Advanced Aircraft Performance Modeling for ATM: Enhancements to the BADA Model

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1 Advanced Aircraft Performance Modeling for ATM: Enhancements to the BADA Model Presented at 24 th Digital Avionics System Conference Washington D.C. October 30 November 3, 2005 Angela Nuic, Chantal Poinsot, Mihai-George Iagaru EUROCONTROL Experimental Centre, France Eduardo Gallo, Francisco A. Navarro, Carlos Querejeta Boeing Research & Technology Europe, Spain European Organisation for the Safety of Air Navigation 1

2 Introduction Accurate prediction of aircraft trajectory is a cornerstone for the development and evaluation of the future Air Traffic Management (ATM) system Aircraft Performance Model (APM) is the core of trajectory prediction BADA - Base of Aircraft Data is a kinetic, mass-varying APM developed and managed by the Eurocontrol Experimental Center BADA provides aircraft performance data and operations models suitable for trajectory prediction and simulation Current BADA 3.6 version provides aircraft performance data for 88 aircraft types An initiative (AMEBA Advanced Model Engineering for BADA) is undergoing in collaboration with Boeing Research & Technology Europe to develop BADA 4.0 2

3 BADA model structure and main features <<model>> APM <<model>> Aircraft Characteristics A340 B772 S MTOW {a 1, a 2, } {o 1, o 2, } {l 1, l 2, } <<model>> Actions <<model>> Motion <<model>> Operations <<model>> Limitations {a 1, a 2, } {o 1, o 2, } {l 1, l 2, } 3

4 Model Identification B t [min] Hp [ft] r [NM] 310/.84 ISA m [Kg] ROC [fpm] SSE. h = n i= 1 i hi ( T D ) SSE i. m = i n i= 1 v i ESFi mi g. mi + Fi 2 2 RMS = SSE n Optimization objectives (metrics) B772 {a 1, a 2, } dt T(a1,a 2,...) D(a v mg,a dh dm dt = = Model Identification F(a 20,a 21,...),...) ESF Model Instance RMS 1 =72.3 RMS 2 =45.2 RMS 3 =81.5 Reference data Observed values from reference data Predicted values based on BADA model for T, D & F Accuracy figures 4

5 Aircraft reference data sources Aircraft Operation Manuals (AOM s) low granularity and precision, normal aircraft operation data coverage Output of Aircraft Performance Engineering Programs high granularity and precision, entire flight envelope data coverage 0.9 Aircraft performance reference data points min to max a/c mass, ISA-20 to ISA Mach Hp [ft] Aircraft Performance Programs 5 Aircraft Operation Manual

6 Current BADA model - 3.X family Focuses on modelling of aircraft normal operation envelope Ensures high coverage of aircraft types Coverage of European air traffic with BADA a/c models in relation to the quality of aircraft reference data Synonym models 17% coverage 204 aircraft types 56 aircraft types 32 aircraft types Original models 27% coverage Original models 55% coverage Aircraft manufacturers programs Aircraft Operation Manual 6

7 Current BADA model - 3.X family accuracy level z Aircraft normal operations z z z Mean RMS error in vertical speed: 100 fpm Fuel flow error: 5% Complete aircraft flight envelope z B744: Absolute error in vertical speed for 18 nominal climb and descent profiles under ISA conditions Mean RMS error in vertical speed: 300 fpm B744: Absolute error in vertical speed for 195 climb and descent profiles under ISA conditions 7

8 Enhancements to the BADA model Advanced Model Engineering for BADA (AMEBA) on-going research that exploits the possibilities for improvements of the kinetic aircraft performance modeling by using today s: availability of better quality aircraft reference data computing capabilities the work performed in cooperation with Boeing Research & Technology Europe Why AMEBA? More applications rely on APM Advanced Decision Support Tools, Analysis and Validation New requirements for improved accuracy on complete aircraft flight envelope, flight phases and type of operation 8

9 BADA 4.0 objectives Provide realistic, accurate and complete aircraft performance model: with reasonable complexity, maintainability and computing requirements capable of supporting accurate computation of the geometric, kinematic and kinetic aspects of the aircraft behaviour applicable to a wide set of aircraft types, over the entire operation envelope and in all phases of flight susceptible of being identified from trajectory data 9

10 BADA 4.0 modelling premises Physical modeling analysis of the underlying physical laws governing aircraft behavior identification of the physical variables upon which aircraft performance is to be represented selection of appropriate mathematical models to relate them Systemic modeling way of organizing APM architecture as a system so functional requirements are met (e.g. provision of drag, thrust and fuel flow) ensure adequate balance of model performance with respect to nonfunctional requirements (e.g. realism, accuracy, complexity, completeness, maintainability, etc) 10

11 Approach and steps Theoretical review Dimensional analysis OOM Model selection Model identification Improvement assessment In-depth review of Flight Dynamics fundamentals underlying aircraft behavior under no simplifying assumptions other than those reasonable in the ATM context Dimensional Analysis (DA) to identify the right physical dependencies for the mathematical models created to represent the required aircraft performances (drag, thrust, fuel consumption, etc). Object Oriented Modelling (OOM) to identify the right roles and responsibilities of the different components encompassing the APM architecture. 11

12 Approach and steps Theoretical review Dimensional analysis OOM Model selection Model identification Improvement results New dependencies for drag, thrust and fuel consumption models C D = f(c L, M) T / δ = f(m, δ T ) δ Throttle δ T = f(m,δ) δ T = f(m,θ T ) for T ISA < T KINK for T ISA T KINK δ F / δθ ½ = f (M, T / δ) θ 12

13 Approach and steps Theoretical review Dimensional analysis OOM Model selection Model identification Improvement results Selection of appropriate mathematical models Polynomial functions Least square technique Raw thrust, drag and fuel flow data for 6 Boeing airplane (different size and technology) Several candidate models analysed Statistical metrics used to measure fit of model to reference data New models selected GENERALIZED THRUST MODEL (6 parameters: c, K,c ) T W MTOW(c1M c2m c3m c4δ T c 5δ TM c6δ TM ) δ = FLAT RATING MODEL (7 parameters: d, K, d ) 1 7 δ = d + d δ + d δ + dm+ dδm + d M + d M δ T,flat TEMPERATURE RATING MODEL (9 parameters: e, K, e ) δ = e + e θ + e θ + em+ emθ + emθ + em + emθ + emθ T,temp 1 2 t 3 t 4 5 t 6 t 7 8 t 9 t 13

14 Approach and steps Theoretical review Dimensional analysis OOM Model selection Model identification Improvement results DESCENTS CLIMBS MCMB Flat Obtain coefficients for thrust, drag and fuel flow from trajectory information that contains kinematic information only ENTRY IDLE THRUST = 0 DRAG GEN. THRUST DRAG GEN. THRUST DRAG GEN. THRUST CLIMBS MCMB Flat CLIMBS MCMB Temp CLIMBS MCRZ Flat DRAG MCMB FLAT RTG MCMB TEMP RTG MCRZ FLAT RTG DRAG DRAG MCMB FLAT RTG MCMB TEMP RTG MCRZ FLAT RTG MCRZ TEMP RTG IDLE THRUST ALL CLIMBS GEN. THRUST MCMB FLAT RTG GEN. THRUST CLIMBS MCRZ Temp dh v TAS = ( T D ) ESF dt W OUTER LOOP DRAG GEN. THRUST DESCENTS Trajectories MCRZ TEMP RTG INNER LOOP Trajectories ALL OUTER LOOP Process fully automated Fixed DRAG Models IDLE Models THRUST to Fit CLIMBS GEN. THRUST MCMB FLAT RTG MCMB Fixed TEMP Models RTG Models DRAGto Fit MCRZ FLAT RTG MCRZ TEMP RTG DESCENTS 14 FUEL IDLE FUEL EXIT

15 Approach and steps Theoretical review Dimensional analysis OOM Model selection Model identification Improvement assessment Assess model improvements in terms of accuracy in: Vertical speed and fuel flow Underlying aircraft forces 25 aircraft models used for assessment 18 Boeing, 4 Mc Donnell Douglas, other 3 jet commercial aircraft 15

Approach and steps Theoretical review z Dimensional analysis OOM Model selection Model identification Improvement assessment Mean RMS error in vertical speed of 70 fpm over complete aircraft flight

16 Approach and steps Theoretical review z Dimensional analysis OOM Model selection Model identification Improvement assessment Mean RMS error in vertical speed of 70 fpm over complete aircraft flight envelope Non-Boeing jet: absolute error in vertical speed for 195 climb and descent profiles under ISA+20 conditions B773: absolute error in vertical speed for 195 climb and descent profiles under ISA+15 conditions 16

and descent profiles under ISA+15 conditions Model identification Improvement assessment Fuel

17 Approach and steps Theoretical review z Dimensional analysis OOM Model selection Over complete aircraft flight envelope: Thrust error well below 10% B773: absolute error in CT for 195 climb and descent profiles under ISA+15 conditions Model identification Improvement assessment Fuel error well below 5% B773: absolute error in CF for 195 climb and descent profiles under ISA+15 conditions 17

18 Current and future work Action model Model identification for low quality reference data Turboprops and piston aircraft types Non clean aerodynamic configuration Motion and operation model Flight regimes other than constant CAS/Mach Lateral motion (roll in/ roll out) Limitations model 18

19 Conclusions BADA 3.X accurately models aircraft at typical operational conditions Substantial room for improvement exists by using today s available data and computing resources BADA 4.0 provides significant accuracy improvements over complete flight envelope, including non clean configuration Thrust and Drag models are susceptible to be identified from trajectory data 19

20 Publication This paper is going to be published on EUROCONTROL web site The European Organisation for the Safety of Air Navigation (EUROCONTROL). All rights reserved. The content represents the Author s own views which do not necessarily reflect EUROCONTROL official position 20

21 Thank you for your attention! Q&A 21

Enabling Advanced Automation Tools to manage Trajectory Prediction Uncertainty

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