralmethod.
ThetheoryisalsothebackboneofGeneralPseudospectralOPtimalControlSoftware(GPOPS).
Themainfocusofthethesisistoenablemulti-aircrafttrajectoryoptimizationforCDAsand
ensuresufficientseparationbetweenallaircraftalongtheentiretrajectorybyimplementing
the separation algorithm. The possibility of using both distance based and the time based
separationisexploredindetail.ItisdemonstratedusingAmsterdamSchiphol(AMS)airport’s
real-timeinboundflightdatathatitisfeasibletoapplytheseparationalgorithmtoseparate
aircraftalongtheentirelateralpathwhilestillbeingabletoperformCDAsduringthepeakand
non-peakperiods.Allthelimitationspertainingtotheseparationalgorithmareanalyzedand
discussedindetail.Byaddressingsomeoftheseshortcomings,thesimulationenvironmentcan
beimprovedtobringitmoreclosetoareal-timescenario.Althoughalotofotherfactorshaveto
beconsideredforapracticalwide-spreadimplementation,successofthismethodwillresultin
theaircrafttrajectorybeingmorepredictabletothegroundcontroller,effectivelyaddressing
oneofthemajorshortcomingsofCDAs.Onamoreimportantnote,thesuccessofthismethod
willalsoresultinreducednoisefootprintandfuelconsumptionbyaircraft,benefitingboththe
environmentandairlines.
ArjunPuttabakula
Delft,January10,2017
v

C
ONTENTS
Preface iii
Summary v
ListofFigures ix
ListofTables ix
Acronyms xvii
1 Introduction 1
2 State-of-Art 3
2.1 LimitationsofCDA 3
2.2 State-of-Art 4
2.2.1 ContinuousDescentArrivalsatSchipholAirport 5
2.2.2 ContinuousDescentArrivalsatLouisvilleInternationalAirport 6
2.2.3 Time-basedSpacedContinuousDescentArrival(TSCDA) 6
2.2.4 3DPathConcept 7
2.2.5 TimeandEnergyManagedOperations(TEMO) 7
2.2.6 TrajectoryBasedOperations(TBO) 8
2.2.7 TheRoleofATCinNoiseAbatementProcedures 8
2.3 PreviousResearchBasedonOptimalControlTheory 9
2.3.1 OptimizationofNoiseAbatementArrivalTrajectories 9
2.3.2 VerticalTrajectoryOptimizationforCDA 10
2.4 ResearchObjectives 10
2.4.1 ResearchGoals 11
3 OptimalControlTheory 13
3.1 Multi-phaseContinuousBolzaProblem 13
3.1.1 GeneralFormulationofMulti-phaseContinuousBolzaProblem 14
3.2 MethodsforSolvingOptimalControlProblems 14
vii
viii Contents
3.2.1 IndirectMethods 14
3.2.2 DirectMethods 15
3.2.3 HeuristicMethods-GeneticAlgorithms 16
3.3 ExperimentalSet-Up 16
3.3.1 GeneralPseudospectralOptimalControlSoftware(GPOPS) 16
4 Methodology 19
4.1 OperationsatAmsterdamSchipholAirport 19
4.2 Point-massModel 21
4.3 LateralPathDesign 22
4.3.1 PostProcessing 23
4.4 AircraftSeparationAlgorithm 24
4.4.1 DistanceBasedSeparation-SameLateralPath 24
4.4.2 DistanceBasedSeparation-DifferentLateralPath 25
4.4.3 TimeBasedSeparation-SameLateralPath 26
5 Results 27
5.1 Single-AircraftTrajectoryOptimization 27
5.2 Dual-AircraftTrajectoryOptimization-SameLateralPath 30
5.2.1 Scenario1-OptimizationofaB733TrailinganOptimizedB733 32
5.2.2 Scenario2-OptimizationofaB733TrailinganOptimizedB744 34
5.2.3 Scenario3-OptimizationofaB744TrailinganOptimizedB733 36
5.3 Dual-AircraftTrajectoryOptimization-DifferentLateralPath 37
5.3.1 Scenario4-OptimizationofaB744TrailinganOptimizedB738 38
5.3.2 Scenario5-OptimizationofaB733TrailinganOptimizedB738 39
5.4 Multi-AircraftTrajectoryOptimization-DifferentLateralPath 41
5.4.1 Scenario6-Optimizationof10AircraftSequence 42
5.5 ComparisonofFuelandTimeOptimizedTrajectories 45
5.6 Multi-AircraftTrajectoryOptimization-AMSAirportReal-timeData 47
5.6.1 Scenario7-MorningArrivalPeakPeriod(09:11-09:20) 48
5.6.2 Scenario8-EveningEndofArrivalPeakPeriod(17:47-18:02) 50
5.6.3 Scenario9-EveningArrivalPeakPeriod(16:57-17:03) 52
5.7 AircraftTrajectoryOptimization-TimeBasedSeparation 53