Estimating Aircraft Landing Weights from Mode S Data

Open AccessEstimating Aircraft Landing Weights from Mode S DataFrank Holzäpfel and Grigory RotshteynFrank https://orcid.org/0000-0003-3182-1832DLR, German Aerospace Center, 82234 Oberpfaffenhofen, Germany*Senior Scientist, Institute of Atmospheric Physics; Associate Fellow AIAA (Corresponding Author).Search for more papers by this author RotshteynDLR, Germany†Research .Search authorPublished Online:1 Feb 2023https://doi.org/10.2514/1.C036689SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail AboutI. IntroductionAircraft mass is a key parameter controlling aircraft performance, thus the prediction path routings, climb descent profiles; hence, it also constitutes relevant information traffic management. However, these proprietary data are usually not easily accessible because airlines consider them confidential information.During definition process concepts operations (Operational Services Environmental Definition) transmitting aircraft-derived meteorological enable wide range Next Generation Air Transportation System Single European Sky Traffic Management Research applications in areas wake turbulence, air management, meteorology, was generally agreed that should be incorporated into downlink crosslink [1,2]. stated traditionally considered sensitive due operator concerns about potentially releasing competitors [2]. This classification may continue hamper provision future protocols.Because turbulence potential risk following aircraft, separation standards between consecutive have been established limit capacity congested airports [3]. Because strength vortices directly proportional weight [4], required pairwise dynamic separations approach landing with vortex advisory system [5]. Here, consideration extends operational empty maximum weight, leading ranges initial strength, which turn limiting gains such systems [6,7]. For efficient onboard volumes need avoided other as compact possible. To accomplish objective, knowledge paramount importance [8]. Also, re-categorization (RECAT) optimization initiated International Civil Aviation Organization (ICAO) requires adequate weights different categories [9,10]. The estimation runway occupancy times, another important element optimizing arrival busy airports, depends on reliable estimates weights. Moreover, planning continuous or adaptive increased glide slopes aiming at reduced fuel consumption noise mitigation depend gross [11,12].A number partly quite extensive methods phases flight reported literature. Chati Balakrishnan applied statistical machine learning techniques (Gaussian regression) model takeoff using recorder ground roll, achieving mean absolute error 3.6% [13]. Alligier et al. focused during phase point-mass parameters observations fitting modeled power observed energy rate [14,15]. Sun estimated total where validation experiments yield 4.3% actual [16]. Fricke presented technique estimate calculating optimum employing mechanical measured speed [11]. based 12 trajectories yields an average deviation recorded masses 12.3%. Measurements Memphis Dallas/Fort Worth resulted overall 85% (MLW) [17]. value adopted typical “European Proposal Revised Wake Turbulence Categorisation Separation Minima Approach Departure” (known RECAT-EU) [18].This Note employs equations suggested speeds depending mass, Base Data (BADA) [19] before touchdown. type, calibrated airspeed, density derived mode protocols. 3328 approaching Vienna Airport, provided Austrian Airlines analysis field measurement campaign [20,21] conducted May November 2019, serve reference masses. A simple correction inclination pilots fly somewhat faster than prescribed handbooks eliminates bias current database.II. MethodA. Mode-S-Based Mass EstimatesAt has accomplished 6 2019 until 28 order assess so-called plate lines mitigate risks final [20,21]. measurements three lidars (light detection ranging) were five planes altitudes above determined 40.8, 45.8, 54.3, 64.8, 74.5 m, respectively, standard 4.9 m. estimates, interpolated planes, all statistics smoothen scatter caused intended airspeed variations those introduced well random error.Mode secondary surveillance radar allows selective interrogation ground-based interrogators airborne transponders [22]. protocols contain date, time, position, attitude, atmospheric pressure, wind speed, temperature update 4 s. Metadata file each provide call sign, origin, destination, time stamps start end tracking, landing. From available parameters, determination type values true temperature.According BADA [19], airspeeds VCAS jet turboprop amount VCAS=CVmin⋅Vstall+VdDES(1)where CVmin=1.3, Vstall stall configuration particular type. increment VdDES amounts 5 kt below 1000 ft, 10 1500 20 2000 50 3000 ft. operating vary mass. variation according Vstall=Vstall,ref⋅mmref(2)where Vstall,ref mref its respective Eqs. (1) (2), can m=(VCAS−VdDESCVmin⋅Vstall,ref)2mref(3)The VTAS protocol translated relation VCAS=VTAS ρ/ρ0(4)simplified low altitudes, ρ0 sea level Standard Atmosphere. ρ retrieved pressure p T, equation state perfect gases ρ=p/RT, gas constant R=287 J/kg⋅K dry air. More elaborate computations (see user manual [19]) only 0.027%, therefore here.B. Reference DataAustrian kindly m landings their fleet trial. 2958 A320 family representative ICAO category medium D (upper medium) RECAT-EU scheme. comprise 560 A319 1932 466 A321 aircraft. Landings B767-300ER falling C (lower heavy) scheme 225. B777-200ER 145 most frequent B within database (1.2% share).These data. quoted consumption. Before every flight, load balance sheet established, contains planned After corrected taxiing subtracted captured flow measuring devices located engines displayed cockpit display set zero engine start.III. Statistical AnalysesTable 1 presents (LW) types. Although almost perfectly coincides MLW found collected [17], types heavier 6.9 (B777) 7.8 (A320) percentage points. deviations, σ[(LW)/MLW], varying 2.7 5.4% appear relatively small.Figure depicts (3) (4) plotted against Airlines. avoid unrealistically high masses, alternatively clipped off (black dots) 95% (blue dots), fraction arbitrarily chosen empirical value. clusters representing (bottom left), (center), (top right) clearly delimited. Figure 2 shows corresponding plots individual exhibit performance our detail. Whereas Fig. good alignment linear regression curves mainly related distinct categories, tend tilt off.The one frequently vertically aligned points any while passing corresponds variations, whereas effect apparent few outliers estimates. Obviously, overestimated reasonably low.Fig. Estimated fractions several types.Fig. types.Table lists biases deviations without clipping. features largest relative overestimates show smallest deviations.The exclusion reduces mostly (Table 3). exhibits both highest Table 1) 2), frequency highest. 0.85 MLW, MLWs exceeded figures unchanged compared 2.Clipping 0.95 4) little impact deviations. As seen Figs. 2, clipping compensates overestimation reduced.Typical slightly higher command certain buffer unforeseen events, rapid changes direction. comparison Quick Handbook [23] indicates 1.2 m/s scheduled. considers headwind assumes employed “full” activated autothrust system.Figures 3 doubling substantially. consequence, completely vanishes detailed 5, remains same. With adjustment, deviate ideal slope 6%. appears overdone Boeing but seems appropriate Airbus aircraft.Figure delineates distribution differences VdDES=10 Unsurprisingly, skewed steeper decline toward MLW.Fig. types.Finally, ft touchdown used suitability Sec. II.A. purpose, deceleration height interpolation VdDES, much deviation. It 0 6.7%. roughly doubled 12.7%, tripled 18.9%, fourfold 27.4%. higher-altitude increase substantially, presumably control prescribes situation. Already, limits application substantially many purposes.Fig. Distribution VdDES.IV. ConclusionsAircraft comprising temperature. These deduced obtained database. family, B767-300ER, B777-200ER. agreement achieved unmodified fair, low. exceeding MLW. eliminated tendency exceed speeds. At heights increases actually flown increasingly assumed database.AcknowledgmentsThis project received funding framework Joint Undertaking “Increased Runway Airport Throughput” (PJ.02 EARTH) “Safely Optimized (VLD3-W2 SORT) Union’s Horizon 2020 Innovation Programme under grant nos. 731781 874520 DLR, Center (DLR) “Wetter und Disruptive Ereignisse.” W. Wurzinger greatly acknowledged, L. Strauss C. Weiß MeteoServe Wetterdienst GmbH highly appreciated. Finally, we give thanks D. Vechtel DLR’s Institut für Flugsystemtechnik instructive discussions. References [1] “Aircraft Derived Meteorological via Link Vortex, Weather Applications, Operational Definition (OSED),” RTCA STD DO-339, Washington, D.C., June 2012. Google Scholar[2] “Standards Development Activities Near-Real-Time Aircraft-Derived Future Applications,” DO-360, Sept. 2015. 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ScholarTablesTable LandingsLW/MLW, %σ(LW/MLW), %A320 family295892.85.4B767-300ER22584.92.7B777-200ER14591.93.6Table Deviations (not clipped) LandingsBias, %σ, family29588.65.1B767-300ER2253.83.7B777-200ER1454.63.6Table (clipped 100% MLW) LandingsClippedBias, family295817785.33.9B767-300ER22523.83.6B777-200ER145304.13.2Table family295824141.64.7B767-300ER225203.63.4B777-200ER145992.03.2Table ktLandingsBias, %All aircraft533288.15.21033280.35.3 article FiguresReferencesRelatedDetails What's Popular Articles Advance CrossmarkInformationCopyright © 2023 Deutsches Zentrum Luft- Raumfahrt (DLR). Published American Aeronautics Astronautics, Inc., permission. All requests copying permission reprint submitted CCC www.copyright.com; employ eISSN 1533-3868 initiate your request. See Rights Permissions www.aiaa.org/randp. AcknowledgmentsThis discussions.PDF Received24 September 2021Accepted1 January 2023Published online1 February