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
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