aircraft performance parameter estimation using global ads

Aircraft Performance Parameter Estimation using

Global ADS-B and Open Data

Thijs W. Gloudemans∗

TU Delft, Delft, Zuid Holland, 2629HS, Netherlands

To enable low cost open source ATM simulations the University of Technology Delft isdeveloping an open source ATM simulator Bluesky. A method was developed to identifyaircraft performance parameters using ADS-B and other open sources of data. The goalis to determine the operational flight envelope and get estimates for the lift- and dragcoefficients. The method streams global ADS-B data from Flightradar24. By making as-sumptions on wind and flight strategies, estimation can be obtained for aircraft parameters.The nature of these assumptions limit the aircraft types being analyzed to commercial air-craft only. The method measures the operational flight envelope and estimates weight, liftand drag coefficients for multiple phases in flight. Next the operational flight envelope wascompared to open data. Here it was found that the estimations showed similar values asthe open data and that the operational flight envelope can be estimated using the method.The drag polar was compared to BADA, which showed a consistent underestimation of thedrag polar.

Nomenclature

V∞ True air speedα∞ Acceleration, m/s2

s Distance, mM Mach numberW Weight, NT Thrust, NL Lift, ND Drag, NCL Lift coefficientCD Drag coefficientCD,0 Zero-lift drag coefficientK Induced drag coefficientγ Flight path angleφ Bank angleψ Heading angleρ Air densityS Aircraft wing surfaceψ Heading angle

I. Introduction

With the introduction of Bluesky1 in 2013, the Air Traffic Management (ATM) department of the DelftUniversity of Technology (DUT) is developing a free and open source real- and fast-time air traffic simulator.The goal of Bluesky is to provide everybody who wants to visualize, analyze or simulate air traffic with a

∗Graduate student, Faculty of Aerospace Engineering, Kluyverweg 1

1 of 34

Delft University of Technology

tool to do so without any restrictions, licenses or limitations. The current version of Bluesky can be usedfor ATM and Air Traffic Flow (ATF) simulations. Bluesky provides the user with a graphical user interfaceshowing selected traffic flows in real- or fast-time on a map. The user can load traffic scenarios, influenceaircraft’s behaviors and perform metric analysis.

At the core of Bluesky are two aircraft performance models (APMs). The first APM is the Base ofAircraft Data (BADA). BADA is a proprietary APM developed by Eurocontrol. Since the proprietarycharacteristic conflicts with the goal to develop an open source and free ATM simulator, also a BlueskyAPM is under development. One of the major difficulties in developing an APM lies with the identificationof these proprietary aircraft type-specific parameters, such as the operational flight envelope or the liftand drag coefficients. For some aircraft these parameters can be found in literature, but the parametersfor most modern commercial airliners are not published by their manufacturers. The aircraft type-specificparameters are key parameters for proper aircraft modeling. Many different aircraft types exist, all of themhaving different modeling parameters. Identifying these parameters is therefore important when making anAPM.

The basis of this research lies in the new ability to use the wealth of data that automatic dependentsurveillance - broadcast (ADS-B) messages provide. Global networks of ADS-B receivers allow for continuoussurveillance of the global airspace. The assumption is that this new source of Big Data can reveal knowledgeof aircraft flight performance that was previously only available to manufacturers, operators and air trafficcontrol. The goal of this research is to find or estimate the operational flight envelope. Also efforts are madeto estimate the lift- and drag coefficients. This knowledge can then be used to improve the Bluesky APM.It must be noted that this is an ambitious goal, with some large hurdles in the way such as aircraft weightestimation. The priority in this research is to obtain results and to set out the steps necessary to get them.This might lead to the use of simplified methods and assumptions. The major requirement for the use ofmethods and assumptions is that they can be enhanced in future research.

ADS-B is a secondary surveillance system. An aircraft equipped with an ADS-B transponder will con-tinuously broadcast flight information such as position, speed, altitude and heading. The data used in theanalysis is obtained from Flightradar24.2 Flightradar24 is an application that bundles ADS-B data obtainedfrom many ADS-B receivers around the world and projects the data on a map. This allows users to trackflights in almost real-time. With a premium account it is possible to query the Application Program Interface(API) for the state of the global airspace. The terms of usage of this account allow for unlimited processingand redistribution of these data, making Flightradar24 an ideal source for large amounts of historical flightdata.

To unravel the knowledge from the ADS-B data a structured approach is used. The process can bedescribed by six subsequent steps, namely; understanding the problem, understanding the data, preparationof the data, data mining, evaluation and implementation. Some of these steps are iterative. This article issetup according to these steps. Although the first two steps have been performed in preliminary research ashort recap will be provided for completeness. Next the method, which composes the preparation and thedata mining steps, is discussed. Finally the results are discussed and an evaluation is performed.

I.A. Background

The need for APMs has been of significant importance to many stakeholders in industry for a long time. In1971, Boeing and NASA worked together on an APM for the B747.3 This six degree of freedom APM wasnot only needed for use in ATM concepts, but also for use in flight training simulators and control design.These dynamic models were capable of predicting dynamic and kinematic behavior relatively well. In theirtests NASA and Boeing determined the aerodynamic and control coefficients from in-flight measurements.This made the model expensive to obtain.

Researchers came up with other, cheaper methods, that suited ATM research better. In the 70s theEROCOA and the PARZOC4 were developed. The EROCOA method is a technique which describes theclimb performance of aircraft by using a set of 8 coefficients. In a similar fashion identical performance forthe en-route and descent could be obtained from the PARZOC approach. Using these methods the verticalprofile of aircraft flying defined speed regimes could be computed through parametric models. Althoughinitially designed for use in flight profile prediction modules for application in on-line ATC systems, theseapproaches proved also suitable for use as aircraft models in ATM simulators.

With the increase in computational power, many different ATM simulators were developed, such asASTOR,5 ACES,6 TMX7 and many more. Most of them started off with an in-house developed APM. In

2 of 34


the early 1990s EUROCONTROL started developing BADA,8 an APM database. The initial goal of BADAwas to realistically simulate en-route aircraft behavior under nominal operating conditions, while ensuringlarge coverage of aircraft types.

BADA APM is based on a kinetic approach to aircraft performance modeling. It is made of two com-ponents. The first component are the model specifications. The model specifications provide the theoreticalfundamentals used to calculate aircraft performance parameters. The second component are the datasets.The datasets contain the aircraft-specific coefficients necessary to perform calculations. Nowadays mostATM simulators use the BADA APM. For example the ACES simulator can use both the Kinematic Trajec-tory Generator (KTG)9 as the Multi Purpose Aircraft Simulation (MPAS).6 Both are used to model aircraftperformance, but at the base of these simulators or generators is the BADA performance database.

The BADA database is built using radar data from EUROCONTROL. Even though BADA is widelyaccepted as the standard it is not flawless. BADA uses reference data to validate its models.10 Thisreference data is generally obtained from Aircraft Operation Manuals (AOMs), but some manufacturersprovide performance data. The drawback of these AOMs is that the resolution of the data provided can bevery low and only applies to general situations.11 This could lead to errors in the model.

Where BADA uses the radar data, everybody else can use ADS-B. As part of international cooperationADS-B has been cast as the next generation aircraft surveillance method and will be mandatory for aviationin many parts of the world by 2020.12 The broadcast characteristic of ADS-B makes it suitable for large scalemonitoring. Everyone with an ADS-B receiver can track flights. With a global network of ADS-B receiversit would be possible to track all flights in the air. Aircraft equipped with ADS-B broadcast altitude, groundspeed, latitude, longitude, heading, ICAO identification and call sign.

Using this data, one of the major pillars of BADA has become available to the public, namely the trackdata. The other source of data used in BADA such as the AOMs remain closed source, but open alternativesexist. Most manufacturers place key parameters such as weights, dimensions, engine types and capacityon their website.13 Other parameters such as fuel flow can be obtained from the ICAO Engine EmissionsDatabank.14 Combining all these open sources a non proprietary APM could be constructed by performinga similar system identification as BADA does. This research strives to be the initiator by identifying theoperation flight envelope and estimations for lift- and drag coefficients for the most used aircraft types.

A crucial component in this analysis is wind. Aircraft performance is very dependent on true air speed,but ADS-B only provides ground speed. To determine the true air speed using the ground speed the windspeed and direction need to be known. Since this research is an initial attempt to use ADS-B for aircraftsystem identification the wind is not taken into account. Within the scope of this research this assumptionis justified for simplicity. Extending this method with weather estimations would be possible, since a lot ofresearch has been performed in meteorological modeling.

The major issue with meteorological modeling is the high degree of non-linearity and dependency oninitial conditions. This is called the ’butterfly effect’.15 For systems that show the ’butterfly effect’, smallchanges in the initial condition can have large influence on the output. The atmosphere shows this effect andobtaining good, distributed measurements is difficult. These errors in the measurements propagate throughthe system, potentially creating very different output. Therefore it is only possible to have good predictionsfor only a few hours. Many weather stations perform atmospheric soundings every 12 hours to gather inputdata for the models. Much of this sounding data is open and can be downloaded.16

Two large organizations provide global weather forecasts using frequent observations. The first is theEuropean Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF is an international organizationthat provides weather forecasts to the national weather centers of the European Union. The model thatthey use is the Integrated Forecast System (IFS), which is a proprietary model. The second center is theNational Center for Environmental Prediction (NCEP). The model used by NCEP is the Global ForecastSystem (GFS). These two models are extensively used for aviation forecasts and can provide reasonable windpredictions. Unlike IFS the GFS output is free and open and can thus be used in subsequent versions of thisanalysis.

So far ADS-B has not been extensively used for aircraft parameter identification, but some applicationshave been discussed in literature. These are discussed next.

I.B. Previous Research

Networks of ADS-B receivers started to emerge around 2006 and large global networks were not availableuntil 2009. Researchers could only use partial tracks obtained from their receivers, which have a limited

3 of 34


range. In 2009 Delahaye17,18 tried to estimate the true air speed from ADS-B. The methods developedlooked at turning aircraft and subsequently at the change in speed during the turn. To extract a windmeasurement from a radar track of a turning aircraft, the vehicle’s velocity in the y-direction was plottedagainst its x-direction velocity. If there was no wind, the aircraft would maintain a constant speed withrespect to both the air and the ground. Using the wind vector and the ground speed it is then possible todetermine the true air speed.

Recently Sun19 proposed two methods to determine the aircraft mass based on ADS-B data. Sun statesthat given standard atmospheric and wind conditions the acceleration profile for an aircraft type is closelycorrelated with its weight. The two methods make use of this by looking at the intermediate measurementsduring take-off and the lift-off speed. This method is used as the basis for the weight estimation performedin this study. But instead of using lift-off speed the take-off distance is chosen.

To identify the phase of an aircraft, Sun20 used fuzzy logic to classify a measurement as either ground,climb, cruise or descent. Based on the altitude, speed and rate of climb a measurement is assigned to oneof the phases. Again this method will be used as basis for the current study. However there are somechanges. The first change is the number of phases. This will be extended with 7 other phases to isolatephases with a specific lift configuration, such as take-off, initial climb and final approach. Sun does notprovide a membership function for every phase, instead higher abstraction levels are used. For example analtitude can be low, medium or large. Although this leaves fewer membership functions it does make theinference more challenging, because it requires the levels to be assigned to the phases. Another disadvantageis that it makes tuning of the membership functions more difficult, because changing a membership functionwill influences multiple phases.

Due to the small time that ADS-B is available it has not extensively been used for performance parameterestimation. It mainly has been used to determine wind speed and direction. The KNMI tried to performupper airspace measurements using ADS-B data.21 For these measurements they not only decoded theADS-B message, but also other Mode S messages that were received. Decoding these messages gives accessto interesting parameters such as pressure altitude and indicated air speed. A large difference here is thatthose messages are not available on a global level, because the aircraft transponder needs to be interrogatedby ATC for it to respond with the Mode S message.

In 2012 de Leege22 extended two methods to estimate wind using ADS-B data. The first method is basedon Delahaye,17,18 which was discussed above. The second method is based on the assumption that aircraftat the same altitude travel, on average, at the same true air speed in all directions. This enables estimatingwind for a larger area at fixed time and altitude intervals.23

Aircraft performance has been researched the last few years, but not by the use of ADS-B. In 2014Alligier24 used machine learning algorithms to estimate the mass and thrust of an aircraft in climb. Thedata used was obtained from the Paris Air Traffic Control center, which included flight trajectories, ModeC and weather reports. The data from ATC, as with the Mode S, contains more information than ADS-Bmaking this method not compatible with the goal of this research.

I.C. Understanding the Data

Quickly stated, the goal is to find the operational flight envelope and lift- and drag coefficients using ADS-Bdata and other open sources. The first step is to reflect upon which data is available. First there is the ADS-B data, which consist of altitude h, ground speed Vgr, heading ψ, latitude, longitude, call sign and ICAOnumber. On top of the ADS-B data, Flightradar24 also provides the aircraft model, destination, origin, rateof climb Vroc and a time stamp for each flight measurement. This data is updated approximately every 5seconds for each aircraft that has its ADS-B transponder turned on and is within Flightradar24 coverage.Such a set of ADS-B data will be called a measurement from now on.

For each aircraft type the following weights are known; maximum take of weight WMTOW , maximumlanding weight WMLW , zero fuel weight, WZFW , operating empty weight WOEW and maximum fuel weightWMFW .25,26 The aircraft dimensions are known, but only the wing surface area S is used. For every aircrafttype the most used aircraft engine types are known.14 The corresponding fuel mass flows for take-off mto,cruise mcr, approach map and idle midle are available. Also the rated thrust Tmax at sea level is known.Finally the thrust specific fuel consumption ct is also found.27

Using this the operational flight envelope can be determined. The operational flight envelope is definedhere as the operational limits, which are minimum true air speed VTASmin , maximum true air speed VTASmax ,maximum altitude hmax, minimum rate of climb Vrocmin

, maximum rate of climb Vrocmax, minimum turn

4 of 34


radius Rmin. Load limits are not taken into account, because it can be safely assumed no flying aircraft willcome close to its limits.

II. Method

II.A. Operational Flight Envelope

Now that there is an understanding of the available data the first parameters can be determined. Thefirst parameter to be determined is the true air speed VTAS because it is a required parameter in manymathematical relations. As discussed earlier no wind estimations are known. Therefore the VTAS is assumedto be the sum of the velocity vectors Vgr and Vroc as seen in Equation 1.

VTAS =√V 2gr + V 2

roc (1)

The next parameter to be calculated is the flight path angle γ. Again this can be determined using Vgrand Vroc given in Equation 2.

γ = arctan(VrocVgr

) (2)

Now using each subsequent measurement in the measurement signal the discrete time derivatives, VTAS ,Vroc, ψ and γ can be determined.

The parameters calculated so far combined with the measurement is what will be referred to as the stateof the aircraft. The operational flight envelope can be determined from all the determined states of anaircraft type. This allows finding the minima and maxima of the whole flight, but this leaves the questionin which phase this minimum or maximum occurs. During a flight an aircraft will go through multiple flightphases. Each phase has its own flight strategy and operation. Knowing the minima and maxima for allthese flight phases is more interesting than just for the whole flight. Therefore each flight is classified intodifferent phases.

The method used is an extended version of Suns20 method. The major difference is that the numberof phases is increased to make a distinction between phases where the aircraft is in high-lift configuration.The flights are separated into 12 phases, namely; ground, take-off, landing, initial climb, climb, cruise climb,cruise, horizontal flight, initial descent, descent, final approach and miscellaneous. In each of these phasesthe type of flight differs from another. For example the difference between initial climb and climb is theconfiguration an aircraft is in. In initial climb the aircraft is generally in a high-lift configuration to be ableto lift-off, while in climb the flaps are retracted to clean configuration. Another example is cruise and thehorizontal flight. The main difference is altitude, but the major goal is to separate aircraft that are in climbor descent but need to maintain altitude due to ATC from actual cruising aircraft.

The classification is done using fuzzy logic. All the phases except ground, landing and take-off are clas-sified based on VTAS , h and Vroc. The only distinction between ground, landing and take-off is accelerationVTAS . Therefore these phases are also classified based on VTAS . Taking the derivative of a noisy signal willincrease the noise. It is difficult to use VTAS for classification directly. A choice is made to smoothen VTAS

before classification. This is done using a moving average with a window of 4 measurements.In Figure 1 the membership functions of the fuzzy logic are shown. Every phase has its own membership

function per variable. Inference is performed by finding the phase with the maximum degree of membership.Membership degree is determined by multiplying each membership degree for each variable as shown inEquation 3.

fphase = msVphasemshphase

msrocphase(3)

Here fphase is the degree of membership for a phase. msVphase, mshphase

and msrocphaseare respectively

the degrees of membership for the variables VTAS , h and Vroc. For the ground phases ground, take-off andlanding also the membership degree of VTAS is added. Next the measurement is assigned to the phase withthe highest degree of membership.

Phase = argmax([fground, ftakeoff , ..., ffinalapproach, flanding]) (4)

5 of 34


0 50 100 150 200 250 300 350m/s

0.00.20.40.60.81.0

Speedgroundtake-offlandinginitialclimbclimbcruiseclimbcruisehorizontalflightinitialdescentdescentfinalapproach

0 2000 4000 6000 8000 10000 12000m

0.00.20.40.60.81.0

Altitude

−10 −5 0 5 10m/s²

0.00.20.40.60.81.0

Accelerationgroundtake-offlanding

−20 −15 −10 −5 0 5 10 15 20m/s

0.00.20.40.60.81.0

Rate of climbdescending phaseshorizontal phasesclimbing phases

Figure 1. Membership functions used for classification

When all membership degrees are zero the measurement is classified as noise. In Figure 2 the resultingclassification of a B737-800 flight can be seen.

−1000 0 1000 2000 3000 4000 5000time (()

0

2000

4000

6000

8000

10000

12000

14000

Al)

)u

de

(m

)

−1000 0 1000 2000 3000 4000 5000) me (()

0

50

100

150

200

250

300

Gro

un

d S

pe

ed

(m

/()

GroundLand ngF nal approach

De(cen)In ) al De(cen)Hor zon)al fl gh)

No (eTake-offInitial climb

ClimbCruise climbCruise

Figure 2. Flight B737-800 different phases

The quality of this method depends on the quality of the tuning. The membership functions are manuallygenerated and differ for each aircraft type. Proper configuration requires some background knowledge of theaircraft. Errors in configuration could cause a measurement to be assigned noise when it could have beenassigned to a phase. The percentage of noise for classifying phases for a B737-800 are around 1.5%. This

6 of 34


includes falsely identified noise. This percentage for the Airbus A320 currently is 2.6%.Now that each aircraft state is classified it becomes possible to start performing statistical analysis. With

each measurement and corresponding full state the parameters in the state are binned, the mean is calculatedand standard deviation is updated. When a value is larger than the current maximum this value is set asthe maximum with the limitation that it cannot be larger than three times the standard deviation. Thislimit is introduced to make sure that the minimum or maximum will not be set by noise. Next the lift- anddrag coefficients are discussed.

II.B. Lift and Drag Coefficients

So far the method describes only how to obtain statistics from measurements and direct derivatives. Butone of the goals of this research is to obtain an estimation for the lift- and drag coefficients CL and CD. Twomajor components for determining CL and CD are the aircraft weight W and the thrust T . These are notknown and therefore need to be estimated first.

II.B.1. Aircraft Weight Estimation

Aircraft weight estimation in itself is already a very difficult topic for which many methods exist. Theyrequire a lot of assumptions and only work in a specific flight phase. Here a simple method is used for weightestimation. It is possible to try and estimate W during any moment of the flight, but this requires CL, whichis not available. The approach used here, similar to Sun,19 is to determine the take-off weight. The weight isestimated based on the assumption that aircraft on average use similar thrust and flap settings for take-off.This leads to a longer take-off distance when the aircraft is heavier. It is also recognized that distance offlight influences the fuel weight needed for the flight. The method used divides the weight into three parts;empty, fuel and payload weight. The empty weight is known. The other two are calculated as follows.

Wfuel = mcrg0stotalVaverage

(5)

Where mcr14 is the fuel mass flow in kg/s, stotal the flight distance and Vmean is the mean speed during

cruise determined from all measured flights. The payload weight is determined by comparing the measuredtake-off distance sto to the maximum of previous measured take-off distances.

Wpayload =sTO

sTOmax

Wmplw (6)

Here sTOmaxis the maximum take-off distance measured and WMPLW is the maximum payload weight.

Finally the total take-off weight is determined by summing up the three parts.

WTO = Weow +Wfuel +Wpayload (7)

By choosing to make only Wpayload dependent on measurements reduces the influence of noise.

II.B.2. Lift coefficient

Now that W has been estimated it is possible to estimate CL. Assuming that the aircraft is in steady flightand L = Wcos(γ) holds then CL can be calculated using Equation 8.

CL =2Wcos(γ)

ρV 2TASS

(8)

Earlier the point was made that splitting up the flight in multiple phases was advantageous to determinethe flight envelope. Another reason to split up the flight in phases is the difference in CL. The CL isdependent on the aircraft configuration and angle of attack α. These are settings that are not knownexactly, but high, medium and clean configurations can be expected. During take-off and initial climb theaircraft is in a medium high configuration after which it changes to clean configuration with a higher α.Until the descent phase the aircraft is in clean configuration. During final approach and landing the aircraftis in high configuration. By dividing the flight in phases an estimate for CL can be gained for the differentconfigurations. This is similar for CD which is discussed next.

7 of 34


II.B.3. Drag coefficient

The last parameter to be found is the drag coefficient CD. Determining CD directly would require knowledgeof T . Thrust settings are not known during analysis therefore other methods were explored which are limitedto only climbing, cruise and descending phases.

During climb CD can be estimated by assuming that dTdVTAS

= 0. Although this is not true for the total

climb it can be assumed true for just one time step. By using the excess power relation from Anderson28

the change in D becomes equal to the change in excess thrust (T −D). The excess thrust can be calculatedfrom the Total Energy Equation obtained from Anderson.28 Assuming that CD is quasi constant it can becalculated as follows.

CD =−1

ρ∞VTASS

d(T −D)

dVTAS(9)

During cruise phase the excess force (T −D) becomes zero, which makes the previous method unfit. Onthe other hand this condition does allow for another advantage, namely T = D. Using the thrust specificfuel consumption ct and the change in weight ∆W the T can calculated. Except ∆W is not known. This issolved in the following manner.

For the cruise phase the CL is determined for the first ten minutes of cruise. For the remainder of thecruise CL is assumed to be constant and equal to the just determined CL. Assuming that Ln = Wn thediscrete time derivative of the weight can be calculated using Equation 10.

∆W

∆t=Ln −Wn−1

∆t=

0.5CLnρ∞n

V 2TASn

S −Wn−1

∆t(10)

Here n is the n-th measurement. This change in W is only caused by fuel burn. The T can be derivedby dividing the change in weight with the thrust specific fuel consumption. This is shown in Equation 11.

T = −∆W

∆t

1

cT(11)

During cruise it is assumed that T = D. Subsequently T can substitute D in the drag equation fromAnderson.28 This leads to Equation 12.

CD =2T

ρ∞V 2TASS

(12)

The drag polar consists of two main parameters: the zero-lift drag coefficient CD0and the induced drag

CDi, which is equal to kC2

L. Together, they can be used to determine CD according to Equation 13 obtainedfrom Anderson.28

CD = CD0+ CDi

= CD0+ kC2

L (13)

For some special flight strategies, such as maximum range or maximum performance it can be shownthat there is fixed relation between CD0 and CDi .

28 In this method it is assumed that aircraft in cruise willfly at at maximum range strategy. This strategy allows to fly as far as possible with the minimum amountof fuel. This strategy leads to the following relation.

CD0= 3CDi

(14)

Using this relation and CD both parameters can be determined and a full drag polar can be generated.The last method to determine CD is using descending phases. Most commercial airliners reduce the

thrust to idle during descent to save fuel. By assuming that the aircraft maintains a glide path angle formaximum range, to optimize fuel use, leads to the aircraft flying at its optimal lift over drag ratio. FromAnderson28 a simple relation between glide path and lift over drag ratio during descent is obtained, whichis given in Equation 15.

tan(γ) =1

L/D=

1

CL/CD(15)

Combining Equation 15, the lift equation from Anderson28 and the assumption that this is a steadydescent, meaning L = Wcos(γ), Equation 16 is obtained.

8 of 34


CD =Wcos(γ)

0.5ρSV 2∞tan(γ) (16)

Similar to the cruise phase it is possible to determine the drag polar for the descending phases. It canbe shown that during the maximum range strategy in descent CD0

and CDiare equal. Using this knowledge

and the determined CD the drag polar can be determined.So far the method has described the calculations and assumptions needed to determine the parameters.

But it was not yet shown how this is implemented. Next the implementation is discussed.

II.B.4. Statistics

For every measured parameter a statistic is maintained. Every measurement is binned, the mean is calculatedand standard deviation is updated. To cut on memory usage the previous values are not stored. This meansthat the mean and standard deviation need to be updated. This is done using Equation 17 and 18 obtainedfrom Dekking29

µi+1 =Niµi +Xi+1

Ni+1(17)

σi+1 =

√Niσ2

i + (Xi+1 − µi+1)2

Ni+1(18)

Here Ni is the counter that tracks the number of measurements added, µi is the mean, Xi is the valueof the random variable and σi is the standard deviation.

The maximum and minimum are updated by checking if the new value is larger or smaller than thecurrent maximum or minimum. When a value is larger than the current maximum this value is set as themaximum. A value larger than three times the standard deviation is ignored. This limit is introduced tomake sure that the minimum or maximum will not be set by noise.

II.C. Implementation

The amount of data that needs to be processed in this method is larger than most computers can handle intheir memory. Therefore the following method was chosen to make processing of this data possible on normalmachines, without large amounts of available system memory. In Figure 3 an overview of the implementationcan be seen.

Figure 3. Flow chart of APE tool

The main idea behind the implementation is on-line processing. Meaning that when a measurementis obtained it is directly processed. This circumvents the need for large storage. For every measurementobtained from Flightradar24 the steps in Figure 4 are performed.

First the measurement variables are updated. Then they are filtered. In this study only the acceleration issmoothened. Next the state of the flight is updated and the phase is determined using fuzzy logic. Dependingon the phase the methods discussed earlier are applied to calculate W , CL , CD,, CD0

and CDi. Finally the

statistics are updated.When a flight is finished it needs to be removed from memory, but occasionally a measurement is missing.

Therefore a distinction needs to be made between missing flights and finished flights. Depending on the phase

9 of 34


Figure 4. Flow chart of inner functions of the APE tool

and state of the last measurement the program waits either 20 minutes or 3 hours before deleting the flightobject. The 3 hours is necessary for trans-Atlantic flights, where it is common that aircraft go unobservedfor a few hours. A more detailed description of the implementation is given in the Appendix.

II.D. Evaluation

At this point the method produced statistical information on the parameters of interest. Next these valuesneed to be verified and the method validated. Because there is only limited actual comparable data publiclyavailable. The flight envelope parameters are compared to public data on manufacturers websites and trustedinstances. For this mainly the Eurocontrol Aircraft Performance Database (EPD)30 is used. The drag polaris compared to BADA. Since the BADA data is proprietary only the maximum differences are shown. Finallythe parameters are used to perform a simulation. For this simulation a simple point mass model is made.Next the ground speed, rate of climb and flight path angle measurements from a flight are taken and usedas input for the model. The resulting output parameters are inspected and discussed.

III. Results

Now that the method has been explained, the results can be discussed. The results are illustrated indetail using the analysis of a B737-800. The B737-800 is used because it is one of the most used commercialaircraft types. This made it possible to analyze thousands of flights in a matter of hours. The results shownin this section are obtained by measuring all B737-800 flights world wide for almost 16 hours. The dataincludes over 11.000 flights and over 11 million ADS-B measurements. First the results for the operationalflight envelope are presented and secondly the lift and drag coefficients are discussed. Other aircraft types,such as A320, B787-800 and the A380, are also discussed, but the results are shown in a more compactmanner.

III.A. Operational Flight Envelope

The results are observations obtained from the ADS-B measurements. In Table 1 the most importantparameters and phases are shown for the B737-800. This table shows the mean and standard deviation fromthe measurements for important variables and phases. The full results for the operational flight envelopecalculations can be seen in Table 3 in the Appendix. Also the full results for the A320, B787-800 and A380are given in the Appendix.

Table 1. Most important results for B737-800

Parameter Ground Climb Cruise Descent

µTO σTO µIC σIC µCL σCL µCR σCR µID σID µD σD µFA σFA

VTAS 58.66 16.71 95.51 13.92 183.74 33.64 229.1 23.51 222.76 24.15 158.44 33.0 83.68 14.55

VTAS 2.04 0.76 0.39 1.0 0.17 0.49 0.0 0.32 -0.05 0.42 -0.1 0.41 -0.1 0.33

h 0 0 652 288 5034 2110 10781 1007 9162 1324 3872 1712 607 315dhdt

0.0 0.0 10.38 4.35 11.42 4.26 0.01 0.26 -8.66 4.84 -7.12 3.28 -4.11 1.29

M 0.17 0.05 0.28 0.04 0.58 0.12 0.77 0.08 0.74 0.08 0.49 0.11 0.25 0.04

γ 0.0 0.0 0.11 0.05 0.07 0.03 0.0 0.0 -0.04 0.02 -0.04 0.02 -0.05 0.01

φ 0.0 0.0- 0.15 0.68 0.09 0.83 0.03 0.81 0.04 0.76 0.07 0.63 0.07 0.5

E 0.0 0.0 135.53 122.48 139.42 168.24 0.31 80.31 -97.08 164.56 -86.65 176.22 -49.1 45.95

To give some reference the results are compared to the performance figures obtained from the EPD.30 In

10 of 34


Table 10 the performance of a B737-800, obtained from the EPD, can be seen. Similar tables for the A320,B787-800 and A380 are given in the Appendix.

Table 2. Flight performance of B737-800 obtained from Eurocontrol Aircraft Database30

Parameter Take-off Initial Climb Climb Cruise Initial Descent Descent Approach Landing

VTAS(m/s) 75 85 150 237 - 145 128 75

h(m) 0 1525 4570 12500 8000 3000 - 0dhdt

(m/s) 0 15 10 0 -4 -17 -8 0

M - - - 0.79 0.78 - - -

The Lets start by looking at the true air speed, since this will have much influence on the other phases.The average speed during take-off is 58 m/s. This should not be confused with the lift-off speed. Theestimated lift-off speed is 73.14 m/s. According to the EPD30 the lift-off speed for the B737-800 is around75 m/s, which is virtually equal to the estimation. In Figure 5 a comparison is made between EPD and theresults for four aircraft types. The first column shows the take-off phase. Here the average true air speed fortake-off, lift-off and EPD are shown. As expected the average take-off speed is lower than the lift-off speed.The speed given by the EPD is the lift-off speed and is very close to the lift-off speed measured. For allaircraft the lift-off speed given by EPD is within one standard deviation from the mean.

take-o

ff

initialclimb clim

b

cruiseclim

bcru

ise

initialde

scent

desce

nt

finala

pproa

ch

landin

g0

50

100

150

200

250

300

True Air Speed (m/s)

B737-800

take-o

ff

initialclimb clim

b

cruiseclim

bcru

ise

initialde

scent

desce

nt

appro

ach

landin

g0

50

100

150

200

250

300


A320

take-o

ff

initialclimb clim

b

cruiseclim

bcru

ise

initialde

scent

desce

nt

appro

ach

landin

g0

50

100

150

200

250

300


B787-800

DataEPDLift-off

take-o

ff

initialclimb clim

b

cruiseclim

bcru

ise

initialde

scent

desce

nt

appro

ach

landin

g0

50

100

150

200

250

300


A380

Figure 5. Comparison of measured true air speed against Eurocontrol Aircraft Database

Figure 5 shows that the results are similar to the reference data for most phases. For example thecruise speed differs only slightly for all aircraft and the reference data is within one standard deviation fromthe mean. The phases that differ most are the climb, approach and landing. There are multiple causesthat influence the measurement, which might result in a difference. First there are the phase definitions.The phase definitions are comparable, but not exactly the same as the EPD definitions. In this research themeasurements are classified based on lift configuration. This causes the climb phase to start at lower altitudethan in the EPD. Also the final approach phase starts at lower altitude than in the EPD, therefore the aircrafthas decelerated more before landing. The landing phase also shows large difference. This is mainly caused bybad classification. Measurements taken during landing are filtered by determining the moving average. Thiscauses many of the first measurements during landing to be identified as final approach, because previous

11 of 34


measurements taken into account. Only measurements late into the landing are classified as landing, whichleads to a low landing speed. The landing is not used for any further analysis, therefore nothing has beendone to change this behavior. Another interesting point to notice is the true air speed for cruise climb. Thisspeed appears to be larger than the cruise speed. A possible cause could be a climb strategy that someairlines use. When the aircraft is close to cruise altitude the thrust is reduced and the obtained momentumis used to reach final cruise altitude and thus exchanging speed for altitude.

It is also possible to compare the Mach number, which are given in Tables 1 and 10. The measuredaverage Mach number is 0.77. According to Boeing31 the Mach number at zero wind flight and at cost indexzero is 0.773 for a B737-800. Most airlines would fly at cost indexes around 12,32 making the Mach numberof 0.78 given by the EPD plausible.

For both the true air speed as the Mach number, the measured values are close to the reference data.This builds confidence in the method, but also of the assumption that wind will cancel out due to the largenumber of measurements.

B737-80

0A320

B787-80

0A380

0

500

1000

1500

2000

2500

3000

3500

Distance (m)

Take-off distance

DataEPD

B737-80

0A320

B787-80

0A380

0

2000

4000

6000

8000

10000

12000

14000

Altitude (m)

Altitude

Figure 6. Comparison of take-off distance and cruise altitude

The take-off distance is the major parameter on which the take-off weight is based and therefore importantfor many of the calculations performed in this study. In Figure 6 a comparison is made of the measuredtake-off distance and the EPD. For all aircraft types the mean take-off distance is significantly lower thanthe EPD data. Also the standard deviation for these parameters is large. This leads us to believe that themeasurement is noisy. The number of measurements belonging to take-off are small. The temporal resolutionis around 5 seconds, therefore a maximum of only 5 or 6 measurements per flight are taken. This also meansthat when one measurement is missing a large portion of the distance is not taken into account. There aremultiple reasons why a measurement could be missing. First there is the ADS-B coverage. Take-off is on theground and therefore it is more likely that buildings and trees are blocking ADS-B antennas reducing thecoverage of the system. Secondly the classification is difficult, by using a moving average it might happenthat the measurements at the start of take-off are wrongfully classified. At the other end of this spectrum,measurements that actually should be part of the initial climb are assigned to take-off. This could explainthe standard deviation to be so large. A possible solution to this would be to change the filtering method.Also different classification methods could be used to find the best option. For the weight estimation itbecomes clear that not one parameter should be used. Other parameters such as lift-off speed could also beused.

On the right of Figure 6 the cruise altitude is compared with EPD. It should be noted that the altitude

12 of 34


given by the EPD is actually the maximum rated altitude for the corresponding aircraft type. Therefore itis to be expected that the measurements show a lower cruise altitude. Even when one standard deviationis added to the mean the altitude is lower than the EPD. This builds confidence since in reality almostno aircraft would fly close to the rated maximum altitude. The altitude for the individual phases are lessimportant since they are largely defined by the fuzzy logic parameters set in the method.

initia

lclimb

climb

cruise

climb

initia

ldesce

nt

desce

nt

finala

pproa

ch−20

−15

−10

−5

0

5

10

15

20

Rate

of Clim

b (m

/s)

B737-800

initia

lclimb

climb

cruise

climb

initia

ldesce

nt

desce

nt

finala

pproa

ch−20

−15

−10

−5

0

5

10

15

Rate

of Clim

b (m

/s)

A320

initia

lclimb

climb

c uise

climb

initia

ldesce

nt

desce

nt

finala

pp oa

ch−15

−10

−5

0

5

10

15

Rate

of Clim

b (m

/s)

B787-800

DataEPD

initia

lclimb

climb

cruise

climb

initia

ldesce

nt

desce

nt

finala

pproa

ch−15

−10

−5

0

5

10

15

Rate of Clim

b (m/s)

A380

Figure 7. Comparison of measured rate of climb against Eurocontrol Aircraft Performance Database

The climb and descent performance is next to the speed and altitude and important part of the flightenvelope. In Figure 7 the climb and descent performance for different aircraft types is shown and comparedto the EPD. In general the rate of climb during the initial climb is lower. This could be caused by the slightdifferences in phase definition. The values for climb and cruise climb are close and within standard deviation.For the initial descent a difference can be seen by the short range and long range aircraft. It should be notedthat the EPD is for educational purposes and no indication whether the data is a maximum or average isgiven. For the previous parameters it was clear from the values, but for the climb performance this is lessapparent. An interesting difference to note is the envelope of climb and descent. Where the EPD expects adecreasing rate of climb, the measurements show that an aircraft will increase its rate of climb during theclimb phase. A similar difference can be seen during descent. The lack of context on the EPD data makesit difficult to pin point a probable cause. This is also visualized in Figure 8. Here the flight envelope fromthe measured data is plotted against the EPD data.

The other parameters such as VTAS , γ and φ are not given by the EPD. A short inspection of theseparameters is given. The acceleration VTAS is rather low during the flight. This is expected, since highaccelerations degrade the user comfort. A clear positive acceleration can be seen during take-off. Noinformation on γ could be found from another trusted source, but there is one phase where γ is widelyknown. This is the final approach. Here the aircraft follows the Instrument Landing System (ILS) glide-slope which is around -0.0524 rad (-3 degrees). This is very close to the γ found in the measurements. Thisprovides confidence that the method used and the other measurements are correct as well. For the bankangle φ no specific optimal performance parameters were found, only limits were found. From the BoeingTechnical Guide33 it was found that the limit on bank angle is set to ± 0.5 rad. The average of the measuredφ is definitely within the limits, but the standard deviation on the parameter is large. This could be causedby the noise, since φ is based on the time derivative of the heading.

13 of 34


0 50 100 150 200 250 300True air speed (m/s)

−2000

0

2000

4000

6000

8000

10000

12000

14000

Al i ude (m

)

meansigmaEurocon rol Da abase

80 100 120 140 160 180 200 220 240True air speed (m/s)

2

4

6

8

10

12

14

16

Ra e

of cl

imb (m

/s)

60 80 100 120 140 160 180 200 220 240True air speed (m/s)

−18

−16

−14

−12

−10

−8

−6

−4

−2

Ra e

of cl

imb (m

/s)

Figure 8. Flight envelope B737-800

III.B. Lift and Drag Coefficients

In this study multiple methods are proposed to determine the lift- and drag coefficients and the correspondingdrag polar for climbing, cruising and descending phases. The method for the climbing phases turns out tobe very unreliable. This is due to the fact that VTAS is very noisy and sometimes becomes zero. Since themethod divides by VTAS this will mean that CD will become infinite, when VTAS is zero. Efforts to suppressor filter this behavior turned out unsuccessful. Even when a proper mean was found the standard deviationwould be an order too large due to the noise. For on-line analysis this method is too sensitive for noise.But for offline analysis, where filtering options can be more elaborate, this method might work. Even thenthere is no method to determine CD0

and K. The results for the cruise and descending phases are shown inFigure 9.

Looking at CL the pattern makes sense from a flight mechanics perspective. During initial climb CL

is around 1.09 after which it starts to decrease to around 0.4 when the aircraft accelerates and changes itsconfiguration and angle of attack. Due to a positive flight path angle the thrust is aiding in overcoming thegravity. This is what causes the increase in CL during the cruise phase. The mean CL remains similar untilfinal approach is started. Here the configuration is changed to allow flight at lower speeds.

The remaining plots show the distribution of CD, CD0 and K for the cruise, initial descent, descent andfinal approach phase. The first thing that stands out is the CD during cruise. The graph shows a negativeminimum. The reason that this is possible is because CD is determined based on ∆W . Because of thenoise in the data it sometimes happens that ∆W becomes positive, where a decrease in weight is expected.This could cause the CD to be negative. It would be possible to just remove the negative values, but thisis not done for the following reason. The idea behind the method is to use the trend in the ∆W signal todetermine CD. The actual ∆W fluctuates around this trend line. This is not a problem as long the trendin the signal is negative. By removing the negative values from the signal the trend of the signal is altered,which in turn causes a change in the resulted CD. In order to see the implications of that change a test runwas preformed where the negative values where filtered out. The resulting CD turned out to be a factor 7too large. Therefore a choice was made not to remove the negative CD values.

For the descending phases the distributions are reasonably normal distributed. The median (red stripe)is fairly close to the mean (black dot). But for the cruise the distribution is skewed. The median is veryclose to the first quantile. This is probably caused by the previously discussed negative CD.

14 of 34


Initial cl

imb

Climb

Cruise

climb

Cruise

Initial desc

ent

Desce

nt

Final appro

ach−0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

CL

Cruise

Initial desc

ent

Desce

nt

Final appro

ach

−0.1

0.0

0.1

0.2

0.3

CD

Cruise

Initial desc

ent

Desce

nt

Final appro

ach

0.00

0.02

0.04

0.06

0.08

CD

0

Cruise

Initial desc

ent

Desce

nt

Final appro

ach−0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

K

Figure 9. Boxplot for CL and CD

The drag polar of the aircraft is not known. Therefore it is not possible to validate the numbers forCD. In order to give some indication on the quality of the results, they are compared to BADA. Since alsothe BADA drag polar data are proprietary only the difference with BADA is presented. BADA provides 5different drag polars for different flap settings. From the ADS-B data it is not possible to determine what theconfiguration of the aircraft is. However for some phases of flight it is known what configuration the aircraftis in. For example an aircraft in cruise will generally always fly in clean configuration. Also an aircraft infinal approach will have a high lift configuration to make sure the landing speed is low enough. Using thisknowledge it is possible to assign a configuration to a phase. Here the clean configuration is assigned to thecruise, initial descent and descent phases. A high lift configuration is assigned to the final approach.

In Figure 10 the comparison with BADA is shown. For every phase a plot is made where the maximumdifference between the measured drag polar and BADA is plotted. The maximum difference is determined fora predefined operating lift coefficient range of 0.4−0.8 for the clean configuration phases. The lift coefficientrange used for the high lift configuration is 0.8 − 1.8.

Two measured drag polars are compared. The first is the mean drag polar, which is made by usingthe mean µCD0

and µK . Let us refer to this drag polar as Q1. The second drag polar is made by usingµCD0

+ σCD0and µK + σK , this one is called Q2. The comparison is made for four aircraft types. The

results in Figure 10 show that the maximum difference between BADA and the measured drag polars islarger for Q1 and becomes smaller by adding the standard deviation. Only for long range aircraft duringinitial descent and descent this does not hold. There Q1 shows a smaller error from BADA. For the cruisephase two possible explanations could exist. First it is possible that the thrust specific fuel consumptionused was not exactly correct. Secondly it is possible that the assumption, that aircraft flies at a maximumrange strategy, does not hold. This would cause the relation between the zero-lift drag coefficient and theinduced drag coefficient to be different. More likely the error is caused by a combination of assumptions andnoisy data. For the descending phases the maximum difference between Q1 and Q2 is smaller. For the finalapproach, where the aircraft is known to have a high lift configuration the maximum difference becomeslarger again. Again this is probably because the actual flight strategy is different from the one assumed inthe method.

So far we have looked at the maximum difference in CD between Q1 and Q2 for a range of possible CL,but it is more interesting to know the difference at the mean operating CL, since this is where the aircraft is

15 of 34


B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200

Max difference

with BADA (%)

Cruise/ Clean configuration

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200

Max difference with BADA (%)

Initial descent/ Clean configuration

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200


Descent/ Clean configuration

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200


Final approach/ High lift configuration

Q1

Q2

Figure 10. Maximum difference of data with BADA within the operational lift coefficient range

more likely to operate. This is shown in Figure 11. These results amplify the previous results, because thedifference with Q1 and BADA is significantly larger at the mean operating lift coefficient than Q2. Overallthe differences at the mean operating point for Q2 are very low. This is consistently the case for multipleaircraft types. This gives the impression that the method currently underestimates the drag polar.

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200

Diffe

rence

at µ with B

ADA (%

)

Cruise/ Clean configuration

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200

Diffe

rence

at µ with B

ADA (%

)

Initial descent/ Clean configuration

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200

Diffe

rence

at µ w

ith B

ADA (%

)

Descent/ Clean configuration

B737-800 A320 B787-800 A380 A330-200 B757-200 B777-2000

50

100

150

200

Diffe

rence

at µ with B

ADA (%

)

Final approach/ High lift configuration

Q1

Q2

Figure 11. Difference of data with BADA at the operational lift coefficient

16 of 34


The previous figures all show the absolute difference with BADA for specific aircraft types. Next wecombine differences for all the aircraft types an look at the resulting statistics. In Figure 12 statistics on thedifference with BADA for different phases are shown. Again it can be seen that the overall difference for Q1

is larger in the cruise phase. Q2 is even very close to zero, but a slight increase in spread can be seen. Thisincrease in spread is much larger for the initial descent and descent phases. Also the overall difference islarger for Q2 is those phases and interesting to see is the switch from a negative error to a positive error. Forthe final approach phase, the spread is smaller again. As was seen before the difference with BADA for Q1

is larger than Q2. Figure 12 clearly shows that the method used in this study estimates a lower drag thanBADA would in all phases. By adding one standard deviation a result more similar to BADA is obtained.But it should be noted that BADA is not equal to the actual drag polar as well.

Cruise Initial descent Descent Final approach−200

−150

−100

−50

0

50

100

150

200

Difference ith BADA (%)

Statistics for combined aircraft types

Q1

Q2

BADA

Figure 12. Error statistics of the comparison with BADA.

III.C. Simulation

So far the operational flight envelope is estimated and an estimation for the drag polar is obtained. Tosee how these newly found figures hold up during simulation a simple point mass model was made and asimulation was run.

The simulation is set up to recreate a measured flight. To achieve this ADS-B measurements are usedas input to the model. The ground speed, rate of climb, flight path angle and corresponding aircraft typeparameters are fed into the simulation. The resulting output includes thrust, drag, lift and weight forces.The non-symmetric parameters are obtained as well, but are not discussed here.

Two simulations are performed. The first simulation S1 uses the drag polar Q1 and the mean µCL. The

second simulation S2 is done using drag polar Q2 and µCL+σCL

. This extra comparison is done to rule outthe possibility that the BADA drag polar data is not of high quality either.

The input data is obtained by measuring flight VA605 from Mackay Airport to Brisbane Airport onSaturday January 9. The first measurement was taken at 00:22:21 universal time and the flight took around1.5 hours. The data is shown in Figure 13.

From Figure 13 it can be seen that the simulation correctly follows the input data. The altitude is notpart of the input parameters, but the simulation is able to closely follow the measured altitude. Thereis some difference in the cruise altitude, which could be caused by the simple Euler method used for thenumerical integration. The altitude is determined by numerical integration of the rate of climb. A different

17 of 34


0 1000 2000 3000 4000 5000 6000Time (s)

−2000

0

2000

4000

6000

8000

10000

12000

m

Altitude

0 1000 2000 3000 4000 5000 6000Time (s)

0

50

100

150

200

250

m/s

Speed

0 1000 2000 3000 4000 5000 6000Time (s)

−20

−15

−10

−5

0

5

10

15

20

m/s

Rate f climb

0 1000 2000 3000 4000 5000 6000Time (s)

−0.10

−0.05

0.00

0.05

0.10

0.15

0.20

rad

Flight path angle

Simulati nSimulati n + σMeasurement

Figure 13. Simulation using measurement as input

method might make the error smaller. It is also possible that the difference exists because of noise in theinput data.

0 1000 2000 3000 4000 5000 6000Time (s)

610000

615000

620000

625000

630000

635000

640000

645000

650000

N

Weight

0 1000 2000 3000 4000 5000 6000Time (s)

0

200000

400000

600000

800000

1000000

1200000

1400000

N

Lift

0 1000 2000 3000 4000 5000 6000Time (s)

0.0

0.5

1.0

1.5

2.0

2.5

(-)

Lift Coefficient

0 1000 2000 3000 4000 5000 6000Time (s)

0.0

0.5

1.0

1.5

2.0

2.5

(-)

Lift over weight ratio

SimulationSimulation + σ


One of the outputs of the simulation are the vertical forces. In Figure 14 four graphs are shown with theweight, lift, lift coefficient and the lift over weight ratio. The weight shows a large difference between thetwo different simulations. The increased drag causes the aircraft to use more fuel. The difference in fuel useis significant. This shows the importance of using the correct drag polar, because the difference in fuel useis large in only a short period of time.

More interesting to see in Figure 14 is the lift over weight ratio or load factor. It is understandable

18 of 34


that the simulation is less accurate during the climbing and descending phases. Because it assumes steadyflight, which is not necessarily the case. But what is interesting to see is the load factor during cruise. Theassumption that the flight is steady during cruise is easily shown by the altitude and speed profile of theflight. Both are more or less constant during cruise. Therefore it is surprising to see that for S1 the loadfactor is below one. This would mean the weight and lift are not in balance and some sort of accelerationwould exist. The load factor for S2 is much closer to one, but still slightly under.

1000 1500 2000 2500 3000 3500Time (s)

0

100000

200000

300000

400000

500000

600000

700000

N

Weight

S1

−15% Weight+15% CL

+15% VTAS1000 1500 2000 2500 3000 3500

Time (s)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

(-)

Lift Coefficient

µCL,B738

±σCL,B738

1000 1500 2000 2500 3000 3500Time (s)

0

50

100

150

200

250

(m/s)

True air speed

µVTAS,B738

±σVTAS,B7381000 1500 2000 2500 3000 3500

Time (s)

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

(-)

Lift over weight ratio

Figure 15. Sensitivity analysis of the lift-over-weight error

In Figure 15 an analysis is performed to expose the largest influences of this error. The three parameterson which the load factor depends are W , VTAS and CL. To determine the sensitivity of the load factor tochanges in these parameters, all parameters are changed by 15% and plotted. From Figure 15 it can beseen that the load factor is most sensitive to changes in VTAS . This is understandable because this relationis quadratic, where the relation of load factor with W and CL is linear. Let us first cosider the true airspeed used in the simulation. This is shown in the bottom left graph of Figure 15. In the figure the usedcruise speeds are shown also the mean and standard deviation for cruise speeds of a B737-800 are shown.It can be seen that the speed in S1 is approximately one σ smaller than the mean. This could actually bethe case, but another possibility is the presence of wind. It could very well be that the aircraft encountereda head wind of around 30 m/s leading to a ground speed of approximately 200 m/s. This lower speedsubsequently influences the lift calculation and thus the load factor. By adding 15% to the true air speed,which is approximately 30 m/s, the load factor becomes much closer to one. The remaining error is likelyto be caused by both errors in W and CL.

In Figure 16 the horizontal forces are shown. By increasing the drag polar also the drag and thereforethe thrust need to be increased. This can be clearly seen. The question is which is a better estimation ofthe drag. Since the difference between drag and thrust during cruise is almost zero it is easier to look at thethrust values. Assuming a CFM56-7B24 engine is mounted the maximum cruise thrust per engine is 5,480lbf (24376N) according to Jet-Engine.com.27 This value seems plausible when compared to the values ofolder models, such as CFM56-3 engines which were used for the older B737 series. The values for the oldermodels are published on the engine manufacturers website.34

Now comparing the simulated thrust to these values it is clear the S1 shows very low thrust values andthat S2 is much closer to the cruise thrust given by the manufacturer.

This simulation shows similar findings as the comparison with BADA. Simulation S2 shows more realisticvalues, which is caused by better aerodynamic coefficients.

19 of 34


0 1000 2000 3000 4000 5000 6000Time (s)

0

50000

100000

150000

200000

250000N

Thrust

0 1000 2000 3000 4000 5000 6000Time (s)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

(-)

Drag Coefficient

0 1000 2000 3000 4000 5000 6000Time (s)

0

20000

40000

60000

80000

100000

120000

N

Drag

0 1000 2000 3000 4000 5000 6000Time (s)

−100000

−50000

0

50000

100000

150000

200000

250000

N

Net Thrust

SimulationSimulation + σ


IV. Discussion

So far a method and results are shown for this research. The goal was to find the operational flightenvelope and to estimate lift and drag coefficients using ADS-B data. The use of global ADS-B data offersa lot of information and so far little research has been performed using this information.

A straightforward method is proposed to estimate the operational envelope, by simply taking measure-ments. But there are a few drawbacks to this method. The first drawback is that ADS-B data only providesground speed. To determine the true air speed it is assumed that on average the effects of wind speed anddirection cancel out. For the VTAS was shown that this assumption gives good results when compared to theEurocontrol Aircraft Performance Database. Although the standard deviation could be reduced by takingwind estimations into account. In some occasions supersonic ground speeds were measured, because theaircraft had a large tail wind. This is beyond the maximum speed of a B737-800. Implementation of windspeed and direction is possible by using the GFS predictions.

In order to determine the weight the take-off distance was determined and used to estimate the payloadweight. The estimated take-off distance was lower than the EPD for all aircraft types. Also the standarddeviation was large. This is probably caused by miss-classified measurements, noisy data and low resolutionof the measurements. It was shown that it is possible to isolate ADS-B measurements that belong to the take-off phase based on acceleration. Because of the noise in the data is was necessary to filter the acceleration,which was done by using a moving average. This filtering method did not have a perfect score on phaseclassification. Also the low temporal resolution made the influence of missing measurement large.

This result for the take-off distance shows that using only the take-off distance for weight estimation isnot enough. This method is used for its simplicity. It was never the goal of this research was to develop afull working method for take-off weight estimation. By only determining the payload weight using take-offdistance the influence on the weight was limited. Many other variables are in play during take-off, such asthrust settings, wind and flap settings. A combination of these parameters will be necessary to develop abetter weight estimation method.

The rate of climb was compared to EPD, which in values were pretty close. The major difference wasthe envelope. The EPD expected the rate of climb to decrease with increasing altitude. This was not thecase for most aircraft. Only for the A380 this pattern was similar. The missing context on these EPD valuesmakes it difficult to state a cause for the differences.

Next a method was given to determine the lift- and drag coefficients. This method implements a combi-nation of ADS-B measurements, ADS-B derivatives and assumptions. In order to determine the CD during

20 of 34


climb the change in excess thrust was used. Even though the method seemed plausible in preliminary work,the method did not work well in practice. The noise in the acceleration signal is too large. The method isbetter suited for offline analysis, where it is possible to use different filtering techniques.

The CD and the corresponding drag polar that was found for the other phases looks much more promising.By comparing the results for multiple aircraft with BADA it can be seen that the method underestimatesthe drag. The comparison with BADA is important to get a sense of the quality of the estimation, butshould not be seen as absolute reference. Since the drag polars from BADA are not the actual drag polars

To extend this sense of quality, the drag polars were tested using a simple simulation. The results ofthe simulation were similar as the comparison with BADA. The thrust in cruise using the found drag polaris much lower than values expected by manufacturers. Since the drag and thrust can be assumed to be inequilibrium during cruise, this leads to the conclusion that the drag is too low.

Another interesting results of the simulation was the low load factor during cruise. To find the origin ofthis low load factor, the sensitivity of the load factor to the three most important parameters was checked. Itwas found that errors in true air speed have the largest effect on the vertical force balance when recreating aflight from ADS-B data. The flight used had a cruise speed roughly 15% lower than the average determinedearlier. Therefore the low true air speed was given as the main cause for the low load factor.

The most important finding is the underestimation of the drag polar. It is difficult to pin point thecause of this underestimation, since many assumptions are made in this method. Obtaining aerodynamiccoefficients from ADS-B data has always been a long shot. In the end it was shown possible, with reasonableresults. Determining the cause of the underestimation would greatly improve the method. To find the cause,potential causes should be eliminated systematically.

V. Conclusion

In order to build an open source APM, an effort was made to develop a method that could extract theoperational flight envelope and possibly the lift- and drag coefficients from the large amount of samples avail-able through ADS-B. For this purpose a tool was developed that can process aircraft ADS-B measurementsin an on-line fashion. The method combines ADS-B data and makes assumptions on wind and flight strategyto determine the operation flight envelope. Also lift- and drag coefficients are determined.

Overall the tool can be used to find the operational flight envelope. In comparison with other open datathe operational flight envelope results seem good. The assumption that the wind speed and direction canceldid not give any big problems. The true air speed compared well with the Eurocontrol aircraft database.Also other parameters such as rate of climb and altitude were as expected.

Next the lift- and drag coefficients were determined and compared with the BADA APM. The methodunderestimated the drag coefficients, where maximum differences of up to 200% were seen. The values atone standard deviation performed much better, with maximum differences for clean configuration as low as10%. The drag polar was put to the test in a simple simulation. The results from the simulation and thecomparison to BADA showed that the tool underestimates the drag polar.

VI. Recommendations

This research is a good start for exploiting the large amounts of ADS-B data available. The data stillhouses many more secrets that can be unveiled. This research maintained a more global approach with simpleideas and assumptions. This gives an inviting overview and insight into the problems and possibilities. Thenext step in this research would be to step away from the whole picture and to focus on small parts ofthe flight envelope. The most important parameter would be the weight estimation. The weight is crucialfor many calculations and therefore should be properly estimated. Using only the take-off distance is notenough. Other parameters such as lift-off speed, rotation rate, lift-off acceleration, lift-off turn radius andlift-off flight path angle could be used to get a better estimation. The best approach here would be to getsome reference data and use semi-supervised learning techniques. But when this is not available it mightbe possible to find patterns in the data by simply clustering the data. For example an aircraft with a shorttake-off distance a steep flight path angle and a high acceleration might be seen as a light aircraft. Anotherpart of the weight estimation that could be improved is the fuel weight estimation. Airlines use standardtables and formulas to determine the fuel based on a few parameters such as weight and distance.

Another flaw in the current tool is the absence of wind. True air speed is one of the most important

21 of 34


parameters in this analysis ans was shown in the simulation. Therefore it is important to have the bestpossible estimation. It is possible to implement wind estimations by using the GFS. The GFS providesatmospheric predictions every 6 hours at a resolution of 0.25 degrees. Using these predictions a betterestimation of the true air speed can be made.

It is recommended to improve the phase classification methods. The fuzzy logic used in this studyworks very good for the larger phases. But for the short duration phases, where only a few measurementare available it is less good. Being able to classify individual measurements with great confidence is veryvaluable. Because many of the hidden knowledge is only revealed by rare occasions and few measurements.To be able to extract that knowledge you should be able to trust your methods. This also applies to filtering.In this research a simple moving average was used, but this is not good enough. Other on-line and off-linemethods should be analyzed.

In the end the problem at hand is multi-parameter optimisation problem, with known constraints onmany parameters. In this research no explicit use is made of these constraints. So maybe it is possible to useglobal optimisation strategies combined with simple models to minimize some specific cost function. Doingthis for the whole flight envelope is difficult, but doing it in small steps could be a good approach. Forexample modeling the take-off and subsequently fitting the model to the data, might lead to better weightestimations. During take-off clear constraints exist on weight, lift coefficient, thrust and drag. Also weatherdata is widely available.

This research presented an overview and a guide. But subsequent research should be in-depth andfocussed.

22 of 34


Appendix

Full results of multiple aircraft types

23 of 34


Table

3.

Result

statis

tic

sof

B737-8

00

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φdE

take-

off

µ58.6

62.0

40.0

0.0

--

--

0.1

70.0

-0

σ16.7

10.7

60.1

10.0

8-

--

-0.0

50.0

-0

max

108.7

94.3

20.3

20.2

4-

--

-0.3

20.0

-0

min

8.5

2-0

.25

-0.3

2-0

.23

--

--

0.0

3-0

.0-

0

init

ialc

lim

b

µ95.5

10.3

9651.5

810.3

81.0

9-

--

0.2

80.1

10.1

5135.5

3

σ13.9

21.0

288.0

94.3

50.3

--

-0.0

40.0

50.6

8122.4

8

max

137.2

63.3

71515.8

623.4

21.9

8-

--

0.4

10.2

72.2

502.9

7

min

53.7

7-2

.6-2

12.7

-2.6

60.2

--

-0.1

6-0

.05

-1.9

-231.9

clim

b

µ183.7

40.1

75034.4

311.4

20.4

7-

--

0.5

80.0

70.0

9139.4

2

σ33.6

40.4

92109.8

4.2

60.1

4-

--

0.1

20.0

30.8

3168.2

4

max

284.6

61.6

411363.8

224.2

0.8

8-

--

0.9

30.1

62.5

7644.1

5

min

82.8

2-1

.3-1

294.9

6-1

.36

0.0

6-

--

0.2

2-0

.03

-2.3

9-3

65.3

2

cru

ise

µ229.1

0.0

10780.7

50.0

10.5

60.0

65

0.0

10.0

10.7

70.0

0.0

30.3

1

σ23.5

10.3

21006.9

20.2

60.1

50.0

12

0.0

10.0

40.0

80.0

0.8

180.3

1

max

299.6

40.9

713801.5

20.8

1.0

0.1

20.0

40.1

11.0

10.0

2.4

7241.2

3

min

158.5

7-0

.97

7759.9

8-0

.78

0.1

20

-0.0

3-0

.10.5

3-0

.0-2

.41

-240.6

1

init

iald

esce

nt

µ222.7

6-0

.05

9161.7

9-8

.66

0.5

0.0

20.0

10.0

40.7

4-0

.04

0.0

4-9

7.0

8

σ24.1

50.4

21324.0

74.8

40.1

30.0

10.0

10.0

30.0

80.0

20.7

6164.5

6

max

295.2

31.2

113133.9

95.8

60.9

0.0

50.0

30.1

40.9

90.0

22.3

3396.5

9

min

150.3

-1.3

15189.5

9-2

3.1

80.1

-0.0

1-0

.01

-0.0

50.4

8-0

.1-2

.25

-590.7

5

des

cent

µ158.4

4-0

.13872.3

2-7

.12

0.5

80.0

30.0

10.0

50.4

9-0

.04

0.0

7-8

6.6

5

σ33.0

0.4

11711.5

43.2

80.2

30.0

20.0

10.0

30.1

10.0

20.6

3176.2

2

max

257.4

51.1

49006.9

42.7

31.2

80.0

70.0

40.1

30.8

20.0

11.9

7441.9

9

min

59.4

4-1

.34

-1262.3

1-1

6.9

6-0

.13

-0.0

2-0

.01

-0.0

30.1

6-0

.1-1

.83

-615.3

fin

ala

pp

roach

µ83.6

8-0

.1607.0

-4.1

11.4

70.0

80.0

40.0

20.2

5-0

.05

0.0

7-4

9.1

σ14.5

50.3

3315.2

51.2

90.5

50.0

40.0

20.0

30.0

40.0

10.5

45.9

5

max

127.3

20.8

81552.7

4-0

.25

3.1

30.1

80.0

90.1

20.3

8-0

.01

1.5

688.7

4

min

40.0

4-1

.08

-338.7

4-7

.97

-0.1

8-0

.03

-0.0

2-0

.08

0.1

2-0

.09

-1.4

3-1

86.9

3

24 of 34


Table

4.

Result

statis

tic

sof

A320

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φE

take-

off

µ56.3

47

2.1

25

0.0

42

-0.0

06

--

--

0.1

66

-0.0

-0

σ17.1

81

0.8

27

0.5

66

0.2

74

--

--

0.0

50.0

04

-0

max

90.7

09

3.7

78

1.1

75

0.5

43

--

--

0.2

67

0.0

08

-0

min

21.9

86

0.4

71

-1.0

9-0

.555

--

--

0.0

65

-0.0

08

-0

init

ialc

lim

b

µ98.6

21

0.3

86

826.6

28

9.5

68

1.0

11

--

-0.2

93

0.1

02

0.1

29

131.4

25

σ20.2

12

0.7

82

354.8

14.5

52

0.3

77

--

-0.0

61

0.0

54

0.5

84

108.2

13

max

139.0

45

1.9

51536.2

48

18.6

73

1.7

65

--

-0.4

14

0.2

11.2

96

347.8

52

min

58.1

97

-1.1

78

117.0

09

0.4

64

0.2

56

--

-0.1

71

-0.0

06

-1.0

39

-85.0

01

clim

b

µ190.0

14

0.1

21

5332.5

99.5

29

0.4

08

--

-0.5

98

0.0

54

0.0

54

111.7

73

σ31.5

95

0.7

89

1968.7

74.1

15

0.1

14

--

-0.1

10.0

30.4

73

205.4

32

max

253.2

03

1.7

9270.1

29

17.7

60.6

37

--

-0.8

18

0.1

14

1.0

522.6

37

min

126.8

25

-1.4

57

1395.0

51

1.2

98

0.1

79

--

-0.3

78

-0.0

07

-0.8

93

-299.0

92

cru

ise

µ228.0

58

0.0

14

10391.4

86

0.0

22

0.4

66

0.0

53

0.0

11

0.0

16

0.7

65

0.0

0.0

33.5

33

σ26.8

79

0.9

46

915.1

26

0.3

31

0.1

21.3

53

0.0

13

0.0

21

0.0

91

0.0

01

0.6

1243.6

06

max

281.8

16

1.9

07

12221.7

38

0.6

84

0.7

05

2.7

58

0.0

37

0.0

58

0.9

47

0.0

03

1.2

5490.7

45

min

174.3

01

-1.8

78

8561.2

34

-0.6

40.2

27

-2.6

52

-0.0

15

-0.0

26

0.5

83

-0.0

03

-1.1

9-4

83.6

79

init

iald

esce

nt

µ227.4

48

-0.0

44

8921.8

88

-8.7

59

0.3

78

0.0

15

0.0

07

0.0

56

0.7

48

-0.0

39

0.0

32

-96.1

74

σ26.3

18

0.8

98

1212.1

94.6

11

0.1

01

0.0

08

0.0

04

0.0

33

0.0

90.0

20.4

42

260.8

75

max

280.0

83

1.7

52

11346.2

68

0.4

63

0.5

79

0.0

31

0.0

15

0.1

22

0.9

28

0.0

02

0.9

16

425.5

76

min

174.8

12

-1.8

46497.5

08

-17.9

80.1

76

-0.0

02

-0.0

01

-0.0

10.5

69

-0.0

79

-0.8

52

-617.9

24

des

cent

µ161.2

42

-0.1

13861.5

64

-7.2

85

0.4

43

0.0

20.0

10.0

58

0.4

98

-0.0

45

0.0

52

-90.5

75

σ34.3

41

0.8

03

1684.8

83.5

82

0.1

65

0.0

12

0.0

06

0.0

33

0.1

14

0.0

19

0.5

73

207.5

14

max

229.9

24

1.4

95

7231.3

24

-0.1

20.7

73

0.0

43

0.0

22

0.1

24

0.7

25

-0.0

07

1.1

98

324.4

52

min

92.5

6-1

.716

491.8

03

-14.4

49

0.1

14

-0.0

03

-0.0

02

-0.0

07

0.2

7-0

.083

-1.0

94

-505.6

02

fin

ala

pp

roach

µ82.2

39

-0.1

07

596.9

96

-3.9

06

1.2

28

0.0

61

0.0

31

0.0

22

0.2

43

-0.0

49

0.0

51

-47.5

65

σ17.7

38

0.4

65

313.2

07

1.3

61

0.4

34

0.0

29

0.0

15

0.0

11

0.0

53

0.0

15

0.3

35

53.7

32

max

117.7

15

0.8

23

1223.4

11

-1.1

84

2.0

96

0.1

20.0

60.0

45

0.3

5-0

.019

0.7

21

59.8

99

min

46.7

62

-1.0

37

-29.4

19

-6.6

28

0.3

59

0.0

03

0.0

01

-0.0

0.1

37

-0.0

78

-0.6

19

-155.0

28

25 of 34


Table

5.

Result

statis

tic

sof

B787-8

00

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φE

take-

off

µ49.9

21.9

70.2

71

0.0

39

--

--

0.1

47

0.0

-0

σ17.1

03

0.7

33

1.3

92

0.3

8-

--

-0.0

50.0

05

-0

max

84.1

27

3.4

37

3.0

54

0.7

99

--

--

0.2

47

0.0

11

-0

min

15.7

14

0.5

04

-2.5

12

-0.7

21

--

--

0.0

46

-0.0

1-

0

init

ialc

lim

b

µ99.8

27

0.3

33

802.4

24

9.7

71.0

95

--

-0.2

96

0.1

03

0.1

16

127.0

94

σ18.7

96

0.5

12

382.6

32

3.6

50.3

46

--

-0.0

56

0.0

47

0.4

745.2

63

max

137.4

19

1.3

57

1567.6

88

17.0

71

1.7

87

--

-0.4

09

0.1

96

1.0

56

217.6

2

min

62.2

34

-0.6

91

37.1

61

2.4

70.4

03

--

-0.1

83

0.0

1-0

.824

36.5

67

clim

b

µ187.9

63

0.1

61

4996.0

02

10.7

31

0.4

6-

--

0.5

89

0.0

61

0.0

7130.1

86

σ34.7

43

0.2

41970.9

96

3.8

96

0.1

49

--

-0.1

19

0.0

29

0.4

953.4

19

max

257.4

50.6

42

8937.9

93

18.5

22

0.7

59

--

-0.8

28

0.1

18

1.0

49

237.0

24

min

118.4

76

-0.3

19

1054.0

12.9

40.1

61

--

-0.3

50.0

03

-0.9

123.3

47

cru

ise

µ247.0

41

0.0

01

11782.1

89

-0.1

18

0.5

10.0

55

0.0

12

0.0

14

0.8

36

-0.0

0.0

25

0.2

3

σ31.0

56

0.1

59

730.9

11

0.2

47

0.2

04

1.1

78

0.0

13

0.0

16

0.1

05

0.0

01

0.6

05

40.0

12

max

309.1

53

0.3

18

13244.0

11

0.3

75

0.9

17

2.4

10.0

38

0.0

46

1.0

47

0.0

02

1.2

35

80.2

54

min

184.9

3-0

.317

10320.3

67

-0.6

11

0.1

03

-2.3

01

-0.0

13

-0.0

18

0.6

25

-0.0

03

-1.1

85

-79.7

94

init

iald

esce

nt

µ240.5

87

-0.0

61

9638.9

18

-9.1

32

0.3

69

0.0

14

0.0

07

0.0

57

0.7

98

-0.0

39

0.0

34

-103.0

82

σ36.6

50.2

55

1614.2

53

4.0

66

0.1

58

0.0

11

0.0

06

0.0

34

0.1

25

0.0

18

0.4

85

108.6

86

max

313.8

88

0.4

48

12867.4

24

-0.9

99

0.6

85

0.0

37

0.0

18

0.1

24

1.0

49

-0.0

03

1.0

04

114.2

9

min

167.2

87

-0.5

76410.4

12

-17.2

64

0.0

53

-0.0

08

-0.0

04

-0.0

11

0.5

47

-0.0

74

-0.9

37

-320.4

53

des

cent

µ157.4

77

-0.1

03

3770.9

98

-6.7

13

0.4

55

0.0

20.0

10.0

55

0.4

86

-0.0

43

0.0

57

-80.0

35

σ35.5

20.2

27

1658.8

86

3.3

66

0.1

71

0.0

11

0.0

06

0.0

33

0.1

17

0.0

18

0.5

58

66.9

45

max

228.5

18

0.3

57088.7

71

0.0

19

0.7

98

0.0

42

0.0

21

0.1

21

0.7

2-0

.006

1.1

74

53.8

56

min

86.4

37

-0.5

56

453.2

25

-13.4

46

0.1

13

-0.0

02

-0.0

01

-0.0

10.2

52

-0.0

79

-1.0

6-2

13.9

25

fin

ala

pp

roach

µ82.3

25

-0.0

98

602.5

14

-4.1

07

1.2

46

0.0

65

0.0

32

0.0

22

0.2

44

-0.0

51

0.0

39

-47.0

38

σ14.4

13

0.2

31

323.8

03

1.2

01

0.4

03

0.0

29

0.0

15

0.0

09

0.0

43

0.0

14

0.1

82

28.3

44

max

111.1

50.3

65

1250.1

21

-1.7

05

2.0

53

0.1

23

0.0

62

0.0

40.3

3-0

.024

0.4

03

9.6

49

min

53.4

99

-0.5

6-4

5.0

92

-6.5

08

0.4

40.0

06

0.0

03

0.0

04

0.1

57

-0.0

78

-0.3

26

-103.7

26

26 of 34


Table

6.

Result

statis

tic

sof

A380

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φE

take-

off

µ55.3

67

1.5

22

0.1

32

0.2

01

--

--

0.1

63

0.0

03

-0

σ18.6

30.6

48

0.9

82

0.3

98

--

--

0.0

55

0.0

05

-0

max

92.6

26

2.8

18

2.0

97

0.9

96

--

--

0.2

72

0.0

14

-0

min

18.1

08

0.2

25

-1.8

32

-0.5

94

--

--

0.0

53

-0.0

08

-0

init

ialc

lim

b

µ101.8

31

0.3

03

682.3

36

7.4

74

1.1

76

--

-0.3

02

0.0

75

0.1

33

104.8

69

σ20.2

31

0.4

53

376.3

13.3

60.4

09

--

-0.0

61

0.0

31

0.8

37

40.6

75

max

142.2

92

1.2

09

1434.9

56

14.1

94

1.9

94

--

-0.4

23

0.1

37

1.8

07

186.2

18

min

61.3

7-0

.603

-70.2

84

0.7

53

0.3

58

--

-0.1

80.0

12

-1.5

423.5

2

clim

b

µ197.9

55

0.1

39

4948.0

45

8.5

11

0.4

74

--

-0.6

20.0

46

0.0

59

110.8

03

σ32.6

04

0.2

47

1971.2

46

3.5

06

0.1

17

--

-0.1

14

0.0

24

0.6

22

51.9

2

max

263.1

63

0.6

34

8890.5

38

15.5

23

0.7

07

--

-0.8

47

0.0

94

1.3

02

214.6

42

min

132.7

47

-0.3

55

1005.5

52

1.4

99

0.2

4-

--

0.3

93

-0.0

03

-1.1

85

6.9

64

cru

ise

µ252.0

15

0.0

01

11513.1

64

0.0

08

0.5

86

0.0

21

0.0

06

0.0

07

0.8

53

0.0

0.0

23

0.3

94

σ23.7

50.2

74

698.2

03

0.2

59

0.1

53

1.4

06

0.0

08

0.0

11

0.0

81

0.0

01

0.4

62

54.6

41

max

299.5

15

0.5

49

12909.5

69

0.5

26

0.8

92

2.8

32

0.0

21

0.0

29

1.0

14

0.0

02

0.9

47

109.6

76

min

204.5

14

-0.5

46

10116.7

59

-0.5

10.2

81

-2.7

91

-0.0

1-0

.016

0.6

91

-0.0

02

-0.9

02

-108.8

88

init

iald

esce

nt

µ240.4

94

-0.0

58

9712.8

11

-8.0

76

0.4

02

0.0

13

0.0

07

0.0

49

0.7

99

-0.0

34

0.0

26

-93.9

83

σ30.5

49

0.3

61

1542.3

92

4.3

46

0.1

34

0.0

08

0.0

04

0.0

43

0.1

08

0.0

18

0.2

32

88.4

64

max

301.5

93

0.6

64

12797.5

96

0.6

16

0.6

70.0

30.0

15

0.1

35

1.0

14

0.0

02

0.4

91

82.9

44

min

179.3

95

-0.7

81

6628.0

27

-16.7

69

0.1

35

-0.0

03

-0.0

02

-0.0

36

0.5

84

-0.0

7-0

.439

-270.9

11

des

cent

µ155.8

49

-0.0

92

3839.2

85

-6.2

40.4

68

0.0

20.0

10.0

53

0.4

81

-0.0

40.0

79

-76.0

89

σ35.0

83

0.1

98

1675.7

75

2.9

78

0.1

70.0

11

0.0

06

0.0

35

0.1

16

0.0

17

0.6

55

47.4

36

max

226.0

16

0.3

05

7190.8

36

-0.2

85

0.8

07

0.0

43

0.0

21

0.1

23

0.7

13

-0.0

06

1.3

88

18.7

83

min

85.6

82

-0.4

88

487.7

35

-12.1

96

0.1

28

-0.0

03

-0.0

02

-0.0

16

0.2

49

-0.0

74

-1.2

3-1

70.9

62

fin

ala

pp

roach

µ82.3

62

-0.1

07

592.3

5-3

.854

1.2

01

0.0

60.0

30.0

24

0.2

44

-0.0

48

0.0

41

-47.4

09

σ15.5

04

0.2

4320.1

12

1.1

11

0.4

40.0

30.0

15

0.0

17

0.0

47

0.0

13

0.3

61

29.1

81

max

113.3

70.3

73

1232.5

73

-1.6

32

2.0

82

0.1

19

0.0

60.0

57

0.3

37

-0.0

22

0.7

63

10.9

54

min

51.3

55

-0.5

86

-47.8

74

-6.0

76

0.3

21

-0.0

-0.0

-0.0

10.1

51

-0.0

74

-0.6

81

-105.7

72

27 of 34


Table

7.

Result

statis

tic

sof

A330-2

00

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φE

take-

off

µ57.1

22

1.9

82

0.0

0.0

11

--

--

0.1

68

0.0

-0

σ17.2

52

0.7

53

0.0

0.1

45

--

--

0.0

51

0.0

02

-0

max

91.6

27

3.4

88

0.0

0.3

01

--

--

0.2

69

0.0

04

-0

min

22.6

17

0.4

75

0.0

-0.2

79

--

--

0.0

66

-0.0

04

-0

init

ialc

lim

b

µ100.2

57

0.3

65

801.6

86

8.8

52

1.0

25

--

-0.2

97

0.0

93

0.1

19

120.6

11

σ20.4

46

0.7

84

356.8

04

4.4

97

0.3

76

--

-0.0

61

0.0

54

0.5

67

83.4

91

max

141.1

48

1.9

33

1515.2

94

17.8

45

1.7

77

--

-0.4

20.2

1.2

52

287.5

94

min

59.3

65

-1.2

03

88.0

78

-0.1

42

0.2

73

--

-0.1

75

-0.0

14

-1.0

15

-46.3

72

clim

b

µ188.9

77

0.1

34

5247.4

39

10.0

87

0.4

42

--

-0.5

94

0.0

57

0.0

64

121.6

84

σ31.8

70.3

59

2017.9

44.0

29

0.1

19

--

-0.1

11

0.0

29

0.6

85

93.0

54

max

252.7

16

0.8

52

9283.3

18

18.1

45

0.6

8-

--

0.8

17

0.1

15

1.4

34

307.7

92

min

125.2

38

-0.5

83

1211.5

59

2.0

30.2

04

--

-0.3

71

-0.0

02

-1.3

06

-64.4

23

cru

ise

µ238.8

68

0.0

01

11589.5

81

0.0

13

0.5

38

0.0

38

0.0

08

0.0

13

0.8

08

0.0

0.0

24

0.2

01

σ23.8

64

0.1

85

750.3

42

0.2

49

0.1

55

1.6

27

0.0

11

0.1

02

0.0

81

0.0

01

0.5

94

51.9

43

max

286.5

97

0.3

71

13090.2

65

0.5

11

0.8

47

3.2

92

0.0

30.2

18

0.9

70.0

02

1.2

11

104.0

87

min

191.1

39

-0.3

710088.8

97

-0.4

86

0.2

28

-3.2

15

-0.0

14

-0.1

92

0.6

46

-0.0

02

-1.1

64

-103.6

85

init

iald

esce

nt

µ231.8

15

-0.0

59612.4

85

-8.5

46

0.3

47

0.0

13

0.0

06

0.0

83

0.7

69

-0.0

37

0.0

23

-96.4

14

σ25.6

28

0.2

36

1520.8

18

4.0

29

0.1

41

0.0

08

0.0

04

0.1

65

0.0

91

0.0

18

0.2

8111.4

6

max

283.0

71

0.4

22

12654.1

2-0

.489

0.6

29

0.0

28

0.0

14

0.4

12

0.9

5-0

.002

0.5

82

126.5

05

min

180.5

58

-0.5

22

6570.8

5-1

6.6

03

0.0

66

-0.0

03

-0.0

01

-0.2

47

0.5

88

-0.0

73

-0.5

36

-319.3

34

des

cent

µ161.8

17

-0.0

93

3856.2

98

-7.1

39

0.3

78

0.0

17

0.0

08

0.0

89

0.5

-0.0

44

0.0

45

-85.1

68

σ32.7

08

0.2

08

1697.3

17

3.3

44

0.1

70.0

11

0.0

05

0.1

52

0.1

09

0.0

18

0.3

27

84.9

57

max

227.2

33

0.3

23

7250.9

33

-0.4

52

0.7

17

0.0

38

0.0

19

0.3

94

0.7

18

-0.0

07

0.7

84.7

46

min

96.4

02

-0.5

09

461.6

64

-13.8

27

0.0

38

-0.0

05

-0.0

02

-0.2

15

0.2

81

-0.0

81

-0.6

09

-255.0

83

fin

ala

pp

roach

µ82.5

15

-0.1

2578.6

21

-3.9

35

1.0

30.0

52

0.0

26

0.0

34

0.2

44

-0.0

49

0.0

49

-48.8

28

σ17.3

32

0.2

92

315.2

91.3

48

0.4

58

0.0

29

0.0

15

0.0

63

0.0

52

0.0

15

0.2

91

42.1

45

max

117.1

79

0.4

65

1209.2

-1.2

41.9

45

0.1

10.0

55

0.1

60.3

48

-0.0

19

0.6

335.4

63

min

47.8

52

-0.7

04

-51.9

58

-6.6

31

0.1

14

-0.0

07

-0.0

03

-0.0

91

0.1

4-0

.079

-0.5

33

-133.1

18

28 of 34


Table

8.

Result

statis

tic

sof

B777-2

00

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φE

take-

off

µ58.5

54

2.1

05

0.0

0.0

02

--

--

0.1

72

0.0

-0

σ16.9

71.1

48

0.0

0.0

74

--

--

0.0

50.0

01

-0

max

92.4

94

4.4

01

0.0

0.1

5-

--

-0.2

72

0.0

02

-0

min

24.6

14

-0.1

91

0.0

-0.1

46

--

--

0.0

72

-0.0

02

-0

init

ialc

lim

b

µ102.9

89

0.3

34

798.9

53

9.0

03

0.9

58

--

-0.3

06

0.0

92

0.1

49

116.5

11

σ18.5

79

1.4

65

362.0

63

3.9

27

0.2

99

--

-0.0

56

0.0

47

0.8

2201.6

53

max

140.1

47

3.2

64

1523.0

816.8

56

1.5

57

--

-0.4

17

0.1

85

1.7

89

519.8

18

min

65.8

3-2

.597

74.8

26

1.1

50.3

6-

--

0.1

94

-0.0

02

-1.4

9-2

86.7

95

clim

b

µ190.3

99

0.1

23

5017.2

17

10.0

95

0.4

44

--

-0.5

97

0.0

56

0.0

64

120.0

39

σ33.0

37

2.2

72

2014.5

48

3.8

66

0.1

29

--

-0.1

15

0.0

27

0.4

47

462.8

66

max

256.4

72

4.6

68

9046.3

13

17.8

26

0.7

02

--

-0.8

27

0.1

09

0.9

58

1045.7

72

min

124.3

25

-4.4

21

988.1

22.3

63

0.1

86

--

-0.3

67

0.0

03

-0.8

3-8

05.6

93

cru

ise

µ247.8

17

0.0

77

11087.7

10.0

01

0.4

84

0.0

35

0.0

08

0.0

14

0.8

37

0.0

0.0

34

15.6

4

σ24.9

75

3.3

3759.0

09

0.2

01

0.1

32

1.2

21

0.0

09

0.0

23

0.0

85

0.0

01

0.8

75

766.1

78

max

297.7

67

6.7

38

12605.7

29

0.4

03

0.7

48

2.4

77

0.0

27

0.0

59

1.0

06

0.0

02

1.7

84

1547.9

96

min

197.8

68

-6.5

83

9569.6

91

-0.4

0.2

21

-2.4

06

-0.0

11

-0.0

32

0.6

67

-0.0

02

-1.7

16

-1516.7

16

init

iald

esce

nt

µ232.4

44

0.0

34

9400.6

82

-8.6

87

0.3

22

0.0

12

0.0

06

0.0

68

0.7

7-0

.038

0.0

42

-81.0

99

σ26.1

63

3.3

45

1432.2

34.7

19

0.1

13

0.0

08

0.0

04

0.0

46

0.0

93

0.0

20.4

57

881.3

38

max

284.7

69

6.7

23

12265.1

42

0.7

50.5

49

0.0

27

0.0

13

0.1

59

0.9

55

0.0

02

0.9

55

1681.5

78

min

180.1

18

-6.6

55

6536.2

23

-18.1

24

0.0

96

-0.0

03

-0.0

02

-0.0

24

0.5

84

-0.0

77

-0.8

72

-1843.7

75

des

cent

µ158.6

25

-0.1

17

3816.2

5-6

.838

0.3

93

0.0

17

0.0

08

0.0

65

0.4

9-0

.043

0.0

72

-94.1

28

σ33.6

29

2.1

73

1688.2

49

3.2

78

0.1

56

0.0

10.0

05

0.0

41

0.1

12

0.0

18

0.7

02

442.0

78

max

225.8

83

4.2

29

7192.7

48

-0.2

81

0.7

06

0.0

37

0.0

18

0.1

46

0.7

13

-0.0

08

1.4

76

790.0

28

min

91.3

66

-4.4

62

439.7

52

-13.3

94

0.0

8-0

.003

-0.0

02

-0.0

17

0.2

66

-0.0

78

-1.3

33

-978.2

84

fin

ala

pp

roach

µ83.3

34

-0.1

08

580.1

64

-4.0

72

1.0

13

0.0

51

0.0

26

0.0

28

0.2

47

-0.0

50.0

41

-49.8

8

σ15.6

0.9

3314.1

09

1.2

07

0.3

90.0

25

0.0

13

0.0

15

0.0

47

0.0

13

0.2

78

76.9

21

max

114.5

34

1.7

52

1208.3

82

-1.6

58

1.7

92

0.1

01

0.0

51

0.0

59

0.3

4-0

.024

0.5

96

103.9

62

min

52.1

35

-1.9

68

-48.0

54

-6.4

86

0.2

33

0.0

01

0.0

01

-0.0

02

0.1

53

-0.0

75

-0.5

14

-203.7

22

29 of 34


Table

9.

Result

statis

tic

sof

B757-2

00

Ph

ase

sVTAS

VTAS

hdh

dt

CL

CD

CD

,0K

Mγ

φE

take-

off

µ54.1

49

2.2

48

0.0

0.0

06

--

--

0.1

59

0.0

-0

σ16.8

62

1.0

03

0.0

0.1

06

--

--

0.0

50.0

02

-0

max

87.8

72

4.2

55

0.0

0.2

18

--

--

0.2

58

0.0

03

-0

min

20.4

26

0.2

42

0.0

-0.2

06

--

--

0.0

6-0

.003

-0

init

ialc

lim

b

µ100.9

10.4

09

870.5

911.1

99

0.8

92

--

-0.3

0.1

16

0.2

06

152.1

55

σ18.1

19

0.9

349.2

09

4.5

78

0.2

89

--

-0.0

54

0.0

55

0.8

66

178.1

92

max

137.1

48

2.2

08

1569.0

07

20.3

55

1.4

69

--

-0.4

08

0.2

26

1.9

38

508.5

39

min

64.6

72

-1.3

91

172.1

72

2.0

43

0.3

14

--

-0.1

91

0.0

05

-1.5

25

-204.2

3

clim

b

µ191.9

66

0.1

82

5173.6

77

10.9

87

0.3

65

--

-0.6

03

0.0

61

0.0

85

147.5

34

σ33.1

92

0.8

06

1948.3

94

4.4

24

0.0

97

--

-0.1

16

0.0

32

0.7

74

244.8

2

max

258.3

51

1.7

94

9070.4

64

19.8

35

0.5

59

--

-0.8

34

0.1

24

1.6

33

637.1

74

min

125.5

82

-1.4

31

1276.8

92.1

39

0.1

7-

--

0.3

72

-0.0

03

-1.4

64

-342.1

05

cru

ise

µ232.7

57

0.0

02

11003.8

85

0.0

0.4

28

0.0

30.0

07

0.0

17

0.7

85

0.0

0.0

31

0.5

σ24.2

22

0.4

15

892.4

04

0.2

70.1

06

1.0

0.0

10.0

39

0.0

82

0.0

01

0.7

22

120.8

47

max

281.2

0.8

31

12788.6

93

0.5

41

0.6

42.0

29

0.0

28

0.0

95

0.9

50.0

02

1.4

75

242.1

93

min

184.3

13

-0.8

28

9219.0

78

-0.5

41

0.2

16

-1.9

7-0

.014

-0.0

61

0.6

2-0

.002

-1.4

14

-241.1

94

init

iald

esce

nt

µ231.8

41

-0.0

38

9279.8

39

-8.9

74

0.3

02

0.0

12

0.0

06

0.0

85

0.7

66

-0.0

39

0.0

34

-100.0

14

σ25.5

92

0.5

37

1392.5

84.8

53

0.1

22

0.0

08

0.0

04

0.0

66

0.0

88

0.0

21

0.6

17

176.1

66

max

283.0

26

1.0

36

12064.9

98

0.7

32

0.5

47

0.0

28

0.0

14

0.2

17

0.9

41

0.0

04

1.2

67

252.3

18

min

180.6

57

-1.1

13

6494.6

8-1

8.6

79

0.0

57

-0.0

04

-0.0

02

-0.0

47

0.5

91

-0.0

82

-1.1

99

-452.3

45

des

cent

µ164.9

05

-0.1

24

3794.1

16

-8.0

36

0.3

50.0

17

0.0

09

0.0

90.5

09

-0.0

49

0.0

61

-105.4

02

σ35.9

93

0.5

58

1662.9

13.9

0.1

68

0.0

11

0.0

05

0.0

67

0.1

19

0.0

20.5

56

214.5

56

max

236.8

91

0.9

91

7119.9

35

-0.2

36

0.6

85

0.0

39

0.0

19

0.2

24

0.7

47

-0.0

09

1.1

72

323.7

11

min

92.9

19

-1.2

4468.2

97

-15.8

36

0.0

15

-0.0

05

-0.0

02

-0.0

45

0.2

71

-0.0

89

-1.0

5-5

34.5

14

fin

ala

pp

roach

µ79.8

91

-0.1

34

607.2

86

-3.8

86

1.1

81

0.0

62

0.0

31

0.0

28

0.2

37

-0.0

50.0

6-5

1.1

43

σ19.5

53

0.4

47

301.3

35

1.4

11

0.5

49

0.0

34

0.0

17

0.0

21

0.0

58

0.0

15

0.4

42

74.5

82

max

118.9

98

0.7

61209.9

56

-1.0

63

2.2

79

0.1

30.0

65

0.0

71

0.3

53

-0.0

21

0.9

44

98.0

22

min

40.7

85

-1.0

28

4.6

17

-6.7

09

0.0

84

-0.0

06

-0.0

03

-0.0

15

0.1

2-0

.079

-0.8

23

-200.3

08

30 of 34


Eurocontrol Aircraft Database

Table 10. Flight performance of A320 obtained from Eurocontrol Aircraft Database30

Parameter Take-off Initial Climb Climb Cruise Climb Cruise Initial Descent Descent Approach Landing

VTAS(m/s) 75 90 150 220 231 220 145 128 75

h(m) 0 1525 4570 8000 12496 8000 3000 1000 0dhdt

(m/s) 0 12 10 5 0 -4 -18 -8 0

M - - - 0.78 0.79 0.78 - - -

Table 11. Flight performance of B787-800 obtained from Eurocontrol Aircraft Database30


VTAS(m/s) 85 98 150 224 241 238 154 123 72

h(m) 0 1525 4570 8000 13100 8000 3000 1000 0dhdt

(m/s) 0 13.7 10 8 0 -13 -14 -8 0

M - - - 0.79 0.85 0.84 - - -

Table 12. Flight performance of A380 obtained from Eurocontrol Aircraft Database30


VTAS(m/s) 77 98 123 260 268 260 154 128 72

h(m) 0 1525 4570 8000 13100 8000 3000 1000 0dhdt

(m/s) 0 7.6 12.7 6.6 0 -5 -10 -5 0

M - - - 0.83 0.85 0.83 - - -

31 of 34


APE Documentation

The APE tool is the main analysis tool used in this thesis. It tool specially developed functions and classes.In order to extend the usability of the tool outside of this thesis and without the expertise of the author adetailed documentation was made. The full documentation is not appended in the Appendix due to its size.It can be found as an attached document. Now a short overview of the tool can be seen. The APE toolconsist of three sub tools. They can be seen as three individual applications, but are bound by the use of theaircraft type object. Each will be discussed in this documentation. The file structure of APE is as follows.

• APE

– APE tool

∗ APE.py: APE is the main analysis script. The user can set analysis parameters such asaircraft type and runtime. By running the script the analysis is initiated.

– APE viewer

∗ viewer.py: The viewer script is used to visualize the results from the APE.py analysis.

– APE simulator

∗ main.py: This script performs the simulation.

∗ simlib: Library that houses supporting simulation functions

· init .py

· editing.py: This module do the waypoint extraction.

· io.py: This module contains input and output functions. Mainly to communicate withthe AircraftType object.

· plot.py: This module contains functions for plotting.

· sim.py: This module contains the dynamics and calculation functions.

– APE lib

∗ init .py

∗ aircrafttype.py: This file contains the AircraftType class, which is used to store aircraft typestatistics.

∗ flightclass.py: This file contains the Flight class.The Flight objects is where the actual calcu-lation of parameter takes place.

∗ aircraftdb.py: This file contains a variable with many aircraft types and corresponding pa-rameters.

∗ airportsdb.py: This file contains a variable with many airports and corresponding attributes.

∗ aero.py: File containing special functions. See Bluesky documentation for this function.

∗ fuzzylog2.py: Module that contains the fuzzy logic used to determine the phase of the mea-surement.

32 of 34


Acknowledgments

I would like to thank my thesis advisers J. Sun, J. Ellerbroek and J. Hoekstra. Whenever I ran intotrouble or had a question about my research or writing it was always easy to reach out. I really value thefact that they allowed this thesis to be my own work. Steering and directions were given, but my opinionappreciated. Finally the pace at which I carried out this thesis would not have been possible without theconsent and active collaboration of my advisers.

References

1Jacco. M. Hoekstra and Joost Ellerbroek. Bluesky atc simulator project: an open data and open source approach. In 7thInternational Conference on Research in Air Transportation, 2016, submitted.

2Fligthradar24, 2016.3C Rodney Hanke. The Simulation of a Large Jet Transport Aircraft Volume I: Mathematical Model. Contractor Report,

pages 1–62, 1971.4Lucio Bianco and Amedeo R Odoni. Large scale computation and information processing in air traffic control. Springer,

1993.5Mark E Peters, Mark G Ballin, and John S Sakosky. A multi-operator simulation for investigation of distributed air

traffic management concepts. In AIAA Guidance, Navigation, and Control Conference & Exhibit, Monterey, California,AIAA, volume 4596, pages 5–8, 2002.

6George J Couluris, CG Hunter, Matthew Blake, Karlin Roth, Doug Sweet, Philippe Stassart, James Phillips, and AlexHuang. National airspace system simulation capturing the interactions of air traffic management and flight trajectories. InAIAA Guidance, Navigation, and Control (GNC) Conference, 2003.

7B.S. Frank Bussink, Ir Jacco Hoekstra, and Ir Bart Heesbeen. Traffic manager: a flexible desktop simulation tool enablingfuture atm research. In Digital Avionics Systems Conference, 2005. DASC 2005. The 24th, volume 1, pages 3–B. IEEE, 2005.

8Angela Nuic, Chantal Poinsot, Mihai-George Iagaru, Eduardo Gallo, Francisco A Navarro, Carlos Querejeta, et al.Advanced aircraft performance modeling for atm: enhancements to the bada model. In 24th Digital Avionics System Conference,Washington DC, 2005.

9Yingchuan Zhang, Goutam Satapathy, Vikram Manikonda, and Nikhil Nigam. Ktg: A fast-time kinematic trajectorygenerator for modeling and simulation of atm automation concepts and nas-wide system level analysis. In AIAA Modeling andSimulation Technologies Conference, pages 2–5, 2010.

10Damir Poles, Angela Nuic, and Vincent Mouillet. Advanced aircraft performance modeling for atm: Analysis of badamodel capabilities. In Digital Avionics Systems Conference (DASC), 2010 IEEE/AIAA 29th, pages 1–D. IEEE, 2010.

11D Poles. Base of aircraft data (bada) aircraft performance modelling report. Eurocontrol Experimental Center, 2009.12European Commission. Report from the commission to the european parliament and the council on the implementation

of the single sky legislation: time to deliver., November 2011.13Airbus. A320 dimensions and key data, 2016.14EASA. Icao aircraft engine emissions databank, 2016.15Lorenz Edward. Predictability: Does the flap of a butterfly’s wings in brazil set off a tornado in texas? American

Association for the Advancement of Science, Washington, DC, 1972.16Larry Oolman. Upper air data, 2015.17Daniel Delahaye and Stephane Puechmorel. Tas and wind estimation from radar data. In Digital Avionics Systems

Conference, 2009. DASC’09. IEEE/AIAA 28th, pages 2–B. IEEE, 2009.18WM Hollister, ER Bradford, and JD Welch. Using aircraft radar tracks to estimate wind aloft. The Lincoln Laboratory

Journal, 2(3):555–565, 1989.19Junzi Sun, Jacco. M. Hoekstra, and Joost Ellerbroek. Modeling and inferring aircraft takeoff mass from runway ads-b

data. In 7th International Conference on Research in Air Transportation, 2016, submitted.20Junzi Sun, Jacco. M. Hoekstra, and Joost Ellerbroek. Large-scale flight phase identification from ads-b data using machine

learning methods. In 7th International Conference on Research in Air Transportation, 2016, submitted.21de Siebren Haan, de Marijn Haij, and Jan Sondij. The use of a commercial ads-b receiver to derive upper air wind and

temperature observations from mode-s ehs information in the netherlands, 2013.22AMP de Leege, MM Van Paassen, and M Mulder. Using automatic dependent surveillance-broadcast for meteorological

monitoring. Journal of Aircraft, 50(1):249–261, 2012.23AMP De Leege, M Mulder, and MM Van Paassen. Novel method for wind estimation using automatic dependent

surveillance-broadcast. Journal of Guidance, Control, and Dynamics, 35(2):648–653, 2012.24Richard Alligier, David Gianazza, and Nicolas Durand. Learning the aircraft mass and thrust to improve the ground-based

trajectory prediction of climbing flights. Transportation Research Part C: Emerging Technologies, 36:45–60, 2013.25Boeing. Boeing website (www.boeing.com), 2016.26Airbus. Airbus website (www.airbus.com), 2016.27Nathan Meier. Jet engine specification database, 2005.28John David Anderson. Introduction to flight, volume 199. McGraw-Hill Boston, 2005.29Frederik Michel Dekking. A Modern Introduction to Probability and Statistics: Understanding why and how. Springer

Science & Business Media, 2005.30Eurocontrol aicraft performance database, 2016.

33 of 34


31Dell Adams William Roberson, Robert Root. Fuel conservation strategies: Cruise flight. Aero Magazine, 2007.32Bill Roberson. Fuel conservation strategies: Cost index explained. Aero Magazine, 2007.33Chris Brady. The Boeing 737 Technical Guide. Lulu Press, Inc, 2015.34CFM. Cfm56-3 turbofan engine specifications, 2016.

34 of 34


aircraft performance parameter estimation using global ads

Documents