ADAPT Public Deliverable D6-V1.0 - Eurocontrol · The study was conducted by a Consortium composed...

TRS T06 / 22316TC Aircraft Data Aiming at Predicting the Trajectory

Ref.: ADAPT / D6-V1.0 Date : 08/02/2008

D6: Final Report ADAPT Public Deliverable Page : i

ADAPT

Aircraft Data Aiming at Predicting the Trajectory

Public Deliverable D6: Final Report 08/02/2008

V1.0


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Document Change Log

Release Author Edition Date

Affected Sections / Comments

0.1 T. COURDACHER, V. MOUILLET

22/11/2007 Creation


06/12/2007 Update following Progress Meeting #8


20/12/2007 Update following 11/12/07 review meeting at EEC


08/02/2008 Finalization at project completion, including: • Consistency check across ADAPT

deliverables D1 to D5 • Thales Air Systems internal review

process

Document Distribution

To/cc Organisation Name

To EUROCONTROL C. TAMVACLIS To EUROCONTROL C. GARCIA To LFV A. ERZELL To THALES Air Systems (ATMS) B. AYRAL To THALES Air Systems (ATMS) T. COURDACHER To THALES Air Systems (ATMS) P. DREYER To THALES Air Systems (ATMS) V. MOUILLET

Review and Approval of the Document

Organisation Responsible for

Approval Name of person approving the

documentDate

EUROCONTROL C. TAMVACLIS


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Executive Summary Study Context and Environment: EUROCONTROL intends to explore the benefits achievable through the integration of ADD (Aircraft Derived Data) into industrial Trajectory Predictors applicable in operational ATC systems. The first phase of this overall study concerned TP performance improvements for RNAV / Continuous Descent Approaches. The objectives of the present study, part of this first phase, was to determine which pieces of ADD could be used in an industrial strength ground TP and quantify the performance improvements they would provide. The present document is the Deliverable D6 of ADAPT Study. It is the Final Report that summarizes the work and measurements done during the ADAPT study and mainly compares the TP performances achieved with ADD parameters versus the baseline TP performance (without ADD). The work performed under this study included:

1. Analysis and identification of ADD parameters that could be useful to the Ground TP; 2. TP tuning and establishment of baseline TP performance without ADD; 3. Definition and development of any required data conversion and analysis tools; 4. Modifications of the ground TP to use the selected ADD; 5. Simulations runs of the modified ground TP using ADD extracted from onboard

recordings of live flights performing RNAV Continues Descent Approaches (CDA) and associated environmental and ATC data;

6. Measurement and assessment of performance benefits achievable through the use of ADD in the Ground TP.

The study was conducted by a Consortium composed of Thales Air Systems as leader and LFV / Swedavia. Its actual duration was 15 months since mid-September 2006 (initially planed for 11 months). The TP used was the new generation Thales TP installed in Denmark ATCC and planned to go into operation by end 2007 (the operational transition of the Danish system is currently ongoing). The study was based on data provided by LFV / Swedavia. These data consisted in flight recordings performed for the NUPII+ project in the following operational environment:

• Flight phase: Green Approaches (GA) equivalent to an Advanced Continuous Descent Approach (A-CDA), which essentially means an uninterrupted idle approach from Top of Descent (TOD) to touchdown in order to minimise fuel burn, airline costs and environmental impact.

• Aircraft type and operator: Boeing 737 NG (4 aircrafts, B737-600 or B737-800) operated by SAS Sweden

• Arrival airport: Stockholm-Arlanda airport


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TP Tuning (Baseline TP Performance without ADD): An evaluation of the baseline performance of the TP was conducted on the acquired flight data recordings. The tuning consisted in making the required adjustments to the TP offline parameters in order to optimize this baseline performance. The following figure summarizes the internal steps of the 4D trajectory computation:

Flight plan

Constraints 2D path data

2D trajectory

Lateral deviation

Route Manager

ConstraintManager

+ TP

TP

Offline data

Trajectory data

Metrics Software modules

TEST RIG

3D trajectory

4D trajectory

A/C performances

Vertical deviation

Time deviation

Online data

Figure 1 - Trajectory computation overview

Measurements that served as reference without ADD were done based on two metrics (vertical deviation and time deviation expressed in mean value and standard deviation 20, 15, 10 and 5 minutes from the measurement points) applied to 5 measurement points selected during the descent phase (i.e. TOD, FL250, 40 NM and 10 NM from runway threshold, and runway threshold – no vertical deviation at runway threshold-). Among the 126 flights without ADD data that have been provided by LFV, a total of 37 flights have been selected for the tuning.


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TP Measurements (TP with ADD versus Baseline TP): During the course of the ADAPT study NUP II+ Aircraft Derived Data collected by LFV were mainly obtained from an onboard recorder (know as the QAR “Quick Access Recorder”) and from the ACARS for FMS 4D Trajectories. Among the 127 flights with ADD data that have been provided by LFV, a total of 20 flights have been selected for measurements. From the TP ADD accuracy measurements, we could divide the analyzed ADD into three groups, given the improvements they brought to the TP accuracy:

• Notable improvements: o FMS trajectory (either single or multiple), which brought improvement in both

vertical and longitudinal accuracies o Takeoff weight, which brought improvement in vertical accuracy; this result

should however be confirmed by further studies, since it was obtained here relative to a non-tuned TP

• Slight improvements: o Ground speed, which brought improvement in longitudinal accuracy

• No noticeable improvement: o TAS (True Air Speed) o Weather data o GPS position

From the baseline TP performance assessment the main areas where accuracy improvements were needed were:

• Top of Descent determination, to improve vertical accuracy: This parameter was improved through knowledge of FMS trajectory and TOW

• Landing speed, to improve longitudinal accuracy: This parameter was improved through knowledge of the FMS trajectory and the ground speed (short term only)

Main Study Achievements: The work that has been realized in the frame of this study brought the following positive achievements:

• The setting of a method and of an appropriate test suite to assess the performance of the Thales “state-of-art” TP 4D based on a EUROCONTROL aircraft performance model

• The application of the above method and test suite to assess and tune the baseline TP performance as well as to measure and quantify the benefit of individual ADD.

• The Thales TP was upgraded to process available ADD parameters (in particular 4D FMS trajectories)

• The TP ADD measurement showed promising results, in particular in the vertical plan for a better TOD determination through the usage of the 4D FMS trajectories

• The comparison of TP ADD versus Baseline TP measures showed that to optimize the overall TP performances: • Tuning of the Baseline TP off-line parameters (without ADD) may have to be revised

once ADD are being used (e.g. processing of the Take-off weight ADD) • Baseline TP on-line process principles and algorithms may have to be refined and

enhanced


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Study Limitations and Recommendations: The present study presented some limitations:

• By definition of the TRS scope, the baseline TP tuning and TP ADD measures were limited to one category of aircraft (B737 NG), in the descent phase of flight (Approach and Landing) for flights performing CDA procedures (I.e. unconstrained flights)

• The ADD used were limited to those present in the QAR onboard recording and to the 4D Trajectories received via the ACARS

Based on the above achievements and limitations, it is recommended to extend the usage of the test suite and of the upgraded TP to a larger scale, e.g. :

• To assess the impact of TP ADD on the overall ATC System, extend the test suite to other operational components and notably controller tools. This will support the definition of minimum TP performance standards that may be required by these operational components: Add operational components that depend on TP 4D performances (i.e. components indirectly impacted by the ADD). For example controller tool like MTCD 4D (Medium Term Conflict Detection), to assess the induced impact of ADD on these operational components

• Process more data: • Get and process more flight samples to increase the statistical validity of the

measures and results • Get and process new ADDs (e.g. FMS mode; ADS-B data) to assess the benefit of

using these new ADDs for profile computation individually (as done in ADAPT), and then in combination with other ADDs (see below)

• Process ADD combinations (e.g. True Air Speed & Takeoff weight) to assess the benefit (or the drawback) of using such or such combination of ADDs for profile computation

• Process more aircraft/FMS types (e.g. A330/340 with Honeywell FMS) to assess the quality, the integrity and the consistency of ADDs across various onboard architectures, used on different airframes, and hence to quantify further the ADD benefit for profile computation. This will support the definition of minimum ADD performance standards that may be required to use the ADD in ground trajectory computation.

• Include other phases of flight to extend the TP upgrades to the whole TP 4D computation (e.g. Climbing phase)

• Include other types of operation (e.g. constrained flights, not CDA only) to include more realism in the measures (CDA operations are very limited nowadays)

• Refine and enhance the baseline TP tuning and algorithms based on the knowledge got from the analysis of TP ADD versus Baseline TP measures.


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Table of Contents

1. INTRODUCTION....................................................................................................... 9 1.1. Presentation of the Document .......................................................................... 9 1.2. Presentation of the Study ................................................................................. 9 1.3. Contents ......................................................................................................... 10 1.4. Glossary, Abbreviations and Acronyms.......................................................... 10 1.5. Reference Documents.................................................................................... 11

2. OPERATIONAL ENVIRONMENT............................................................................ 12 2.1. Green Approaches ......................................................................................... 12 2.2. Boeing 737 NG from SAS............................................................................... 12

2.2.1. Flight Management System................................................................ 12 2.2.2. ADS-B 13

2.3. Stockholm-Arlanda airport .............................................................................. 14 2.3.1. Airport runways .................................................................................. 14 2.3.2. STARs 15 2.3.3. Approach procedures ......................................................................... 15

3. ANALYSIS METHOD AND METRICS..................................................................... 19 3.1. Metrics applicable to input data ...................................................................... 19

3.1.1. 2D flown path ..................................................................................... 19 3.1.2. ADD values ........................................................................................ 19 3.1.3. Weather conditions............................................................................. 20

3.2. Metrics applicable to output data.................................................................... 20 3.2.1. Time deviation .................................................................................... 20 3.2.2. Vertical deviation ................................................................................ 21 3.2.3. Derivations of metrics ......................................................................... 22

3.3. Conversion/Analysis tools description ............................................................ 22 3.3.1. Environment of the tools..................................................................... 22 3.3.2. Description of the input data............................................................... 23 3.3.3. Description of the output data............................................................. 24 3.3.4. Description of the tools ....................................................................... 25

4. TUNING OF THE TP............................................................................................... 29 4.1. Aims of the Tuning.......................................................................................... 29

4.1.1. TP algorithms ..................................................................................... 29 4.1.2. TP offline parameters ......................................................................... 29

4.2. Parameters to tune......................................................................................... 29 4.2.1. Flight path extraction data .................................................................. 29 4.2.2. Constraint definition data.................................................................... 29 4.2.3. Aircraft performances ......................................................................... 30

4.3. Tuning Methodology ....................................................................................... 30 4.3.1. Ordering of the tuning phases ............................................................ 30 4.3.2. Tuning process................................................................................... 30

4.4. TP Tuning Results .......................................................................................... 30 4.4.1. Scope, Rationale and Objectives ....................................................... 30 4.4.2. Description of the collected data ........................................................ 31 4.4.3. Applied Tuning ................................................................................... 35 4.4.4. Achieved Baseline Performances (after Tuning) ................................ 42

4.5. TP Tuning Conclusions .................................................................................. 42 5. TP ADD ASSESSEMENT ....................................................................................... 43

5.1. Available ADD Parameters............................................................................. 43


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5.2. Description of Collected Data .........................................................................44 5.2.1. Quantity of collected data ...................................................................44 5.2.2. Overview on collected ADD quality.....................................................45

5.3. Used metrics and ADD Improvements Visualization.......................................46 5.3.1. Vertical deviation ................................................................................46 5.3.2. Time deviation ....................................................................................46 5.3.3. Visualization of ADD improvements....................................................46

5.4. TP ADD Measurement Results (TP with ADD versus Baseline TP) ...............47 5.4.1. Higher priority parameters ..................................................................47 5.4.2. Lower priority parameters ...................................................................66

5.5. TP ADD Measurement Conclusions ...............................................................82 6. ACHIEVEMENTS, LIMITATIONS AND RECOMMENDATIONS .............................82 ANNEX A: TUNING OF THE TP ....................................................................................A0


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1. INTRODUCTION 1.1. Presentation of the Document The present document is the Public Deliverable D6 of the ADAPT Study. It is the Final Report that summarizes the work and measurements done during the ADAPT study and mainly compares the TP performances achieved with ADD parameters versus the baseline TP performance (without ADD). 1.2. Presentation of the Study EUROCONTROL intends to explore the benefits achievable through the integration of ADD into industrial Trajectory Predictors applicable in operational ATC systems. The first phase of this overall study concerned TP performance improvements for RNAV / Continuous Descent Approaches. The objectives of the present study, part of this first phase, was to determine which pieces of aircraft derived data could be used in an industrial strength ground TP and quantify the performance improvements they would provide. The results of this study will help EUROCONTROL to quantify the impact of TP accuracy ameliorations on ATM capacity and safety and to assess whether such TP performance improvements would be sufficient to meet the needs of controller support tools such as those required by the TMA2010+ and FASTI Programmes. The work performed under this study included:

1. Analysis and identification of ADD parameters that could be useful to the Ground TP; 2. TP tuning and establishment of baseline TP performance without ADD; 3. Definition and development of any required data conversion and analysis tools; 4. Modifications of the ground TP to use the selected ADD; 5. Simulations runs of the modified ground TP using ADD extracted from onboard

recordings of live flights performing RNAV CD approaches and associated environmental and ATC data;

6. Measurement and assessment of performance benefits achievable through the use of ADD in the Ground TP.

The study was conducted by a Consortium composed of Thales Air Systems as leader and LFV / Swedavia. Its actual duration was 15 months since mid-September 2006 (initially planed for 11 months). The TP used is the Thales TP installed in Denmark ATCC and planned to go into operation by end 2007. The above work was formally reported and documented into seven deliverables (including the present ADAPT Final Report that is not described in the list here below):

1. ADAPT Proprietary Consortium Deliverable D0.a [2]: The TP ADD PWP (Project Work Plan) that describes how tasks and deliverables were planned to be achieved, for what dates and at what cost. It also describes project reporting mechanism and meeting dates. Last, it describes how project risks were identified and tracked

2. ADAPT Proprietary Consortium Deliverable D1 [3]: The ADAPT TP Test Plan that is a high level Test Plan that explained the environment, the objectives and the method to be followed by ADAPT Study;

3. ADAPT Proprietary Consortium Deliverable D2 [4]: The ADAPT Baseline TP Performance Report that describes the actual baseline performances of the ground TP being used in the operational conditions specified in D1 (TP Test plan), but without any


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ADD. As a tuning of the TP Aircraft Performance Model, offline parameters (e.g. STAR definition) and input data (e.g. 2D path) was needed to set this baseline, the D2 report also presents this tuning;

4. ADAPT Proprietary Consortium Deliverable D3 [5]: The ADAPT Ground TP Modification Report that describes the required changes on the TP so that it was able to integrate the useful information extracted from the airborne recorded data;

5. ADAPT Proprietary Consortium Deliverable D4 [6]: The ADAPT Conversion/Analysis Tools Specification that aims at specifying the pre-processing tools that converted the format of input data and automatically ran the whole batch of tests, as well as the post processing tools that supported the analysis of the results of these tests;

6. ADAPT Proprietary Consortium Deliverable D5 [7]: The ADAPT TP Performance Measurements that presents the set of TP performance measurements for all the assessed ADD parameters.

1.3. Contents The present document is composed of 6 chapters preceded by an executive summary.

The present introduction mainly presenting the study and the document; A description of the operational environment where the study was conducted; A presentation of the metrics used to assess various TP performances (including the

Analysis/Conversion tools description); A presentation of the way used to tune the TP, and of the TP tuning results; A presentation of the ADD assessed, including the preliminary selection, the actual ADD

assessed, and the measurement results in the form of a comparison between the TP with and without ADD;

A conclusion section that describes the study achievements and limitations, and that proposes recommendations for future TP ADD activities.

In addition a Consortium Proprietary Annex (Annex A: Additional Tuning Information) provides TP tuning information that has been deemed confidential to the ADAPT consortium. 1.4. Glossary, Abbreviations and Acronyms Acronym Meaning 4D Four Dimension(al) A-CDA Advanced CDA ADAPT Aircraft Data Aiming at Predicting the Trajectory ADD Aircraft Derived Data ADEP Departure Airport ADES Destination Airport ADS-B Automatic Dependent Surveillance APR Automatic Position Report ARN Arlanda (airport) ATC Air Traffic Control ATCC ATC Centre ATM Air Traffic Management B737 Boeing 737 BADA Base of Aircraft Data CAS Calibrated Air Speed CDA Continuous Descent Approach


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Acronym Meaning D Deliverable DATMAS Danish Air Traffic Management System EEC EUROCONTROL Experimental Centre ETA Estimated Time of Arrival ETO Estimated Time Over (a point) FMS Flight Management System GA Green Approach GNSS Global Navigation Satellite System GRIB GRIdded Binary ILS Instrument Landing System ISA International Standard Atmosphere LFV Luftfartsverket NG New Generation NUP NEAN Update Programme P-RNAV Precision RNAV QAR Quick Access Recorder RFL Requested Flight Level RNAV Area Navigation ROCD Rate Of Climb/Descent SAS Scandinavian Airlines SID Standard Instrument Departure STAR Standard Arrival TAS True Air Speed TCP Trajectory Change Point TMA Terminal Manoeuvring Area TOD Top Of Descent TP Trajectory Predictor 1.5. Reference Documents Identifier Title Reference

[1] Use of Aircraft Derived Data in Trajectory Prediction / specification

Annex to T06/22316TC

[2] ADAPT Proprietary Consortium Deliverable D0.a (TP ADD Project Work Plan)

TPADD-PWP-V0.1

[3] ADAPT Proprietary Consortium Deliverable D1 (ADAPT TP Test Plan)

ADAPT-D1-V1.0

[4] ADAPT Proprietary Consortium Deliverable D2 (ADAPT Baseline TP Performance Report)

ADAPT-D2-V1.0

[5] ADAPT Proprietary Consortium Deliverable D3 (ADAPT Ground TP Modification Report)

ADAPT-D3-V1.0

[6] ADAPT Proprietary Consortium Deliverable D4 (ADAPT Conversion/Analysis Tools Specification)

ADAPT-D4-V1.0

[7] ADAPT Proprietary Consortium Deliverable D5 (ADAPT TP Performance Measurements)

ADAPT-D5-V1.0


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2. OPERATIONAL ENVIRONMENT The study was based on data provided by LFV / Swedavia. These data consisted in flight recordings performed for the NUPII+ project in the following operational environment:

• Flight phase: Green Approaches • Aircraft type and operator: Boeing 737 NG from SAS • Arrival airport: Stockholm-Arlanda airport

Since some data were not available during the study, the following description encompasses some scenarios absent from the actual data or on the contrary some scenarios lacks some information about the actual collected data. However, these scenario are kept to keep track of what was initially intended and of what has actually been done. This final report presents the refinement performed on the actual content of the provided data and agreed with LFV during the course of this study. 2.1. Green Approaches “Green approach” (GA) is equivalent to an Advanced Continuous Descent Approach (A-CDA), which essentially means an uninterrupted idle approach from Top of Descent (TOD) to touchdown in order to minimise fuel burn, airline costs and environmental impact. When the aircraft reaches TOD, it follows the FMS calculated profile to the runway. In principle, interventions by the controller should only be made in order to maintain flight safety, but the Green Approach clearance may include defined ATC restrictions in terms of cleared level and required ETO or ETA given at an early time still that allows the aircraft to continue an uninterrupted descent. Adapted procedures are required for integrating flights making a Green Approach with flights making a conventional approach supported by controller vectoring instructions. This is because the approach trajectory in a Green Approach is set (unless needed to be modified for flight safety or strong traffic reasons) and that therefore other flights will be assigned a lower priority and have to “give way” to the Green Approach flight. Live trials have been conducted in periods of low traffic loads when the problems of traffic merging were small. 2.2. Boeing 737 NG from SAS The aircrafts used for the recordings were Boeing 737 New Generation (NG) operated by SAS Sweden. This fleet was composed of 4 aircrafts, B737-600 or B737-800. 2.2.1. Flight Management System In support of the NUP II+ project, SAS Sweden will equip their entire fleet of Boeing 737 New Generation with an upgraded Flight Management System (FMS) from Smiths Aerospace/General Electric that is capable of 4D trajectory and high precision ETA output. The aircraft FMS is a vital element for supporting NUP II+ validation trials. Whereas all aircrafts operated by SAS Sweden are equipped with an FMS, only B737 New Generation aircrafts with a Smith FMS including Upgrade 10.6 support the air-ground interactions and data requirements for the Green Approaches. Most of the recordings have been made with revision 10.6 of the Smith FMS.


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2.2.2. ADS-B The Green Approaches also require the downlink of trajectory data derived from the FMS. The fleet of B737NG operated by SAS Sweden is ADS-B equipped. ADS-B is a function on an aircraft or a surface vehicle that periodically broadcast its identity, state vector (horizontal and vertical position, horizontal and vertical velocity) and other GNSS derived information. ADS-B is automatic because no external stimulus is required to elicit a transmission; it is dependent because it relies on on-board broadcast transmission systems to provide surveillance information to other users. ADS-B could have provide a ground surveillance source through the downlink of tactical 4D Trajectory data that can be contained in the intent part of the ADS-B message broadcast by the aircraft. During the course of the ADAPT study NUP II+ Aircraft Derived Data collected by LFV were mainly obtained from an onboard recorder (know as the QAR “Quick Access Recorder”) and from the ACARS for FMS 4D Trajectories. This implied that some ADD parameters perceived important to improve the TP performances and readily available in ADS-B format were missing (e.g. the aircraft intent). However, onboard (more or less complete) FMS 4D Trajectories (coming from SAS-LFV ACARS exchanges) were available to LFV for some flights in one or more occurrences (e.g. 40 minutes before runway threshold and 3 minutes before TOD –Top Of Descent-). The availability of FMS 4D trajectories were considered as a good mitigation mean to the actual lack of ADS-B intents.


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2.3. Stockholm-Arlanda airport 2.3.1. Airport runways Stockholm-Arlanda Airport is operated by LFV. With three runways, Arlanda (ARN) is a Complex category airport. There are two parallel runways 01L-19R (3,301 m) and 01R-19L (2,500 m). The distance between these runways is 2,300 m and threshold of runway 01R is displaced 800 m to the south. A converging runway 08-26 (2,500 m) is located north of runway 01R.

Figure 2 - Arlanda arrival runways

Green Approaches have been performed to runways 26, 01L and 19R at Arlanda.

01L

01R

19R

19L

26


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2.3.2. STARs STARs may be defined from a TMA entry point down to the final approach fix or may end at a holding pattern. At large airports it is normal for all aircrafts to be vectored by ATC from the end of the STAR, which may be at a holding pattern, onto the final approach. The factors that must be considered in defining STARs include: the local topography, the navigation aids available (VOR/DME/NDB), any built-up areas and noise sensitive areas, location of other SIDs, STARs and airports. An arriving aircraft typically joins a STAR at the TMA entry point during descent after having left its cruising level at the TOD. STARs based on area navigation (RNAV) are becoming increasingly common as they are based on waypoints defined by coordinates and not ground-based NAVAIDS that have to be over-flown. P-RNAV SIDs are available for departure operations from Arlanda, but all STARs are based on ground-based NAVAIDs (VOR and DME). There are currently no RNAV STARS. Aircrafts conducting a Green Approach will use P-RNAV STARs developed specifically by the NUPII+ project. These STARs extend to the ILS. FMS/RNAV arrival procedures, by which the FMS calculates an optimal flight path for interception of the final approach track, enable a straight-in or curved approach by the flight crew’s own navigation. In the recorded flights, the approach has been carried out as a conventional approach based on ILS. Later trials may include (possibly curved) RNP 0.1 or 0.3 approaches without ILS. RNP STARs supporting Green Approaches are not yet defined. The STARs used for the Green Approaches to runways 26, 01L and 19R are described in the following figures. 2.3.3. Approach procedures The operational procedure used for Green Approaches includes the following elements:

• Speed constraint: 250 kt below FL100

• Level constraints: - constraint removed on inbound fix Ex1: FL220-FL250 on HAMMAR for GA on runway 01L, constraint is FL190 for non-GA Ex2: FL200-FL250 on TROSA for GA on runway 19R, constraint is FL190 for non-GA - existing constraints on other STAR fixes are naturally respected by GA flights Ex1: FL100-FL130 on SA468 for GA on runway 26, constraint is FL100 for non-GA Ex2: FL069-FL073 on SA467 for GA on runway 26, constraint is FL070 for non-GA

• GA flights may be directed to bypass the beginning of the STAR Ex1: DCT SA808 for GA on runway 01L through STAR ELTOK1J Ex2: DCT SA606 or SA607 for GA on runway 26 through STAR TROSA1V


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Figure 3 - RNAV STAR for runway 26


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Figure 4 - RNAV STAR for runway 19R


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Figure 5 - RNAV STAR for runway 01L


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3. ANALYSIS METHOD AND METRICS 3.1. Metrics applicable to input data Basic metrics have been defined on some TP inputs, either to assess the quality of these inputs and discard flights that do not reach a predefined quality threshold, or to enable the categorisation of flights into relevant groups based on these inputs. The following TP inputs were subject to basic metrics definitions:

• 2D flown path (radar tracks versus flight plan 2D trajectory) • ADD values • Weather conditions

3.1.1. 2D flown path The metric used to evaluate the quality of the radar track vs. the expected 2D path was the lateral deviation, which is defined as the length of the perpendicular line segment from a track point to the closest trajectory segment. Given the curved nature of the considered approaches, non negligible lateral deviations were found between the “straight” TP input flight path and the actual aircraft trajectory. Consequently, the evaluation of the most important lateral deviation along the whole approach path has been used to give a good indicator of the adherence between the expected and actual trajectories in the 2D plan. Flights whose actual trajectory did not adhere tightly enough to the expected trajectory were discarded from the study.

x

y

R a d a r

T P

d 3

d 2

d 1

Figure 6 - Example of lateral deviation evaluation

This indicator has been used during the evaluation of the baseline TP performance to tune the offline STAR definitions, in order to ensure that lateral deviation was kept to a minimum. It was not used in the subsequent evaluation of the ADD impact on TP accuracy: The 2D path is processed by the route manager and used as input by the TP, hence no improvement was achievable on the lateral deviation through the use of ADD by the TP. 3.1.2. ADD values Some of the assessed ADD parameters have been subject to basic metrics definitions:

• Instantaneous aircraft performances (speed, ROCD): The max delta along the descent between the instantaneous ADD values and the default offline values was used to discard, or treat separately, flights whose actual performances were largely different from the expected performances

D=max(di)


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• Weather conditions: The max delta along the descent between the ADD instantaneous wind/temperature measures and the global wind/temperature forecasts (ex: GRIB) used for all flights by the TP was used to discard, or treat separately, flights whose weather conditions were not considered as consistent.

3.1.3. Weather conditions To produce a relevant statistical analysis among several flights, these flights could have been grouped by similar weather conditions:

• Weak or strong winds • High or low temperature

The proposed basic metrics on input weather data to allow such a grouping were:

• For wind conditions: max wind speed along the descent • For temperature conditions: max delta ISA along the descent.

Both weather global forecasts (from GRIB data) and weather local measurements (from ADD data) could have been used as source data to perform the weather conditions analysis. The above statements and initial hypothesis about input metrics on weather conditions were actually not applied to the study as it was found difficult to classify the available weather data using the defined wind/temperature categories. However, the weather data were taken into account in the TP tuning exercise that permits to set up the TP baseline (i.e. without ADD) used as a basis for the ADAPT TP comparison (i.e. TP without ADD versus TP with ADD). 3.2. Metrics applicable to output data After analysis of the operational context, the following basic metrics have been considered in order to assess the accuracy of the TP outputs:

• Time deviation: the difference in seconds between the predicted time over a trajectory point and the actual time over the closest matching track point

• Vertical deviation: the difference in altitude between the predicted level over a trajectory point and the actual level over the closest matching track point

Since time deviation is roughly equivalent to along-track deviation, the combination of those metrics cover the full (x, z) 2D space, allowing a complete evaluation of the TP accuracy. 3.2.1. Time deviation Two critical points appear as the most relevant ones for time deviation evaluation:

• arrival at runway (touchdown point) • merge point between RNAV approaches to this runway (ex: SA462 for runway 26),

located approximately 10 NM from the runway threshold. A third point was used for further evaluations earlier in the descent: 40 NM from threshold (i.e. approximately crossing FL100).


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Initial results showed that other evaluation points may be needed to present the time deviation in the first part of the descent, mainly the Top of Descent (TOD) and crossing of FL250 (approx. 80 NM from threshold). These additional evaluation points were actually considered when needed.

t

d ( t )

Radar TP(5)

TP(10)

t(A)-10 t(A)-5 t(A)

delta(5)

delta(10)

A

Figure 7 - Time deviation evaluation on arrival point

To give a global overview of the TP accuracy during the arrival flight phase, the time deviation on these points was evaluated at several look-ahead times: 5, 10, 15 and 20 minutes. 3.2.2. Vertical deviation Since the vertical deviation at touchdown is null, one critical point remains relevant for vertical deviation evaluation: the merge point between RNAV approaches to the runway (ex: SA462 for runway 26), located approximately 10 NM from the runway threshold. A second point was used for further evaluations earlier in the descent: 40 NM from the runway threshold. Initial results showed that other evaluation points may be needed to present the vertical deviation in the first part of the descent, mainly the Top of Descent (TOD) and crossing of FL250 (approx. 80 NM from threshold). These additional evaluation points were actually considered when needed.


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t

f l ( t )

Radar

TP(5)

TP(10)

Dfl(5)

Dfl(10)

t(P)-10 t(P)-5 t(P)

Figure 8 - Example of vertical deviation evaluation

To give a global overview of the TP accuracy during the arrival flight phase, the vertical deviation on these points was evaluated at several look-ahead times: 5, 10, 15 and 20 minutes. 3.2.3. Derivations of metrics The output metrics defined above can be considered as unit metrics, since they describe the accuracy of the TP outputs on a single flight. From these unit metrics, we can derive global metrics that apply to categories of flights, in order to provide relevant indicators of some general tendencies in the TP accuracy results. A derivation can be described as the application of a statistical method to an existing metric in order to derive a new metric. Examples of derivation include maximum, mean, and standard deviation. The chosen derivation was a combination of mean and standard deviation, which provided information on the expectable TP accuracy (mean) and the probability of achieving such an accuracy (standard deviation). 3.3. Conversion/Analysis tools description This section aims at specifying the pre-processing tools that convert the format of input data and automatically run the whole batch of tests as well as the post processing tools that support the analysis of the results of these tests. 3.3.1. Environment of the tools A pre-processor was specified and developed to provide services for converting recorded data into TP inputs. This implements the way to use the recorded data, especially the conversion of ADD into TP inputs.


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A post-processor was specified and developed to provide support to analysis, especially services for recording performance metrics and present them to the user, for comparison between runs. Both the pre-processor and the post-processor are integrated on a test rig together with the TP under assessment. This environment is described in the present section. The following figure presents an overview of the test rig architecture.

Flight plan

Track

Weather

ADD

Scenario database

Predicted trajectories with ADD

ADD impact

indicators

Pre-processor Replay tool

Post-processor

TP Engine

Input data

Intermediate data

Output data

Assessment applications to be developed

Existing application to be assessed

TEST RIG

Predicted trajectories

without ADD

Graphical display of selected

indicators and trajectories

Figure 9 - Overview of the rig 3.3.2. Description of the input data The main source of data for this study consisted in a series of recordings performed by LFV. The required input data can be divided in several main sets:

• Flight plan information • Track information • ADD information • Weather forecasts information

3.3.2.1. Flight plan information

Flight plan information consists in all the data required to create the planned trajectory that is used by the TP:

• aircraft type and operator, take-off weight • route information, such as ADEP, ADES, STAR and arrival runway • RFL and TAS


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These data have been recorded once for each flight. Occasional updates were also recorded for some flights: for example when this information changes in the course of the flight (e.g.: route change). 3.3.2.2. Track information

The track data was initially planned to be either radar based (SSR, ARTAS) or GPS based (ADS-B and/or QAR). The kind of track data that would have actually been used as reference in the study would have depended on its availability for each data source. The track data actually used as reference was the radar based one. The main reason is that this radar data has been available for the flights as soon as the tuning stage of the study began. The second reason is that the GPS based track data, that arrived late in the project time frame, was found very close to the radar based track data. The required track data information consisted in a set of 4D positions of the flight. These data has been recorded on a regular time basis for each flight. 3.3.2.3. ADD information

Available aircraft derived data (ADD) consists in: • Aircraft position: latitude, longitude, altitude (from GPS) • Aircraft state: airspeed, ROCD, heading, weight, FMS mode, etc. • FMS intents: 4D trajectory information on the next trajectory change points (TCP) • Local weather data measurements: temperature, wind speed/direction

When available, these data have been recorded on a regular time basis for each flight. However, these data arrived late in the project time frame and was not necessarily complete and consistent. This made the final ADD parameter selection for the evaluation of the TP with ADD restricted to the ADD available data. 3.3.2.4. Weather forecasts information

Two kinds of weather data are needed by the TP: • Wind data (direction and module) • Temperature data

These data are usually organised in cells, each cell being a part of the airspace defined by an altitude layer and a 2D area (GRIB data message format). For both kinds, the data need to be available at least for all the airspace crossed during the descent phase of the recorded flights. Moreover, these data are updated several times a day, so they need to be available for the actual time of recording. Since the absolute times of the flights were deleted to make the data anonymous, a correlation has been kept between the weather forecasts data and the flights, in order to be able to use the right data for the right flights. 3.3.3. Description of the output data This study aimed at showing the improvements that ADD could bring to the TP accuracy. For that matter, basic metrics were defined to quantify the TP accuracy. These metrics were evaluated twice for each flight stored in the input data: once with the standard TP, and once with the enhanced TP using the ADD.


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When all these individual results were computed, a statistical analysis was conducted. Statistical computations were carried out on the metric results to find out the global accuracy improvements that can be expected from the use of ADD in the TP. To better illustrate those achievements, graphical representations of the most relevant results were generated. 3.3.4. Description of the tools 3.3.4.1. Pre-processor

From the raw input data, a database of flight plan scenarios tailored to the TP input format was created. The nature of the required processing depends on the set of input data:

• Flight plan data: LFV provided this data by means of BFD/CFD messages that had to be converted into ICAO compliant EUROCAT input messages, to allow the flight plan creation and possible input of controller clearances (although such clearances are not desirable in the context of this study, they may have been present in some particular scenarios). This step was done automatically, but was followed by a manual analysis/update to allow crosschecking of RFL and flight route between information from BFD messages and track data.

• Track data: the relevant Automatic Position Reports (APR) have been extracted from the full track data and converted into the EUROCAT APR input format. E.g.: for time deviation on point P with a 5 min look-ahead time, an APR has been extracted from the track data at t(P)-5. An automated tool has been developed to handle this step: Once fed with the initial TP trajectory, the full track data, a list of evaluation points (defined either by their distance to threshold or their level) and a list of look-ahead times, the tool computes the required list of APR in the EUROCAT format.

• ADD data: Same problematic as track data. The relevant ADD messages have been extracted from the full ADD data and converted into an input format suitable for the EUROCAT. The tool designed to handle track data has been enhanced to handle ADD data as well.

• Weather forecasts data: LFV provided this data by means of GRIB messages, which are already supported by the EUROCAT.

These conversions were only needed once: The generated scenarios were replayable automatically, thus allowing several runs to be performed without any new manual processing. A filtering step was required during the scenario database generation to discard recorded flights that did not comply with the expected requirements on input data: non-CDA arrivals, presence of unexpected controller orders or clearances, lack of information to recreate the flight, etc. Given the wide range of possible reasons to filter out a flight and the moderate volume of expected data, such a step was handled manually. 3.3.4.2. Replay tool

Once a scenario database had been created, a tool was required to allow the automatic replay of these scenarios. Some separate Thales Air System tools were already available to handle flight plan creation (and input of controller orders), APR and GRIB replay. An integration of all these tools under a common interface was attempted, in order to enable the automatic replay of a scenario with no manual input. However, given some heavy architectural differences between the GRIB simulator and the other ones, its integration into a streamlined scenario player was problematic. As a


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consequence, these tools have only been used to conduct the first part of the study, i.e. tuning and evaluation of the TP without ADD and without GRIB data.

Figure 10 - Streamlined replay of FPL commands and APR

A new simulator was delivered to Thales Air Systems, which enabled the replay of scenarios containing flight plan commands, APR and GRIB messages. It was used to complete the tuning and evaluation with GRIB data. The last step was to enhance the new simulator in order to handle ADD data. This allowed the replay of a complete scenario, including all the required inputs: flight plan commands, APR, ADD, and GRIB messages. 3.3.4.3. Post-processor

3.3.4.3.1. Trajectories extraction

After a scenario has been replayed, the consecutive 4D trajectories computed by the TP need to be extracted to be later compared to the actual flown trajectory. To get the TP output data in the format expected by the next steps, an advanced parsing tool has been designed to extract the trajectories from the TP log file and reformat them on the fly. A similar tool has been designed to extract the actual flown 4D trajectory from the raw input track data, in the same format as the TP trajectories. The TP trajectories extraction occurs every time a scenario is replayed, whereas the actual trajectories extraction is only done once.


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3.3.4.3.2. Trajectories synchronization

Once the TP and actual trajectories of a scenario are available in the same format, they have to be synchronized to allow proper comparisons later on. Several data alignment processings are required to achieve such a result:

• Since the TP trajectory natively contains only the relevant points, whereas the track information is recorded on a regular time basis, interpolations are needed to have the same granularity between the actual and TP trajectories

• Depending on the kind of metrics to be computed, trajectories need to be aligned either on a time basis (4D info given as a function of time) or on a geographical basis (4D information given as a function of the 2D position, represented in 1D by the distance to threshold)

• Since the TP trajectory natively goes from take-off to landing, whereas the actual trajectory recordings are limited to the radar coverage area, TP trajectories need to be trimmed down to the same time range as the recorded actual trajectories. NB: Even though the recordings may cover more than the descent phase (i.e. part of en route), trimming the trajectories down to the descent phase should be avoided, since it may prevent evaluation of deviations on some points in case of long look-ahead times (ex: 20 min).

3.3.4.4. Metrics evaluation

The Microsoft Excel spreadsheet has been used to perform the trajectory comparisons, statistical analyses and graphic reports at the beginning of the study. Since such a tool includes easy to use and robust statistical and graphical engines, only the evaluation of basic metrics on trajectories required custom developments, allowing a quick start of the first part of the study. However, the necessity to transfer lots of data between the Unix environment of the test rig and the Windows environment of the analysis tools, combined with the inability to automate some time consuming tasks in the spreadsheet, led to the development of new analysis tools in the Unix environment (Figure 11). All trajectories comparisons, metrics evaluations, statistical computations and graphical presentations (charts and plots) are now automated through the use of custom tools.


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Figure 11 - Custom trajectories analysis tool


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4. TUNING OF THE TP 4.1. Aims of the Tuning According to the metrics defined in section 3, an evaluation of the baseline performance of the TP was conducted on the acquired flight data recordings. The tuning consisted in making the required adjustments to the TP in order to optimize this baseline performance. Given the design of the considered TP, two targets have been identified for possible improvements:

• The TP algorithms • The TP offline parameters

4.1.1. TP algorithms The TP to be used within this study was the new generation EUROCAT TP. This TP includes the most recent functional upgrades. Functionally, the new generation EUROCAT TP has implemented a complete aircraft model based on BADA 3.6. No modification to the TP algorithms as such were expected. Software defects identified in the course of the study were corrected. Furthermore, it has been agreed that no work will be done towards the improvement of current TP general algorithms. 4.1.2. TP offline parameters The main tuning task targeted the offline parameters used by the TP, in order to optimize the baseline performance and remove the possible biases that may exist in the default offline values. 4.2. Parameters to tune The offline parameters used by the TP (either directly, or indirectly through the associated route and constraint managers that feed the TP) can be grouped in three sets:

• Parameters related to the flight path extraction • Parameters related to the constraint definition • Parameters related to the aircraft performances

4.2.1. Flight path extraction data The 2D flight path used by the TP is computed by the route manager, based on several offline parameters: airways definitions, STARs definitions, ILS positions, etc. In order to ensure that lateral deviation is kept to a minimum, those parameters were adjusted to best match the 2D paths actually flown by the aircrafts in the recorded approaches. The foreseen approach was to align the offline parameters on the average flight path followed by the recorded flights NB: The results of this tuning step are specific to Arlanda. 4.2.2. Constraint definition data The list of 3D applicable constraints used by the TP is computed by the constraint manager, based on several offline parameters: ATC constraints, coordination level constraints, etc. Since


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the studied trajectories consist in Continuous Descent Approaches, some of the constraints defined in the current operational dataset were no longer applied in the special procedures used for those particular flights. When this happened, the offline constraint parameters were adjusted to reflect the operational procedures used for CDA. NB: The results of this tuning step are specific to Arlanda. 4.2.3. Aircraft performances This confidential information is presented in deliverable D6 Consortium Proprietary Annex A Section 1. 4.3. Tuning Methodology 4.3.1. Ordering of the tuning phases After analysis of the dependencies between the metrics used for the study and the sets of offline data, the chosen order to be followed to tune the TP offline parameters was:

1. Tuning of the 2D path data to minimize lateral deviation 2. Tuning of the offline constraints to minimize vertical deviation 3. Tuning of the aircraft performances to minimize both vertical and time deviations.

4.3.2. Tuning process For each phase, an iterative process was used, comprising the following steps:

1. Set the considered offline parameters to their initial values. 2. Run the TP on the whole flight database. 3. Compute the defined unit metrics on each recorded/predicted trajectory couple and

derive the associated global metrics across the whole flight database. 4. If the global metrics do not match the target accuracy criteria:

- analyse the results to find a possible bias - identify the probable source of the bias among the considered offline parameters - determine the required modifications to the identified parameters - go to step 2.

4.4. TP Tuning Results 4.4.1. Scope, Rationale and Objectives Section 4.4 describes the first step of the study which did not consider the ADDs: establishment of the baseline TP. This step was fundamental for the validity of the assessment of the benefits to be brought by the ADDs. This assessment needed baseline predictions, so that trajectory predicted considering ADDs can be compared to the baseline TP. To achieve these baseline predictions TP measurement were performed without ADDs.


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It is also important that the baseline TP be of the highest quality without ADDs, so that the benefits that will be observed when using the ADDs are really due to their use. Therefore some tuning was necessary to achieve this first step. The objective of the first step of the study was to establish a performance baseline of the used TP. To ensure the quality of this baseline, a tuning of the TP to the specific environment was also necessary. This section mainly presents the tuning because, once this tuning is done, the establishment of the baseline is merely the presentation of TP accuracy measures according to agreed metrics. The main tuning task targeted the offline parameters used by the TP (i.e. the TP Aircraft Performance Model), in order to optimize the baseline performance and remove the possible biases that may exist in the default offline values. Once the global metrics matched the target accuracy criteria, the tuning was considered achieved and a synthesis of the measurements could be performed. This first step of the study was supported by the part of the test rig depicted in the following figure.

Flight plan

Track

Weather

Scenario database

Pre - processor Replay tool

Post-processor

TP Engine

Input data Intermediate d ata Output data

Assessment applications developed Existing application to be assessed

TEST RIG

Predicted trajectories

without ADD

Graphical display of selected

indicators and trajectories

Figure 12 - Overview of the rig

4.4.2. Description of the collected data 126 flight recordings have been provided by LFV. A flight recording comprises several sets of data that all need to be available for this flight to be used in the study:

• Flight plan data: ADEP, ADES, route, RFL, TAS • Radar data: ARTAS recordings • GRIB data: weather predictions

Among the 126 flights received, some presented missing or invalid data in one or several of these sets of data:


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• Flight plan data:

o 61 flights with obsolete planned route (missing either DIRECT orders or STAR changes): Those problems can be circumvented by manual update of the flight plan data after analysis of the radar data.

o 20 flights with missed turns or other uncommon discrepancies in flown trajectory versus planned route: Those problems cannot be fixes by update of flight plan data.

• Radar data: o 31 flights with level-off whereas the study requires unconstrained flights (see

4.4.2.1) • GRIB data:

o 59 flights miss GRIB (see 4.4.2.2) The end result of the filtering step consists in 16 flights without any defect. To extend the statistical validity of the flight sample used for tuning and measurements, another 21 flights have been selected among those presenting incorrect flight plan data, whose planned route has been manually updated to reflect the flown 2D trajectory. A total of 37 flights have thus been selected for tuning and measurements. NB: The set of flights used for measurements should in principle be different from the set used for the tuning. However, given the limited number of usable flights, the whole set of selected flights has been used for both tuning and measurements.

4.4.2.1. Filtering of radar tracks

Among the 126 recorded flights, 31 flights have been discarded due to level-offs or other constraints impacting the vertical trajectory of the radar track. An example of such a flight, presenting several level-offs and a clearance near the Top of Descent, is shown on Figure 14.

1

17

21

22

10

242

GRIB pb

FPL pb

Radar pb

« Perfect » flights

11

16

Selected flights

Figure 13 - Distribution of data problems among recorded flights


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Figure 14 - Flight discarded due to vertical constraints

4.4.2.2. Importance of weather data

The TP used for the study is designed to use wind and temperature data to accurately predict the trajectory. These data are usually available through GRIB forecasts, which provide both wind and temperature forecasts every 6h. Among the 126 recorded flights, 59 flights have been discarded due to missing weather data. The following figures present the improvements in TP accuracy due to the knowledge of weather data, thus justifying that only flights with known weather data were taken into account.


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Figure 15 - Impact of weather data on vertical profile

Figure 16 - Impact of weather data on time profile


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4.4.3. Applied Tuning 4.4.3.1. Tuning of the Flight path extraction data

The reference set of offline data used for the evaluation is the operational dataset of the Eurocat system used in the Stockholm ATC center. It contains the definitions of all the named points, airways, SIDs and STARs, runways and airports used as offline inputs by the TP to determine the 2D path of a flight. The second source of data is the set of AFTN messages that are used as online inputs of the TP to create the flights. These were recorded in the Stockholm ATC center and provided by LFV as part of the input data. Each AFTN message contains the planned 2D route of one flight. Although they are not offline data, these inputs had to be tuned to reflect the potential discrepancy between the initially planned 2D route and the route actually flown, due to DIRECT orders or change of STAR that may happen after the reception of the AFTN message. 4.4.3.1.1. Reference off-line dataset

Table 1 summarizes the STARs used for CDA in Arlanda. Complete definitions are available in section 2.3.3.

Runway Entry point 26 19R 01L

HMR HAMMAR 1V HAMMAR 1N HAMMAR 1J TROSA TROSA 1V TROSA 1N TROSA 1J ELTOK ELTOK 1V ELTOK 1N ELTOK 1J XILAN XILAN 1V XILAN 1N XILAN 1J

Table 1 - Summary of CDA STARs

As could be expected from an operational dataset, almost none of the definitions did require a change. The only concern was linked to the definitions of three experimental STARs used for CDA to runway 19R: HAMMAR 1N, TROSA 1N and XILAN 1N, one point of which did not match the STAR definitions published in the related AIP Supplement. Table 2 indicates the number of recorded flights for each STAR.

Runway Entry point 26 19R 01L

HMR 50 5 1 TROSA 45 0 0 ELTOK 22 1 0 XILAN 1 1 0

Table 2 - Summary of STAR usage in recorded flights

Most of the recorded flights (94%) landed on runway 26. No tuning of the STAR definitions was necessary for runway 26: The maximum lateral deviation observed along the STAR paths


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among the selected flights is less than 0.5 NM. To illustrate this good adherence of the flown paths to the STAR definitions, the following Figure 17, Figure 18 and Figure 19 present the superimposed paths of the selected flights using STARs ELTOK 1V, HAMMAR 1V and TROSA 1V (too few data were available for XILAN 1V). The number of recorded flights landing on runways 19R (7 flights) and 01L (1 flight) was not sufficient to allow a proper tuning of the associated STAR definitions; even the validation of the official definition mentioned above was only possible for HAMMAR 1N.

Figure 17 - Arrivals using STAR ELTOK 1V


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Figure 18 - Arrivals using STAR HAMMAR 1V

Figure 19 - Arrivals using STAR TROSA 1V


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Since radar data confirmed the official definition, former point SA408 has been replaced by official point SA453 in the offline definitions of STARs HAMMAR 1N, TROSA 1N and XILAN 1N. A graphical illustration of this tuning can be found in Figure 20 for HAMMAR 1N; no data is available for TROSA 1N and XILAN 1N.

Figure 20 - Tuning of STAR HAMMAR 1N 4.4.3.1.2. AFTN flight plan routes

The recorded flights may be split into three sets, with a tuning action associated to each set: • Flights not diverging from their planned 2D route: No action to do (45 flights); • Flights following a route reproducible through DIRECT orders: The planned route may

be manually updated to reflect the flown route (61 flights); • Flights following a route not easily reproducible: The flight is discarded (20 flights).

An example of flight from each set is given on following Figure 21, Figure 22 and Figure 23.


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Figure 21 - Flight not diverging from its planned 2D route (lateral deviation < 0.2 NM)

Figure 22 - Flight following a route reproducible through DIRECT order


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Figure 23 - Flight discarded due to route discrepancy

For flights whose actually flown route could be reproduced from the initially planned route through DIRECT orders (21 flights among the 37 selected flights), the 2D route has been updated with DIRECT orders matching the route observed on radar data. Graphical illustrations of this tuning can be found in Figure 24 and Figure 25.


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Figure 24 - Example of AFTN planned route tuning (1)

Figure 25 - Example of AFTN planned route tuning (2)


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4.4.3.2. Tuning of the Constraint definition data

According to the Green Approach procedure as described by the operational staff of Stockholm-Arlanda, the ATC constraints present in the offline STAR definitions have been removed. Since this change was known beforehand, all the measurements have been done with the constraints removed, hence no before/after comparison is necessary. 4.4.3.3. Tuning of the aircraft performance parameters

This confidential information is presented in deliverable D6 Consortium Proprietary Annex A Section 2. 4.4.4. Achieved Baseline Performances (after Tuning) This confidential information is presented in deliverable D6 Consortium Proprietary Annex A Section 3. 4.5. TP Tuning Conclusions The baseline TP performances have been assessed for the B737-600 SAS aircrafts participating to the NUP II+ project and from which LFV has collected and provided ADDs. LFV collected data (without ADD) were used to measure and tune the TP input (e.g. Flight Plan) and offline parameters (e.g. STAR definition, Aircraft Performance Model) in order to obtain a TP baseline to be used later (when TP was “fed” with ADD parameters) as a reference measurement basis. The tuning was performed in three stages:

• 2D path tuning, consisting in correcting Flight Plan 2D modification not reflected in AFTN flight plans (e.g. direct) and offline parameter data (e.g. erroneous STAR data). After tuning, lateral deviation does not exceed 3 NM for the whole trajectory and 1 NM during the last 15 minutes of the descent;

• 3D ATC constraints tuning, consisting in adjusting the offline constraint parameters to reflect the way CDA approaches are flown in Stockholm-Arlanda. No tuning actually happened as LFV operational staff advised us beforehand to remove all ATC constraints related to Green Approach procedures used in Stockholm-Arlanda;

• Aircraft Performance tuning, consisting in tuning the Aircraft Performance Model Parameters so that the vertical and longitudinal profiles computed by the TP better match to the real collected radar trajectories. The tuning, focused on the descent part of the flight (i.e. from ToD to the runway threshold) here was also pretty minor: only one modification was done on a descent speed parameter (increased by 8% from 280 knots to 300 knots) of the BADA 3.6.

Measurements that served as reference without ADD were done based on two metrics (vertical deviation and time deviation expressed in mean value and standard deviation 20, 15, 10 and 5 minutes from the measurement points) applied to 5 measurement points selected during the descent phase (i.e. ToD, FL250, 40 NM and 10 NM from runway threshold, and runway threshold – no vertical deviation at runway threshold-).


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5. TP ADD ASSESSEMENT 5.1. Available ADD Parameters In the frame of the study, the list of available ADD parameters collected by LFV, and received from various sources, was:

• From Quick Access Recorder (QAR): o Press Altitude (Pressure Altitude) o TAS (True Air Speed) o SAT (Static Air Temperature) o TAT (True Air Temperature) o N1 Eng 1 (Turbine RPM for engine 1 in percentage of maximum throttle setting) o N1 Eng 2 (same as above for engine 2) o Ground Speed o Gross Weight o Fuel Flow Eng 1 o Fuel Flow Eng 2 o Latitude o Longitude o Flap Position o Gear Position o Speed Brake Handle Position o Wind Speed o Wind Direction o FMC (Flight Management Computer) Airspeed Selected o A/P (Auto Pilot) Pitch Engaged mode

• From ACARS:

o 4DTR 3 min before Top of Descent : latitude, longitude, altitude, time o 4DTR 40 min before threshold : latitude, longitude, altitude, time

In the future, ADD parameters may be received from other sources:

• From ADS-B:

o Latitude o Longitude o Flight level o Time o Geometric Altitude o Barometric Vertical Rate o Airspeed o Magnetic heading o Ground speed o Track angle o Velocity accuracy o TCP (Trajectory Change Point) 1 to 5 : latitude, longitude, flight level, time

• From ACARS:


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o Multiple and regular ACARS 4DTR updates along the 4D trajectory (only available for a few flights in the frame of ADAPT)

5.2. Description of Collected Data 5.2.1. Quantity of collected data 127 flights with ADD data have been provided by LFV. A flight comprises several sets of data that all need to be available for this flight to be used in the study:

• Flight plan data: ADEP, ADES, route, RFL, TAS • Radar data: ARTAS recordings • QAR data: onboard recordings of aircraft parameters • 4DTR data: 4D trajectory predictions from the FMS, downlinked through ACARS • GRIB data: weather predictions

Among the 127 flights received, some presented missing or invalid data in one or several of these sets of data:

• Radar data:

o 62 flights with vertical constraints, whereas the study requires unconstrained flights: either level-offs longer than 2NM, or constrained RoD on a part of the descent

o 1 flight with inconsistent data (wrong coupling)

• 4DTR data:

o 54 flights with outdated 4DTR arrival path (FMS planned path is different from actually flown path)

o 3 flights with (spurious) level constraints in 4DTR o 67 flights with incomplete 4DTR (beginning or end of the trajectory is missing)

• GRIB data:

o 27 flights miss GRIB


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The end result of the filtering step consisted in 10 flights without any “defect”. To extend the statistical validity of the flight sample used for measurements, another 10 flights have been selected among those presenting incomplete 4DTR data, since their problem impacts only the measurements related to the “4DTR” ADD (refer to 5.4.1.1.2). A total of 20 flights have thus been selected for measurements.

16

37

10

11

27

6 0

GRIB pb

4DTR pb

Radar pb

« Perfect » flights

10

10

Selected flights

Figure 26 - Distribution of data problems among recorded flights

5.2.2. Overview on collected ADD quality A given level of analysis was performed a priori on some data (preliminary analysis as part of deliverable D3 [5], and more detailed analysis as part of deliverable D5 [7]): e.g. the GPS positions were compared to the radar tracks and the result of this comparison showed that there was no interest to use this ADD in the TP computation (See section 5.4.2.2). The analysis of the ADD quality was also performed a posteriori on some others data (detailed analysis as part of deliverable D5 [7]): e.g. Comparison of the FMS prediction and of the radar tracks at threshold showed that about half of the 4DTR have an ETA (Estimated Time of Arrival) estimation that is more than 1min late, most of this delay appearing in the last 10 minutes of the descent. Other ADDs that did not show particular benefit (e.g. the Ground Speed) were not subject to investigation as the model and the reality more or less matched. Finally, due to the lack of comparison point it was not possible to assess the quality of some of the remaining ADDs: e.g. the takeoff weight (TOW deduced from the Gross Weight ADD) could not be compared to another actual reference1 (e.g. pilot reports prior takeoff).

1 The comparison done with the default TOWs of the aircraft performance model can not be considered as a

comparison between two measured sources of actual data (See section 5.4.2.3.1)


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.3. Used metrics and ADD Improvements Visualization To ensure maximum consistency throughout the study, the metrics used for measurements of the TP accuracy with ADD are the same as the ones used for measurements of the baseline TP accuracy without ADD (measurements presented thoroughly in Consortium Deliverable D2 [4], and summed up in the deliverable D6 Consortium Proprietary Annex A). Additional visualizations have been developed that provide quantified overviews of the vertical and longitudinal improvements for most of the studied ADD (see 5.3.3). 5.3.1. Vertical deviation The following points have been selected for vertical deviation evaluation:

• 10 NM from the runway threshold (location of the merge point between RNAV approaches to the runway)

• 40 NM from the runway threshold • crossing of FL250 • Top Of Descent

In the following figures presenting the results, the measurements of the vertical deviation are shown on each selected point with four different look-ahead times: 5, 10, 15 and 20 min. 5.3.2. Time deviation The following points have been selected for time deviation evaluation:

• The arrival runway threshold • 10 NM from the runway threshold (location of the merge point between RNAV

approaches to the runway) • 40 NM from the runway threshold • crossing of FL250 • Top Of Descent

In the following figures presenting the results, the measurements of the time deviation are shown on each selected point with four different look-ahead times: 5, 10, 15 and 20 min. 5.3.3. Visualization of ADD improvements To sum up the aforementioned results, one more plot presents the accomplished improvement in accuracy, using these rules:

• The horizontal axis presents, for all evaluation points and look-ahead times, the improvement on the mean error, by indicating how much closer the mean error has come to zero when using the ADD;

• The vertical axis presents, for all evaluation points and look-ahead times, the improvement on the standard deviation, by indicating how much closer the standard deviation has come to zero when using the ADD;

• Measurements performed at the same evaluation point share the same pictogram (ex: square on runway threshold);

• Measurements performed at the same look-ahead time share the same colour (ex: green at 20min).

This plot (an example of which can be found in Figure 27) provides a global view of the accuracy improvement, which may be estimated using the following guidelines:


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


• The best improvement is obtained on points located in the upper right part of the graph (both mean and standard deviation have been improved);

• The accuracy has worsened on points located in the lower left part of the graph (both mean and standard deviation have been worsened);

• Points located near the center of the graph show little or no improvement or worsening • The gathering of same colour points let us know if measurements with the same look-

ahead time behave similarly; • The gathering of same pictogram points let us know if measurements on the same

reference point behave similarly. 5.4. TP ADD Measurement Results (TP with ADD versus Baseline TP) Among all the available ADD, some have not been considered for several reasons:

• Redundancy with other information sources; ex: A/C type (already known from ICAO messages)

• Irrelevance for the study; ex: registration number • Impossibility to update the parameter online; ex: fuel flow (handled offline through the

BADA 3.6 model) The remaining parameters have been split into two priority groups, according to the level of improvement they were expected to bring to the TP accuracy. 5.4.1. Higher priority parameters The most important parameters to be assessed were:

• Trajectory predictions from the FMS • Instantaneous aircraft performances

Those parameters were expected to bring the biggest improvements to the TP accuracy. 5.4.1.1. Trajectory predictions from the FMS

The FMS trajectory predictions were available in the following form: • full 4D trajectories, down-linked 40 min before threshold and 3 min before TOD (from

ACARS) • and, for a few flights, multiple and regular ACARS 4DTR updates along the 4D trajectory

Several ways to integrate FMS trajectories in the ground TP have been studied:

• direct update, by overriding the TP predictions with the FMS predictions; this may have been the easiest implementation, but would have probably led to inconsistencies or instabilities along the whole trajectory

• indirect update, by deriving from the FMS predictions the changes in TP input data that are required to compute trajectory predictions compatible with the FMS predictions; ex: determination of an updated speed schedule to override the offline defined one.

Another set of ADD parameters were directly linked to the FMS trajectories: FMS modes information, i.e. FMC Airspeed Selected / A/P Pitch Engaged mode (from QAR). The actual usage of the above FMS trajectory predictions parameters is described in sections 5.4.1.1.1 and 5.4.1.1.2 below.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


In the future, further FMS trajectory updates might become available through next 5 TCP intents (from ADS-B). 5.4.1.1.1. FMS Modes

FMS modes information, i.e. FMC Airspeed Selected / A/P Pitch Engaged mode (from QAR) are directly linked to the FMS trajectories. These parameters could have been used to enable or disable the use of FMS trajectory predictions, whether these predictions were considered as reliable or not in the selected mode. However as stated above, the only FMS predictions available in the study time frame were “one shot” predictions that were downlinked early in the flight life, instead of regular predictions dowlinked all along the flight duration (this only happened for a few flights). Consequently, no update of the FMS trajectory was available after an FMS mode change, thus preventing this FMS mode change to be used in the software. 5.4.1.1.2. Full 4D trajectories and further trajectory updates

Full 4D trajectories were down-linked from the FMS twice during the flight life: 40 min before threshold and 3 min before TOD (from ACARS). It was found possible to get more trajectory updates per flights from the ACARS. These additional trajectory updates were provided for a few flights only and therefore after flight filtering only two of these “multiple trajectory updates” flights were exploitable. These two flights are treated in a separate section later in the document. In the future 4D trajectories could be further updated through next 5 TCP intents (from ADS-B). As indicated above, several ways to integrate the trajectories in the ground TP were foreseen:

• direct update, by overriding the TP predictions with the FMS predictions • indirect update, by deriving from the FMS predictions the changes in TP input data that

are required to compute trajectory predictions compatible with the FMS predictions

The “direct update” option has been discarded, due to the necessity to maintain continuous consistency between current trajectory and tentative trajectories (“WhatIf”) in the ground TP. To ensure such a consistency, the TP shall not only be able to output a trajectory similar to the FMS one in the current flight conditions, but it shall also be able to apply aircraft performances similar to the FMS ones in case of flight data modifications (ex: route change, new STAR allocation). In addition to the fact that the “indirect update” method allows “WhatIf” without having to request it from the onboard system (The usage of the onboard “WhatIf” would necessitate more sophisticated trajectory/constraint exchange mechanism and communication systems as planned in the future ATM system) the choice of the “indirect update” method was mainly driven by the fact that it offers a better robustness and maintains a certain level of independence of the ground trajectory prediction from the quality, availability and integrity of the processed ADDs. This, even though the TP ADD outputs would have been the same applying the “direct update” or the “indirect update” method (e.g. TP ADD outputs and the FMS trajectory matches whatever method is used).


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


The robustness of the “indirect update” method was proven by its capacity to process partial ADDs (e.g. missing part in the FMS trajectory in QAR data). The chosen method will also facilitate the future integration of ADS-B TCPs which will not necessarily cover the whole trajectory and/or may not be spread evenly along the flight path. Since the “indirect update” method is considered more robust to the variation of ADD quality and integrity, the ADD quality and integrity of the ADDs actually available in the study time frame were not directly assessed (This was also not the primary goal of the study). The proposed software implementation is thus based on the “indirect update” method. New TP requirements were defined as follow:

1. From the downlinked FMS trajectory, the TP shall be able to recompute an updated version of its aircraft performance model, used as input during trajectory computation.

2. Instead of being limited to performance model defined per aircraft type and airline operator in BADA 3.6, the TP shall be able to use a “per-flight performance model”, tailored to the flight-specific performance data acquired through ADD.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.1.1.2.1. Vertical deviation measurements

5.4.1.1.2.1.1. Overall ADD vertical improvement diagram

The usage of FMS trajectory information in the TP brought marked improvements, mainly in the vertical accuracy.

Figure 27 - Improvement in vertical accuracy (ADD 4DTR)

Instructions for proper understanding of Figure 27 are available in 5.3.3, and the resulting analysis can be found in 5.4.1.1.2.1.2. 5.4.1.1.2.1.2. Focus on specific vertical aspect

The altitude error at FL250 and ToD, for example, decreases from more than ±3000 feet to less than ±1000 feet. The main impacting factor was the much better ToD determination that resulted from FMS trajectory knowledge: The important altitude errors in baseline TP performance were due to inaccurate ToD determination, whereas FMS ToD determination was most of the time exact.

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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


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Figure 29 - Vertical deviation at Top of Descent


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


Note: Since the descent profile (from Top of Descent to ADES) is computed backwards by the TP, the vertical deviation at ToD is expected to be the same for all the look-ahead times. However, radar data was not always available 20min before ToD, due to radar range limitations. The resulting different sample sizes between the measurements at 20min and 15/10/5min look-ahead times explain the different vertical deviation results observed at ToD. The following figures show examples of improved ToD determination through the knowledge of FMS trajectory prediction, whether the ToD happens earlier or later than expected.

Figure 30 - Earlier ToD correction in TP Figure 31 - Later ToD correction in TP


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.1.1.2.2. Time deviation measurements

5.4.1.1.2.2.1. Overall ADD longitudinal improvement diagram

Longitudinal accuracy was clearly improved on the runway threshold.

Figure 32 - Improvement in longitudinal accuracy (ADD 4DTR)

Instructions for proper understanding of Figure 32 are available in 5.3.3, and the resulting analysis can be found in 5.4.1.1.2.2.2. 5.4.1.1.2.2.2. Focus on specific longitudinal aspect

The baseline TP results showed that the landing speed derived from BADA 3.6 was too low for this aircraft type, but that could not be fixed through tuning because this speed was defined using parameters common to all aircraft types. The usage of FMS-derived speeds freed the TP from this dependency to global aircraft performance model parameters, allowing more accurate time predictions in the last part of the descent.

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Figure 33 - Time deviation at runway threshold

Note: A positive bias (which means that TP predicted ETA is late compared to ATA) of nearly 30s may be observed on the measurement results with 4DTR as ADD. This bias is present in the input FMS predictions: About half of them have an ETA estimation that is more than 1min late, most of this delay appearing in the last 10 minutes of the descent. Figure 34 presents an example of such a late ETA prediction by the FMS, where ETA is greater than ATA by 2min 30s, and where the delay only appears at the end of the descent phase.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


Figure 34 - Late ETA prediction by the FMS

5.4.1.1.2.3. Impact of regular 4DTR updates

A small set of flights was obtained just before the end of the measurement phase, including regular FMS trajectory downlinks, every 2 or 3 minutes, instead of only two downlinks for the previous flights. This allowed us to measure the TP performances with an up-to-date knowledge of the FMS trajectory, by using the latest downlinked trajectory at the time of prediction of each selected horizon. Among the 11 received flights, 6 flights missed radar and flight plan data, and 3 flights among the remaining ones presented level-offs in the radar track. Consequently, only 2 flights have been selected for the measurements. The following figures present the improvement between the knowledge of a single FMS trajectory downlinked before the descent started (plotted in blue), and the knowledge of up-to-date FMS trajectories downlinked all along the descent phase (plotted in red). Only significant results are shown hereafter: The plots not shown presented no notable impact of multiple 4DTR knowledge on the TP accuracy. A slight improvement can be noticed both in vertical and longitudinal accuracies, but the size of the sample cannot be considered statistically significant: More flights should be studied to confirm the trend noticed in those multiple 4DTR measurements.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.1.2. Instantaneous aircraft performances

Some instantaneous aircraft performances were available in the following form: • TAS (from QAR) • Ground Speed (from QAR).

These instantaneous measures of the aircraft performance have been used as updated values of the offline defined ones. Several implementations were initially envisaged:

• Update limited to the current trajectory segment; low error risk, but low expected improvement of long term predictions

• Update extended to all trajectory segments in the same altitude layer; possibly higher improvement of long term predictions, but with higher error risk.

The current TP architecture uses a flight speed schedule which is offline defined per aircraft type and airline operator. The schedule indicates, for each altitude layer, the constant CAS (Calibrated Air Speed) or Mach to be followed. During TP computations, this CAS/Mach is first converted into a TAS (using the appropriate level), and then into a ground speed (using the available forecast wind information provided to the system in GRIB format). From this design, we proposed adapted solutions to integrate immediate TAS or ground speed knowledge into the TP computations. The actual usage of the above speed parameters is described in sections 5.4.1.2.1 and 5.4.1.2.2 below. In the future the following aircraft performances values might become available:

• Airspeed, groundspeed (from ADS-B) • ROCD (from ADS-B barometric vertical rate)

5.4.1.2.1. TAS

Given that: • the CAS (Calibrated Air Speed) can easily be deduced from the TAS with the

knowledge of the current flight level, • the current flight level is generally known with a small margin of error, • the CAS is expected to be constant on long periods,

The proposed implementation for instantaneous TAS usage in TP is the following:

1. Compute CAS from TAS and current level. 2. Update the speed schedule of the flight by using the computed CAS instead of

the offline defined CAS. This implementation expected to take advantage of the expected constancy of the computed CAS to allow the use of the ADD on more than just the current trajectory segment, thus maximizing the expected improvement in long term trajectory predictions. However, the constant CAS hypothesis proved to be wrong (with the available sample of date). This is explained in the final result section below.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.1.2.1.1. Vertical deviation measurements


Impact of TAS usage on the vertical accuracy was negligible, as expected a priori.

Figure 37 - Improvement in vertical accuracy (ADD TAS)


TAS is a longitudinal parameter that does not directly impact the vertical profile. An example at 10 NM from threshold is provided below.

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Impact of TAS usage on the longitudinal accuracy was noticeable, but it did not exhibit any tangible improvement.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


Figure 39 - Improvement in longitudinal accuracy (ADD TAS)


The TAS does not exhibit any tangible improvement and in some cases slightly degrades the longitudinal performance (see 10 NM from threshold example below).

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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


The fact that in some cases the TAS slightly degrades the longitudinal performance can be explained by relating the observed CAS profiles of the flights to the expected aircraft performance model CAS profile. The aircraft performance model models the descent speed profile through several constant CAS phases across fixed level layers, whereas the CAS profiles determined from ADD TAS recordings do not always follow this model: They may either present non-constant CAS segments, or constant CAS segments whose level layer boundaries do not match the aircraft performance model ones, or both. Consequently, the update of the aircraft performance model speed profile with the current CAS value does not necessarily result in a speed profile closer matching the real one. An example of speed profile not following the aircraft performance model speed profile model is given in Figure 42.

Figure 42 - CAS profile diverging from aircraft performance model

5.4.1.2.2. Ground speed

Given that: • the TAS can only be deduced from the ground speed with a precise knowledge of the

current wind data, • the current wind data is generally known with a big margin of error, since it comes from

forecasts made hours ago, The proposed implementation for instantaneous ground speed usage in TP is the following:


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


1. On the current trajectory segment, replace the TP ground speed (computed from offline CAS and wind forecasts) with the ADD ground speed.

Learning from earlier experiments by Thales, this implementation limits the usage of instantaneous ground speed to the current trajectory segment, thus minimizing the risk of propagating a performance value that is known not to be stable. 5.4.1.2.2.1. Vertical deviation measurements


Impact of Ground Speed usage on the vertical accuracy was “non-existent”, as expected a priori.

Figure 43 - Improvement in vertical accuracy (ADD GS)


Ground Speed is a purely longitudinal parameter that does not impact the vertical profile.

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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008




Ground Speed usage exhibited a slight improvement in longitudinal accuracy on all the evaluations points.

Figure 45 - Improvement in longitudinal accuracy (ADD GS)


The slight improvement provided by the Ground Speed is in line with the impact foreseen from the implementation: The ADD value only updates a small portion of the TP trajectory, so only small improvements could be expected.

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5.4.2. Lower priority parameters The remaining parameters of interest were related to:

• Weather information: temperature, wind speed/direction (from QAR) Those parameters provide measurements of the weather conditions in the vicinity of the aircraft, that was used instead of the global weather forecasts from GRIB data and improve short term predictions.

• Position information: latitude, longitude, altitude (from QAR) Those parameters provide position information that may have been more accurate than the radar track positions.

• Weight information: gross weight (from QAR) This parameter was used to evaluate the takeoff weight, which is already an input of the TP. Several ways could have been studied to derive the TOW from the instantaneous weight, possibly based on the elapsed distance, elapsed time, or ADEP.

• Aircraft state information: flap position, gear position, speed brake handle position, throttle (i.e. N1 Engine 1 and 2) (from QAR) Those parameters may have been used to activate specific speed laws, if they were found to be reliable indicators of situations where the default performance model is not accurate enough.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


The actual usage of the above low priority parameters is described in sections 5.4.2.1, 5.4.2.2, 5.4.2.3 and 5.4.2.4 below. In the future the position information might become available from ADS-B (latitude, longitude, flight level, time), and may be assessed as what was done for the position information issued from QAR (See section 5.4.2.2). 5.4.2.1. Weather

The proposed new requirements are the following:

1. Instead of being limited to airspace-based temperature and wind tables common to all flights, the TP shall be able to use per-flight temperature and wind tables, tailored to the flight-specific weather data acquired through ADD

2. Per-flight temperature and wind tables contain flight-specific weather data in airspace cells where these data are available, and default to weather forecasts common to all flights in the other airspace cells


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.2.1.1. Vertical deviation measurements

5.4.2.1.1.1. Overall ADD vertical improvement diagram

Impact of local weather data usage on the vertical accuracy is barely noticeable, and did not exhibit any tangible improvement.

Figure 47 - Improvement in vertical accuracy (ADD Weather)

Instructions for proper understanding of Figure 47 are available in 5.3.3, and the resulting analysis can be found in 5.4.2.1.1.2. 5.4.2.1.1.2. Focus on specific vertical aspect

The following diagram shows that at 40 NM from threshold the impact of local weather data (i.e ADD Weather vs GRIB data) is insignificant (a few feet of difference).

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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.2.1.2. Time deviation measurements

5.4.2.1.2.1. Overall ADD longitudinal improvement diagram

Impact of local weather data usage on the longitudinal accuracy is barely noticeable, and did not exhibit any tangible improvement.

Figure 49 - Improvement in longitudinal accuracy (ADD Weather)

Instructions for proper understanding of Figure 49 are available in 5.3.3, and the resulting analysis can be found in 5.4.2.1.2.2. 5.4.2.1.2.2. Focus on specific longitudinal aspect

The following diagram shows that at 40 NM from threshold the impact of local weather data (ADD Weather vs GRIB data) is insignificant (a few seconds difference).

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Figure 50 - Time deviation at 40NM from threshold

The quasi-inexistent impact of weather on prediction can be explained by comparing the predicted and recorded weather profiles of the flights: temperature and wind forecasts that are available to the TP (from GRIB data issued 6h earlier) provide information that is accurate enough not to expect marked improvements from instantaneous local recordings. The following figures present the predicted (plotted in red) and recorded (plotted in green) temperature and wind profiles of one of the flights, in order to illustrate the quality of available GRIB weather forecasts.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


Figure 51 - Predicted and recorded temperature profiles of a flight

Figure 52 - Predicted and recorded wind direction profiles of a flight


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


Figure 53 - Predicted and recorded wind module (speed) profiles of a flight


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.2.2. Position

The 2D position of the aircraft (latitude and longitude) was available as ADD. This parameter provided GPS-based position information that may be more accurate than the radar track positions. Since the TP was already able to update its trajectory with position information acquired from radar data, no software modification would have been necessary: Feeding the TP with QAR position information instead of radar position information would have allowed us to study the impact of QAR data vs radar data. However, preliminary evaluation of available position data showed a very small discrepancy between radar and QAR information. So the impact study was proposed to be skipped. However this is the object of a detailed explanation in the final result section below. No measurement of the impact of GPS position on TP accuracy has been performed due to the results of a preliminary evaluation: Available position data show a very small discrepancy between radar and GPS information. The following table sums up lateral deviation results on the flights selected for evaluation.

Flight Mean Standard deviation Max F304 0.019 0.017 0.183 F306 0.021 0.017 0.118 F327 0.010 0.012 0.091 F330 0.014 0.013 0.109 F357 0.013 0.016 0.148 F359 0.019 0.015 0.106 F362 0.012 0.013 0.132 F364 0.011 0.016 0.174 F365 0.013 0.019 0.201 F368 0.014 0.016 0.139 F371 0.019 0.018 0.155 F388 0.012 0.015 0.140 F389 0.015 0.016 0.120 F407 0.012 0.013 0.107 F416 0.012 0.014 0.138 F418 0.010 0.013 0.150 F438 0.011 0.014 0.141 F445 0.016 0.024 0.187 F453 0.015 0.016 0.144 F459 0.012 0.013 0.131

Table 3 - Lateral deviation (in NM) between ARTAS and GPS positions

2D positions average difference is less than 0.02 NM on evaluated flights. Such a small difference would not bring any tangible improvement in TP accuracy.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.2.3. Weight

The instantaneous gross weight was available as ADD. This parameter was used to evaluate the takeoff weight (TOW), which is an input of the studied TP. 5.4.2.3.1. Preliminary analysis

The first series of results that were obtained, using the tuned aircraft performances, exhibited a negative impact of the TOW on the TP accuracy. This can be explained with the following facts:

• Tuning (without TOW) increased descent speed (CAS) to increase Rate of Descent (RoD) above 10,000 feet

• Higher descent CAS and lower TOW both have the same effect on descent profile above 10,000 feet, that is: a higher RoD

• TOW analysis exhibits lower average TOW than default TOW (refer to Table 4)

Flight TOW (tons) F304 52.5 F306 50.5 F327 54.9 F330 47.5 F357 50.8 F359 51.8 F362 51.9 F364 53.8 F365 48.1 F368 50.1 F371 53.2 F388 55.4 F389 46.7 F407 52.4 F416 51.5 F418 49.3 F438 53.9 F445 50.7 F453 53.2 F459 53.4

Average 51.6 Default for B737-600

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default

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Table 4 – Takeoff weight summary

A posteriori, the knowledge of actual TOW indicated that the tuning could have consisted in decreasing the default TOW, instead of (or along with) increasing the descent speed. When the TP is using both the tuned aircrafts performances and the ADD TOW, their combined effects lead to the resulting Rate of Descent being too high above 10,000 feet, as illustrated by


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


Figure 54. As a consequence, the impact of the TOW has been re-evaluated against a baseline TP accuracy measured with the default aircraft performances, not the tuned ones: Those are the results presented hereafter.

Figure 54 - Impact of CAS and TOW on Rate of Descent above 10k feet


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.2.3.2. Vertical deviation measurements

5.4.2.3.2.1. Overall ADD vertical improvement diagram

Impact of TOW usage on the vertical accuracy was marked on evaluation points located at or above 10,000 feet. One should keep in mind that this vertical improvement is relative to a non-tuned TP, as explained in 5.4.2.3.1.

Figure 55 - Improvement in vertical accuracy (ADD TOW)

Instructions for proper understanding of Figure 55 are available in 5.3.3, and the resulting analysis can be found in 5.4.2.3.2.2. 5.4.2.3.2.2. Focus on specific vertical aspect

Accuracy is notably improved at 40 NM from threshold, FL250 and ToD. One should keep in mind that these vertical improvements are relative to a non-tuned TP, as explained in 5.4.2.3.1.

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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.4.2.3.3. Time deviation measurements

5.4.2.3.3.1. Overall ADD longitudinal improvement diagram

Impact of TOW usage on the longitudinal accuracy was noticeable only at runway threshold, where the accuracy is actually degraded.

Figure 59 - Improvement in longitudinal accuracy (ADD TOW)

Instructions for proper understanding of Figure 59 are available in 5.3.3, and the resulting analysis can be found in 5.4.2.3.3.2. 5.4.2.3.3.2. Focus on specific longitudinal aspect

The TOW negative impact noticeable at runway threshold is explained by the following facts: • Average ADD TOW is lower than default TOW • Lighter aircrafts have a lower landing speed • Landing speed is already too low in the baseline TP performance (that could not be fixed

through tuning because this speed is defined using BADA 3.6 parameters common to all aircraft types).

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5.4.2.4. Aircraft state

A review of the current TP design conducted Thales to conclude that those parameters cannot be used without heavy architectural changes. The computations using aerodynamics and thrust data are done offline. Transferring these computations into the online TP would require too much work to be completed in the course of this study. Consequently, it was not possible to evaluate the impact of these parameters in the frame of the ADAPT study. It was agreed that implementation of aircraft state parameters goes beyond the scope of ADAPT project. This remains a possible issue to be addressed in future studies.


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


5.5. TP ADD Measurement Conclusions From the TP ADD accuracy measurements, we could divide the analyzed ADD into three groups, given the improvements they brought to the TP accuracy:

• Notable improvements: o FMS trajectory (either single or multiple), which brought improvement in both

vertical and longitudinal accuracies o Takeoff weight, which brought improvement in vertical accuracy; this result

should however be confirmed by further studies, since it was obtained here relative to a non-tuned TP (see 5.4.2.3.1).

• Slight improvements: o Ground speed, which brought improvement in longitudinal accuracy

• No noticeable improvement: o TAS o Weather data o GPS position

From the baseline TP performance assessment the main areas where accuracy improvements were needed were:

• Top of Descent determination, to improve vertical accuracy: This parameter was improved through knowledge of FMS trajectory and TOW

• Landing speed, to improve longitudinal accuracy: This parameter was improved through knowledge of the FMS trajectory and the ground speed (short term only)

The present results should be confirmed on a larger scale:

• more flights, to increase the statistical validity of the results • more aircraft types, to analyze the accuracy of predictions from other FMS • other environments: different flight phase (climb), different airport and approach

procedures. 6. ACHIEVEMENTS, LIMITATIONS AND RECOMMENDATIONS The work that have been realized in the frame of this study brought the following positive achievements:

• The setting of a method and of an appropriate test suite to assess the performance of the Thales “state-of-art” TP 4D based on a EUROCONTROL aircraft performance model

• The application of the above method and test suite to assess and tune the baseline TP performance as well as to measure and quantify the benefit of individual ADD.

• The Thales TP was upgraded to process available ADD parameters (in particular 4D FMS trajectories)

• The TP ADD measurement showed promising results, in particular in the vertical plan for a better TOD determination through the usage of the 4D FMS trajectories

• The comparison of TP ADD versus Baseline TP measures showed that to optimize the overall TP performances: • Tuning of the Baseline TP off-line parameters (without ADD) may have to be revised

once ADD are being used (e.g. processing of the Take-off weight ADD) • Baseline TP on-line process principles and algorithms may have to be refined and

enhanced However, the present study presented some limitations:


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


• By definition of the TRS scope, the baseline TP tuning and TP ADD measures were

limited to one category of aircraft (B737 NG), in the descent phase of flight (Approach and Landing) for flights performing CDA procedures (I.e. unconstrained flights)

• The ADD used were limited to those present in the QAR onboard recording and to the 4D Trajectories received via the ACARS (e.g. ADS-B data, and especially ADS-B intents were not available in the study time frame)

Based on the above achievements and limitation, it is recommended to extend the usage of the test suite and of the upgraded TP to a larger scale, e.g. :

• To assess the impact of TP ADD on the overall ATC System, extend the test suite to other operational components and notably controller tools. This will support the definition of minimum TP performance standards that may be required by these operational components: Add operational components that depend on TP 4D performances (i.e. components indirectly impacted by the ADD). For example controller tool like MTCD 4D (Medium Term Conflict Detection), to assess the induced impact of ADD on these operational components

• Process more data: • Get and process more flight samples to increase the statistical validity of the

measures and results • Get and process new ADDs (e.g. FMS mode) to assess the benefit of using these

new ADDs for profile computation individually (as done in ADAPT), and then in combination with other ADDs (see below)

• Process ADD combinations (e.g. True Air Speed & Takeoff weight) to assess the benefit (or the drawback) of using such or such combination of ADDs for profile computation

• Process more aircraft/FMS types (e.g. A330/340 with Honeywell FMS) to assess the quality, the integrity and the consistency of ADDs across various onboard architectures, used on different airframes, and hence to quantify further the ADD benefit for profile computation. This will support the definition of minimum ADD performance standards that may be required to use the ADD in ground trajectory computation.

• Include other phases of flight to extend the TP upgrades to the whole TP 4D computation (e.g. Climbing phase)

• Include other types of operation (e.g. constrained flights, not CDA only) to include more realism in the measures (CDA operations are very limited nowadays)

• Based on the knowledge got from the analysis of TP ADD versus Baseline TP measures, refine and enhance the baseline TP tuning and/or algorithms, in order to enhance the TP precision while maintaining a relative independence of the ground trajectory prediction from the quality, availability and integrity of the processed ADDs. For example, this may ultimately conduct to the development of new ground TP based on on-line total energy model (e.g. on-line BADA model) and/or adaptive TP (i.e. TP that gradually “tunes” it-self on-line with the constant acquisition and knowledge got from ADDs, weather information, etc.).


Ref.: ADAPT / D6-V1.0 Date : 08/02/2008


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Ref.: ADAPT / D6-V1.0 Date : 08/02/2008

D6: Final Report ADAPT Consortium Proprietary Page : A0

ANNEX A : Additional Tuning Information

Consortium Proprietary Annex

(available as separate document)

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