Advanced Aerospace Technologies

Research Seminar Intelligent Transport Systems Research Group – Aviation Team

Next Generation ATM & Avionics Systems: Enabling Four-Dimensional Trajectory and Intent Based Operations Melbourne, 2nd February 2015 Assoc. Prof. Roberto Sabatini PhD, FRIN, SMAIAA, SMIEE, MRAeS, MCGI Aviation Team Leader and Head of ITS Research Group School of Aerospace, Mechanical and Manufacturing Engineering Office: +61 3 9925 8015 Mobile: +61 457 126 495 Email: [email protected]

Sir Lawrence Wackett Research Centre

About the presenter...  Assoc. Prof. Roberto Sabatini – Aviation Team Leader

– SLWARC – ATM & Avionics Topic Leader – Head of the Intelligent Transport Systems Research Group

 25 Years of Experience in the Aviation/Aerospace Industry and in Academia  16 Years Flight Test Engineer and Avionics Instructor (ITAF/R&FTC)

 5 Years R&D Manager (USA) and Airworthiness/Acquisition Manager (IT)  3 Years Lecturer in Aviation Electronic Systems (Cranfield University, UK) and Program Manager for the EC Clean Sky Joint Technology Initiative for Aeronautics and Air Transport – Systems for Green Operations (SGO) ITD  Visiting Professor at Technical University of Turin (Italy) and Chosun University (South Korea). Teaching aviation courses in Singapore and in China (Nanjing University of Aeronautics & Astronautics)  2000+ FH on 18 different aircraft types, FTE, APPL, MPL, EWO, WEO © RMIT University

Research Seminar – Assoc. Prof. Rob Sabatini

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Seminar Outline  RMIT Aviation Team Overview – Team Composition

– Research Concentrations – Research Network – Research Activities

 Air Traffic Management (ATM) and Avionics Systems Research

 Next Generation ATM & Avionics (CNS+A) Systems for 4D Intent Based Operations  Future Research Time-Based Operations Trajectory-Based Operations Performance-Based Operations © RMIT University


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RMIT Aviation Team


Aviation Team Capabilities  Research Concentrations: – ATM & Avionics Systems – Aviation Business Models – Human Factors Engineering – Airport Greening Technology

– Aircraft Systems for Green Operations – Sustainable Lifecycle Management and Logistics

 8 Permanent Staff Members: 2 Associate Professors, 2 Senior Lecturers, 1 Senior Research Fellow, 2 Lecturers, 1 Research Fellow

 11 HDR Candidates: 9 PhD Students and 2 MRes Students  2013-2014 Master and Bachelor Students: 37 FYP and 5 Internships  2013-2014 ERA Publications: Over 100 publications  Integrated in the Intelligent Transport Systems Research Group in 2014 © RMIT University


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Research Network  Main Industrial and Government Partners –

Australian Government: Airservices, CASA, DSTO, CSIRO, DMTC, VIC Ministry of Aviation Industry

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Airlines and Operators : QANTAS and Express Freighters Australia

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Aircraft Manufacturers: Airbus, Boeing, Embraer and Aerosonde

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Aero Systems Companies: Thales Australia, BAE Systems, Flight Data Systems, Advea and Selex-ES

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International Organisations: European Commission REA/ASN, NASA, JAXA, CIRA, NLR, DLR, ROKAF, US-DoD, IT-MoD, ITAF, DARPA, AORAD/AFRL

 Main Academic Partners –

University of Nottingham and Cranfield University (UK)

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Polytechnic University of Turin and University of Rome (Italy)

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University of Melbourne, Swinburne UoT, Queensland UoT (Australia)

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Technical University of Braunschweig (Germany)

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Nanjing University of Aeronautics and Astronautics (China)

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Chosun University (South Korea)

© RMIT University


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Multidisciplinary Research Methodology Science Navigation/Piloting, Human Factors, Environmental Science, Meteorology, etc.

Technology

Management

Aircraft, Airport and Air Traffic Management Systems

Airline Business Models, Flight Ops and Airport Mgmt

© RMIT University


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Research Activities  Strategic Research Focus: Improving Aviation Safety, Efficiency and Environmental Sustainability  Research Areas – Improved efficiency and capacity of airports – ATM Communications, Navigation, Surveillance (CNS) & Avionics (A) Systems (CNS+A) – Cost-effective through-life support of new and ageing aircraft – Uptake of low emission technologies and alternative fuels (including bio-fuels) – Best practice processes and solutions for enhanced aviation safety and security

© RMIT University


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Sustainable Aviation Research ATM Systems and Procedures

Aircraft Vehicle, Systems and Flight Ops

© RMIT University


Airport Design and Operations

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Sustainable Aviation Research Aircraft Technologies  Weight gains: • Lighter materials and structures. • Use of composites and new metals to reduce mass.

 Aerodynamic gains: • Novel aircraft shapes and architectures (e.g., blended wing, flying wing, morphing wings, smart high-lift devices).

 Gaseous emission gains: • Novel propulsion systems (e.g., high bypass ratio, open rotor, distributed propulsion). • Bio-fuels / Hydrogen / sustainable fuels.

 Noise emission gains: • flight paths / new engines / configurations.

© RMIT University


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Sustainable Aviation Research Aircraft Technologies (cont.)  Energy management gains: • Evolution of aircraft systems to increase fuel economy and reduce spurious off-takes from the engine to improve engine performance. • This includes the on-board fuel system and how it controls and manages fuel efficiently. • The most appropriate choice of hydraulic, electrics and bleed air as a source of power for systems actuation and the impact on engine off-take.

 Operational gains: • Next generation avionics systems connected with highly automated groundbased CNS/ATM systems (Network-centric ATM).

• The future aircraft are “moving” nodes in a network with Pilots and ATCO’s providing high-level decision making. • 3D and 4D trajectory optimisation in the presence of PBN requirements • Prognostics and health monitoring – Systems that detects degrading performance of the aircraft systems and can be used to predict failures and poor performance to improve maintenance planning. © RMIT University


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Sustainable Aviation Research Airport Design and Operations  Airport-level noise mitigations • Reductions at source. • Land use planning and management. • Noise abatement operational procedures. • Airport operating restrictions (limiting access to given airports). • Noise Charges.

 Air quality and climate change mitigations • Air quality problem: − Particulate matter (PM)/smoke. − Nitrogen oxides (NOx). − Unburned hydrocarbons (UHCs). − Ozone (O3). − Carbon monoxide (CO). © RMIT University


Direct health effects

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Sustainable Aviation Research Airport Design and Operations (cont.)  Air quality and climate change mitigations • Measuring air quality and assessing its impacts − Concentration-response functions (CRFs), taking into account concentration levels and exposure times to observe health response (e.g, epidemiological studies). − Air pollution sensors (ground/airborne) and emission models. − Computer models for emission and dispersion (e.g., ICAO, FAA, EUROCONTROL and UK DoT models). • Possible mitigations: − Operational procedures (e.g., APU use limitations, restrictions on engine run-up for test, restrictions on thrust reverse). − Emission charges. − Airport authority policies (e.g., cleaner ground transportation, highoccupancy, hybrid and electric vehicles).

© RMIT University


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Sustainable Aviation Research Airport Design and Operations  Airport water quality impact mitigations • De-icing fluids amount, collection and disposal • Fuel and other chemical leaks and spills reductions (reliable storage and distribution, secondary containment and cleanup procedures) • Stormwater runoff

 Airport wildlife impact mitigations • Reducing wildlife dangers to aircraft and vice versa • Vegetation management • Controlling the establishment of landfills and waste disposal sites • Airport fencing

• Animal distress calls • Loud sounds, chemical repellents

© RMIT University


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Sustainable Aviation Research Air Traffic Management  Development of innovative ground-based and airborne Communications, Navigation and Surveillance (CNS) systems needed to enable the 4DTrajectory (4DT) optimisation, negotiation and validation features required in the future ATM context.  Current research addresses the design of innovative ground-based ATM system for 4DT Planning, Negotiation and Validation (4-PNV) with the Next Generation of Flight Management Systems (NG-FMS).

 The 4-PNV system receives multiple options of 4DT intents from each aircraft equipped with NG-FMS. These 4DT intents are based on performance weighting adhering to uplinked airspace constraints and meeting the operational objectives by enhancing the economic efficiency, environmental sustainability and minimising disruptions caused by unexpected events. © RMIT University


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Sustainable Aviation Research Air Traffic Management (cont.)  These novel ATM systems validate the aircraft trajectory intents by implementing adequate separation and flow optimisation methods, establishing an optimal and safe solution for each aircraft.

 The overall aim is to enhance the efficiency and effectiveness of ATM by increasing the level of automation (negotiation and validation schemes) enhancing the decision making process, improving operations efficiency and safety.

© RMIT University


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Experience in Sustainable Aviation Research • Deliver active wing technologies and new aircraft configuration for breakthrough, new products.

• Low-weight aircraft using smart structures, • Low external noise configurations and • Integration of technology developed in other ITDs, such as engines, energy management and new system architectures. Co-led by: Alenia and EADS-CASA

• Design and build five engine demonstrators to integrate technologies for low noise and lightweight low pressure systems, high efficiency, low NOx and low weight cores and novel configurations. Co-led by: Rolls-Royce and Safran

Evaluation platform: Assesses the environmental impact of the technology developed by the ITDs.

Co-led by: Airbus and SAAB

• Innovative rotor blades and engine installation for noise reduction, • Lower airframe drag, • Integration of diesel engine technology and • Advanced electrical systems for elimination of noxious hydraulic fluids and fuel consumption reduction. Co-led by: Eurocopter and AugustaWestland

Co-led by: DLR and Thales

• Focus on more/all-electrical aircraft equipment and systems architectures, thermal management, capabilities for "green" trajectories and mission and improved ground operations to give any aircraft the capability to fully exploit the benefits of the Single European Sky.

• Focus on green design and production, withdrawal, and recycling of aircraft, by optimal use of raw materials and energies thus improving the environmental impact of the whole products life cycle. Co-led by: Dassault and Fraunhofer Institute

Co-led by: Thales and Liebherr

© RMIT University


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Experience in Sustainable Aviation Research Clean Sky SGO Management of Trajectory and Mission (MTM)

 Aircraft will be able to fly greener trajectories throughout the various flight phases, thanks to technologies which allow a reduced fuel consumption, avoidance of meteorological hazards and tailoring of the flight path to known local conditions.  The silent and agile aircraft will generate a reduced noise footprint during departure and approach, with significant benefits for the population.  The synergies between Clean Sky and SESAR are exploited in the MTM domain. © RMIT University


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Experience in Sustainable Aviation Research

© RMIT University


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Experience in Sustainable Aviation Research Clean Sky – SGO ITD Department of Aerospace Engineering and Department of Power & Propulsion Dr Roberto Sabatini PM WP3.1 and WP3.2 CNS/ATM Research Team Leader E: [email protected] T: +44 1234 75 8290

Greener Aircraft Trajectories under ATM Constraints (GATAC) Aircraft will fly greener trajectories throughout the various flight phases, thanks to technologies which allow a reduced fuel consumption, avoidance of meteorological hazards and tailoring of the flight path to known local conditions. The silent and agile aircraft generates a reduced noise footprint during departure and approach

© RMIT University


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Experience in Sustainable Aviation Research Dr Roberto Sabatini PM Clean Sky SGO WP3.1 and WP3.2 CNS/ATM Research Team Leader E: [email protected] T: +44 1234 75 8290

Airborne Laser Systems for Atmospheric Sounding in the Near Infrared Innovative Technique for Atmospheric Propagation Measurements, allowing a direct determination of atmospheric extinction, and, through suitable inversion algorithms, the indirect measurements of important natural and man-made atmospheric constituents, including Carbon Dioxide (CO2)

© RMIT University


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Experience in Sustainable Aviation Research ENDEAVOUR Project Next Generation ATM Systems – 4D Trajectory Planning, Negotiation and Validation

© RMIT University


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ATM & Avionics Systems Research


ATM and Avionics Research  Strategic Research Focus: Air Navigation Safety and Integrity, Systems for Green Operations and Human Factors Engineering  ATM and Avionics Research Areas –

4-Dimensional Trajectory and Intent Based Operations

– –

Fault-Tolerant and High-Integrity Multi-Sensor CNS+A Systems Next Generation ATM and On-Board Flight Management Systems

– –

CNS+A Systems for Cooperative and Non-Cooperative Sense-and-Avoid Unmanned Aircraft Systems Integration and Future Suborbital Transport Systems

© RMIT University


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CNS+A Current Research Topics  Multi-Objective Trajectory Optimisation and Four Dimensional (4D) Trajectory Based Operations (TBO)  Dynamic Airspace Management (DAM)  Air Traffic Flow Management (ATFM) and Avionics Flight Management Systems (FMS)  Multisensor Navigation and Obstacle Avoidance Systems for Improved Flight Safety and Mission Efficiency  Aircraft/Avionics Based Integrity Augmentation (ABIA) Systems  CNS+A for Remotely Piloted Aircraft Systems (RPAS)  Network-Centric ATM technologies and System-Wide Information Management (SWIM) for Collaborative Decision Making (CDM)  Evolutionary Human Machine Interface and Interaction (HMI2), addressing aircrew/ATC interoperability with higher levels of automation © RMIT University


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Multi-Objective Trajectory Optimisation Engine and Emissions Model

Operational Business Model

Airframe Systems Model

ATM Model

Demographic Database

Multi Objective 3D/4D Trajectory Optimisation Globally Optimal Green Trajectories

Noise Model

2D

Terrain Database Weather Model © RMIT University

Aircraft Dynamics Model

Contrails Model


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Next Generation ATM Systems Research  4DT Optimisation and DAM Algorithms for State-of-the-Art ATM/ATFM Software  Funding Partner:

Australia

• Introducing new CNS+A based functionalities into the THALES TopSky system • Transitioning from current vector based ATM operations to 4D intent based operations • Applying trajectory optimization theory to optimize 4D trajectories for more efficient ATM operations • Developing techniques to reduce traffic in congested areas and optimise airspace usage • Introducing models for Dynamic Airspace Management (DAM)

• Developing 4D Prediction, Negotiation and Validation techniques for future ATM and avionics FMS systems

© RMIT University


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Example: Tailwind Trajectory Optimisation

Wether Cell Variable Time travelled Distance Travelled TSFC @ Vcruise/TAS Fuel Used (mass) Fuel Used (volume) Fuel Saving Cost saving CO2 Emissions (3156g/Kg) CO2 Emission reduction

© RMIT University

Straight Line Path Optimized Trajectory 9hrs 47.8 min 9hrs 38.2min 8050km 8207km 0.8254 Kg/(min*kN) 62.06 tons 61.05tons 49.89 x 103 Lts 48.90 x 103 Lts 990 Lts (217.77 Gallons) ~ A$655.30 195,861 Kg 192,674Kg 1.6%


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Integrated Navigation, Obstacle Detection and Avoidance Systems • Enhance aircraft flight safety − − − −

Bad weather and low visibility RPAS Detect-and-Avoid (DAA) Nap-of-the-Earth military operations Landing on moving platforms

• Increase mission efficiency − Near all-weather operation capability − Increased A/A and A/G functionalities

Flight management System or Pilot

Obstacle avoidance guidance

Flight control system

Aircraft model

• Reduce cost − Emerging COTS sensor technologies − Innovative multi-sensor fusion algorithms − Reduce airworthiness costs Cockpit or Remote Displays (UAS)

© RMIT University

Obstacle detection & location processing

Enhanced synthetic vision


Navigatio n system RF, VIS and EO Sensors

Outside scenario

Digital terrain/ obstacle database

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Integrated Navigation, Obstacle Detection and Avoidance Systems System Architecture

Integration

Performance

Human-Machine Interface

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Real-time Obstacle Detection in Aerodrome Areas • Aerodrome Safety − Air traffic is continuously increasing, with airports operating on tighter schedules and operators subjected to greater workloads − Ramps/taxiways are very busy with multiple aircraft and communication is mainly visual and vocal − Many obstacles are present, making surface manoeuvring difficult and increasing probability of accidents − Accident causes: congestion, crew distraction/misjudgment, poor visibility, lack of personnel, deviation from standard procedures, etc.

• Aim − Developing real-time machine vision-based obstacle detection systems to enhance crew situational awareness in aerodrome areas © RMIT University


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Aircraft/Avionics Based Integrity Augmentation Systems for Safety-Critical Applications  GNSS Integrity − Along with Space Based and Ground Based Augmentation Systems (SBAS/GBAS), Aircraft Based Augmentation Systems (ABAS) are being researched − Focus is on Integrity (in addition to Accuracy and Continuity) augmentation obtained by system/software redundancy and suitable analytic approaches − Using suitable data link and data processing technologies, a certified ABIA system can be a core element of a future GNSS SpaceGround-Avionics Augmentation Network (SGAAN)

 Aim − Developing ABIA systems for safety critical GNSS applications (aircraft precision approach/landing, UAV sense-and-avoid, operation under jamming/spoofing, etc.) © RMIT University


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Remotely Piloted Aircraft Systems Safety, Certification and Integration*  RPAS Detect-and-Avoid

 Safety assessment

– Analytical decomposition and uncertainty models (Aerosonde, NU)

– Ground impact safety case templates (CASA, Boeing)

– Unified approach to manned and unmanned DAA (Aerosonde, FDS, Nottingham Uni and Cranfield Uni)

– Mid-air collision safety case templates (Boeing)

 UAS airspace integration – Airspace integration study (CASA, Boeing, Thales)

– UAS Traffic Management (NASA Ames) – Voice controlled UAS (Thales)

 Regulation and Certification – Airworthiness (Northrop Grumman)

– Regulatory categorisation and certification schemes (DSTO AD)

 Human factors – Workload assessment for MAVs – Multi-aircraft supervisory control (Boeing)

 Autonomy – Definition of levels of autonomy (Boeing) – Autonomous safety systems (FDS) – Robust autonomy (Boeing, QUT)

 Societal perception and acceptance * In collaboration with RMIT Autonomous Systems Group

© RMIT University


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Suborbital Transportation Systems  Trajectory Optimisation for Rocket Based Combined Cycle (RBCC) Powered Space Transportation Vehicles  Communication, Navigation and Surveillance and Avionics (CNS+A) System Evolutions for Space Transportation Vehicles  Air Traffic Management (ATM) Evolutions for Orbital and Re-Entry Flight Operations

Suborbital Space Transport

 Multidisciplinary Design Optimisation (MDO) of Space Transportation Vehicles

 Ground Control Station (GCS) Design for Manned and Unmanned Space Vehicles  Multi-Mode Spaceport Design for Manned and Unmanned Space Vehicles Unmanned Reusable Space Vehicle © RMIT University


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Space Transportation Systems – Avionics Research

© RMIT University


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Multi-Objective Trajectory Optimisation for RBCC-Powered TSTO Spaceplane (Collaboration with JAXA and CIRA)

RBCC Engine

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Flight path and control of optimal solution for access to ISS Research Seminar – Assoc. Prof. Rob Sabatini

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Aviation Research Infrastructure Investments and strategic partnerships enabling new research capability: • Several fully operational RPAS platforms • Human Factors and Autonomous Systems Flight Test Laboratory

• Agreement signed between RMIT and FDS for access to CASA approved flight test area for UAS • Partnerships with QANTAS Flight Training Centre for Research Flight Simulator

• New ATM Systems Laboratory

© RMIT University


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Aviation Research Infrastructure Access to THALES ATM simulation laboratory (CASIA) and to QANTAS FTC Level D Certified Aircraft Flight Simulator: • Enabling validation of the developed avionic and ATM/ATFM systems and software codes • Enabling experimental research in human factors, flight training, operations and flight deck design optimisation

© RMIT University


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Aviation Research Infrastructure New CNS/ATM Laboratory • Industrial level hardware and software facilities, including – BARCO Displays – THALES TopSky – SimRoom Control Tower Simulation Environment

• Enabling research and teaching activities in CNS+A systems, operations and human factors • Networking capabilities with existing SAMME facilities and external research partners including:

PHASE 2

PHASE 1

– THALES CASiA/SkyCentre/NextGen Innovation Lab – QANTAS Flight Training Centre (Research Simulator) – Cranfield University CNS/ATM Laboratory – TU Braunschweig CNS/ATM Laboratory – NUAA ATM Research Centre – NLR and DLR

2 x Workstations 1 x Workstation

Local Server and intranet RMIT Network

THALES Network RMIT A/C Sim ATC Tower Simulator

© RMIT University


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Next Generation CNS+A Systems for Intent Based Operations


Air Traffic Management (ATM) Modernisation  The key performance improvement areas identified in the Global Air Navigation Capacity and Efficiency Plan by the International Civil Aviation Organization (ICAO) are: • Airport operations • Efficient flight path planning and execution

• Optimum capacity and flexible flights • Globally interoperable systems and data

Safety

Capacity

Flexibility

© RMIT University

Efficiency

Interoperability

Costeffectiveness

Environmental Sustainability


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Fuel-burn, Emissions and Noise Reduction Goals

Agency

ACARE – SRA and SRIA

NASA - ERA

Programme

Vision 2020

FlightPath 2050

N+1 2015

N+2 2025

N+3 2035

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SRA - Strategic Research Agenda, SRIA - Strategic Research and Innovation Agenda ERA - Environmentally Responsible Aviation © RMIT University


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CNS+A Evolutionary Roadmap Key Targets and Enablers  4-Dimensional Trajectories  Collaborative Planning and Decision Making  System Wide Information Management  Performance Based Operations  Free Routing (User Defined Trajectories)

© RMIT University


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CNS+A Operational Concepts

© RMIT University


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CNS+A Operational Concepts  Key SESAR/NextGen concepts: •

Moving from Airspace to Trajectory/Intent Based Operations

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Collaborative planning so that all parties involved in the flight management can participate to the enhancement of the overall performance that the system will deliver

•

Dynamic airspace management (civil/military coordination) and dynamic tactical allocation of airspace resources

•

Migration from voice-based to data-driven communications

• Humans as central decision-makers •

Improved HMI2, interoperability and overall automation

© RMIT University


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SESAR Evolutionary Roadmap

© RMIT University


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Enabling the CNS+A Concept  Multi-Objective 4D Trajectory Optimisation (MOTO-4D)  Next Generation Flight Management System (NG-FMS)

 4DT Planning, Negotiation and Validation (4-PNV)  Next Generation Airborne Data Link (NG-ADL)

© RMIT University


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CNS+A Operational Tasks Time Domain EMERGENCY

TACTICAL ONLINE

STRATEGIC ONLINE

10 minutes before hazard 20 minutes before hazard

NG-FMS and 4-PNV systems are conceived for the ONLINE phase with a focus on STRATEGIC and TACTICAL scenarios TACTICAL OFFLINE

STRATEGIC OFFLINE

© RMIT University


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NG-FMS Concept Next Generation Flight Management System (NG-FMS)

 The NG-FMS development is focused on 4-Dimensional Trajectory functionalities and air-ground trajectory negotiation/validation capabilities, including: • Multi-Objective 4D Trajectory Optimisation

• 4D Trajectory Monitoring • Real-time rerouting and information updating • 4D Trajectory Negotiation/Validation

© RMIT University


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NG-FMS General Architecture Integrity Flag

TRAJECTORY VALIDATION GroundInitiated Negotiation

Updated Trajectories

AircraftInitiated Negotiation

PATH CORRECTOR

PSE

TRAJECTORY MONITOR

Estimated/Predicted Manoeuvre & PSE

Constraints, Flyable Path

4DT Intent

TRAJECTORY PLANNER/OPTIMISER 4DT Optimiser Constraint Pool

Model Pool

ATM Operational Constraints/ Flight Plan Constraints

Aircraft Performance and Motion Constraints

Airspace Condition Constraints

Airline Constraints Performance Weightings

Trajectory Uncertainty Buffer (RNP Manager)

Aircraft Performance Model Earth Model

PDE NSE PSE

Atmospheric Model

Cost Functions Min. Fuel/Emission Min. Cost

Min. Contrail

© RMIT University

Min. Time Min. Noise

Noise Model

Error Aggregation yes

< 2xRNP?

no

Contrail Model


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Trajectory Optimisation and Flight Phases Green cruise

Green departure

T/O

Climb

Cruise

Green approach

Descent

Approach

Noise NOx Contrails CO2 Fuel

© RMIT University


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Multi-Objective Trajectory Optimisation Engine and Emissions Model

Operational Business Model

Airframe Systems Model

ATM Model

Demographic Database

MOTO-4D Globally Optimal 4D Trajectories

Noise Model

2D

Terrain Database Weather Model © RMIT University

Aircraft Dynamics Model

Contrails Model


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Multi-Objective Trajectory Optimisation

Lagrange term

© RMIT University

Mayer term


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Multi-Objective Trajectory Optimisation  Articulation of preferences: “a priori” versus “a posteriori”

 Other possibilities: •

Progressive articulation of preferences

•

No articulation of preferences

© RMIT University


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NG-FMS Dynamics Models • Both 6-DOF and 3-DOF flight dynamics models with variable mass can be used to propagate the aircraft state vector • The different flight phases (and associated manoeuvres) are considered: Climb, Cruise and Descent

© RMIT University


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3DOF Uncertainty Analysis & Error Propagation wx Lon Rt wy Lat Rm wh

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Control variables Other parameters

Parametric Uncertainty Propagation

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NG-FMS 4D Trajectory Optimisation Priority

Weightings Kfuel

Ktime

Knoise

Kweather

cell

Priority 1

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Kweather

cell1

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Kfuel2

Ktime2

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Kfuel3

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cell3

•

© RMIT University


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NG-FMS 4D Trajectory Optimisation • Validation -> Airbus A380 from London Heathrow (EGLL) to Atlanta International (KATL). • Optimal 4DT intents with PCFRs and weather cells as path constraints generated within 10 seconds and 72 seconds when wind is added.

Item

Latitude

Longitude

Radius

N 51o 29’ 8.41”

W 0o 28’ 0.01”

-

NFZ 1

N 51o 54’ 0”

W -2o 15’ 0”

4.3 NM

NFZ 2

N 51o 44’ 58”

W -1o 10’ 0”

2.7 NM

Weather Cell 1

N 38o 5’ 0”

W -70o 0’ 0”

80 NM

Weather Cell 2

N 40o 0’ 0”

W -38o 0’ 0”

80 NM

PCFR 1

N 43o 0’ 0”

W -50o 0’ 0”

350 NM

PCFR 2

N 45o 0’ 0”

W -20o 0’ 0”

350 NM

N 33o 38’ 12.01”

W -84o 25’ 43.79”

-

Initial Waypoint

Final Waypoint

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59

NG-FMS 4D Trajectory Optimisation Trajectory Reference No.

Time (s)

Fuel Burn (KG)

CO2 (KG)

NOx (KG)

Trac-1-cl (min-time)

725.0

6091.4

19066.08

85.89

Trac-2-cl (0.1*fuel+0.9*time)

728.5

5486.7

17173.37

77.36


739.9

5434.9

17011.24

76.63


746.2

5415.9

16951.77

76.36


750.2

5407.5

16925.48

76.25


755.7

5400.6

16903.88

76.15


758.9

5398.0

16895.74

76.11


760.0

5395.5

16887.92

76.08


764.6

5395.1

16886.66

76.07


766.5

5388.6

16866.32

75.98

Trac-11-cl (min-fuel)

766.0

5387.4

16862.56

75.96

Time required for each trajectory generation :

Advanced Aerospace Technologies

Advanced Aerospace Technologies

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