Advanced Aerospace Technologies

CNS/ATM, Avionics and UAS Research at RMIT University Assoc. Prof. Roberto

Sabatini

FRIN, SMAIAA, SMIEE, MRAeS, MCGI Aviation Team Leader UAS Research Team Member Avionics & ATC/ATM Topic Leader Sir Lawrence Wackett Aerospace Research Centre School of Aerospace, Mechanical and Manufacturing Engineering Tel: +61 3 9925 8015 Email: [email protected] Braunschweig, 10 March 2014

Sir Lawrence Wackett Aerospace Research Centre

Aviation Research Focus: Avionics & ATM Systems

© RMIT University

School of Aerospace, Mechanical & Manufacturing Engineering

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RMIT University Expertise Aircraft Design and Operations

Air Transport Systems Design and Operations © RMIT University


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Aerospace Platforms

© RMIT University


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Aerospace Research & Teaching Topics Aircraft

Airframe/Structure

Flight Systems

Avionic Systems

The major structural aspects of the aircraft:

The systems that enable the aircraft to continue to fly safely throughout the mission - usually involves a transfer of energy:

The systems that enable the aircraft to fulfil its operational role - usually involves a transfer of data:

Propulsion

Sensors

Fuselage Wings Empennage

Aerodynamics Structural Integrity

Flight Controls

Mission Computing Controls & Displays

Fuel System

Strong integration links - weight, installation, loads, low observable impact

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Navigation

(Weapons)

Strong integration links - demands for action, display data


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Aerospace Systems Engineering Aerospace systems

Knowledge domain of systems engineers

Systems Engineers Signal and data subsystems Flight Controls

Sensors

Engines

Components and subcomponents

Controllers

Knowledge domains of design specialists Design Specialists Parts

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Avionics Research and Education Avionics Topics

Avionics Systems Engineering  Avionics Systems Engineering  Avionics SW Engineering and Design  System Safety Engineering

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Avionics Technology Subjects      

NAV Technologies COMM Technologies A/C GNC Technology A/C Stability and Control Fault-Tolerant Techniques Data Buses and Systems Integration  Display Systems  ATC/ATM

Multidiscipline Subjects     


Airframe Systems Aircraft Aerodynamics Aircraft Performance Aircraft Design IVHM Techniques

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Some Research Topics •

Fault-Tolerant Multi-Sensor Navigation

•

Development of Smart Inertial-Aiding GPS Tracking Loop Algorithms

•

Study of Virtual Rendezvous Airspace Models (VIRA) and Conflict Detection Resolution Methods for Cooperative Free Flight (CoFF)

•

Development of UAV Sense-and-Avoid (SAA) Capability

•

UAV Attitude Determination using Low-Cost GNSS Sensors

•

Development of Fly-By-Wireless UAV Flight Control Architecture

•

Optimal Approach Trajectory Design

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Model-based Avionics Fault Diagnosis

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Kinematic GPS/Low-Cost AHRS Integration for RNP 0.1 System

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Design and Development of 4D-FMS RNP Display Systems

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Some Research Topics (2) • Students Research Topics (cont.) – Development of Next Generation ATM Route Planning and On-Board Flight Management Systems – Non-Deterministic Approaches for 4D Trajectory-Based Operations (TBO) – Active and Passive Electro-Optical Sensor Systems (EOSS) for Air Vehicle Navigation and Guidance – Aircraft Based CNS+A Integrity Augmentation Systems for Safety-Critical Applications – Avionics Integration of Multispectral and Iperspectral Imaging Sensors – Development of Data Link Relative Navigation and Integrated Systems for Improved Navigation Performance – Air Traffic Management: Development of Congested Flow Optimization Models – Air Traffic Management: Enabling Communication, Navigation and Surveillance (CNS) Technologies for ‘Free Flight’ – Air Traffic Management: Harmonization of SESAR and NextGen in the Global Air Traffic Management Network – Unique Aspects and Challenges of UAS Development, Verification and Certification: Access to the Global Air Traffic Management Network – Vison-Based Techniques for Low-Cost UAV Navigation and Guidance © RMIT University


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Fault-Tolerant Navigation System Design Hierarchical Structure of Fault-Tolerant Design Fault-Tolerant Navigation System (High Reliability and Performance) Software Redundancy

Hardware Redundancy

Dissimilar Systems/ Sensors INS, GPS, radio nav, air data system, Doppler radar, magnetic heading

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Similar Navigation Systems Dual, triple or quadruple INSs

Analytic Redundancy

Similar multiple Sensors Several inertial sensors


Mathematical Models Translational Dynamics, Rotational Kinematics

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Integrated Navigation Software Development • Open Software System Architecture

• Data Fusion Filter Design

– Different operating modes – Failure detection and isolation – System dynamic reconfiguration

– Centralised Filter Architecture – Cascaded Filter Architecture – Federated Filter Architecture

– Navigation solution integrity monitoring

– Distributed Filter Architecture

Multi-Mode Integrated Navigation Management

INS Navigation

GPS Navigation

Integrated Navigation

Software

Software

Software

External Interfaces © RMIT University


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GPS/Galileo Compatible Software RX • Combination of GPS and future Galileo systems will provide real global required coverage, availability, integrity and accuracy of satellite navigation signals. • Standard GPS receivers track the code phase using a Delay-Locked Loop (DLL) and the carrier phase using a Phase-Locked Loop (PLL), which are hardware. • Software receivers afford batch data processing options that are not available in hardware implementations. Software batch processing can be useful when there is an extremely low signal-to-noise ratio (SNR).

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Navigation Algorithm Software

Acquisition and Tracking Softwarebased processing


Software

Position, Velocity, Time, etc.

ADC

Hardware

RF to IF Converter

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Multisensor Navigation and Obstacle Avoidance for Improved Helicopter and UAV Flight Safety and Mission Efficiency • Enhance Helicopter flight safety in various flight conditions – Bad weather and low visibility – Low altitude – Nap of Earth – Landing on moving platforms

• Increase helicopter mission efficiency – Near all-weather operation capability – Increased functionality at a lower cost

• Low cost – Emerging COTS sensor technologies – Innovative multi-sensor fusion algorithms

© RMIT University


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Integrated Navigation, Obstacle Detection and Avoidance Systems Functional architecture Flight management System or Pilot

Obstacle avoidance guidance

Flight control system

Aircraft model

Navigation system Obstacle detection & location processing

Displays System Cockpit or Remote (UAS)

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Enhanced synthetic vision

RF, VIS and EO Sensors

Outside world

Digital terrain/ obstacle database


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Virtual Rendezvous Airspace Models and Conflict Detection/Resolution Methods • Indicators of traffic delays and congestions –

Saturation of the airspace due to airspace limitations and inefficiencies of air traffic management, – Saturation of airport infrastructures leading to reduced capacity of airport, and – Increased cost to airlines due, among other things, to increased length of routes.

• Critical problems with the current ATC system –

Need for improved safety and situational awareness (safety) – Need for optimising airspace structures (efficiency and safety) – Need for flexible use and access of airspace and airports (efficiency) – Need for saving energy and flight time (efficiency)

• Aim of this research –

Study and assess new concepts and methods, known as Virtual Rendezvous Airspace for Cooperative Free Flight (VIRA/CoFF), to resolve air traffic conflict and congestion problems in free flight environments for the flexible use of finite airspace.

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VIRA/CoFF Architecture • Research objectives – Develop 4D probabilistic trajectory models to predict short-term and long-term trajectories – Develop dynamic VIRA model to predict congestion spaces – develop safe CoFF escape and recovery strategies – Establishment of a hybrid simulation network consisting of existing flight simulators and several PCs

RSP

4D Probabilistic Trajectories Predictions

RCP

RNP

New aircraft flight plans

VIRA Models

ADS-B

RNP: required navigation performance. RCP: required communication performance. RSP: required surveillance performance.

CoFF Conflict Detection and Identification

CoFF Escape and Recovery Trajectories

Aircraft (RNP)

Aircraft State Vector (RSP)

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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 workers subjected to greater workloads – Ramps/taxiways are very busy with multiple aircraft and communication is mainly visual and verbal – 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 – To design and develop a real-time machine vision-based obstacle detection system to enhance crew situation awareness in aerodrome areas.

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Aircraft Based GNSS Integrity Augmentation Systems for Safety-Critical Applications • Aircraft Based Augmentation – Along with Space Based and Ground Based Augmentation Systems (SBAS/GBAS), Aircraft Based Augmentation Systems (ABAS) are being researched – Focus is on Integrity (and not only Accuracy/Continuity), towards a novel Avionics Based Integrity Augmentation (ABIA) concept – 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 – To develop innovative Avionics Based Integrity Augmentation (ABIA) systems for safety critical GNSS applications (aircraft precision approach/landing, UAV senseand-avoid, etc.) © RMIT University


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Next Generation ATM Route Planning and On-Board Flight Management Systems • Next Generation ATM (SESAR/NextGen) – Communications, Navigation & Surveillance Systems for SESAR/NextGen – Air Traffic Flow Management, Decentralization and Collaborative Decision Making – Mathematical Models for Aircraft Trajectory Optimisation in the presence of ATM constraints, including CleanSky criteria – RNP, RCP, RSP and Enabling Technologies for “Free Flight” – Trajectory Estimation and Tracking – 4-DT ATM Route Planning – 4-DT Flight Management Systems

• Aim – To develop innovative technologies which have the potential to be used in the next generations of ATM Route Planning and Avionics Flight Management Systems © RMIT University


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Aviation Research

Focus: Systems for Green Operation

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Introduction to Aviation Sustainability  The main ‘actors’ in aviation sustainability: • Organisations for international cooperation − United Nations, Organisation for Economic Cooperation & Development (OECD), Intergovernment Panel on Climate Change (IPCC), European Commission (EC) • −

International aviation organisations ICAO, FAA, International Air Traffic Association (IATA), ACARE, etc.

• −

Air transport operators: airports, air traffic control & airlines Melbourne airport, Air Services Australia, Qantas, etc.

•

−

Aerospace manufacturers: aircraft, engines, avionics & other equipment Boeing, Airbus, Rolls Royce, Thales, BAe Systems, etc.

• −

Non-Government organisations & lobby groups ‘Green’ groups etc.

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Users: passengers & freight shippers

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Research & scientific organisations

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Sustainability: Environmental Management •

Environmental management in the broadest sense involves oceans, freshwater systems, land and atmosphere.

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Aviation is mostly concerned with the environmental management of the atmosphere: atmospheric pollution/noise and of the land: airports and ground operations.

•

Aircraft environmental management includes: − Use of non-renewal resources (e.g., fossil fuels, materials) − Greenhouse gases (e.g., CO2) − Nitrogen oxides − Sulphur oxides − Volatile organic compounds − Particulate matter − Contrails − Noise

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Sustainability: Environmental Management •

Airport environmental management includes: − Contaminated land (ground & surface water at airports arising from jet fuels) − Aircraft de-icing − Waste generation − Land take − Terminal buildings − Ground transport (e.g., terminal buses)

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Introduction to Sustainability: Pollution •

Atmospheric pollution caused by aircraft is becoming recognised as the most serious sustainability issue for aviation.

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Pollution includes carbon dioxide, nitrogen oxide, contrails, all of which contribute to global warming.

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It is estimated that aviation industry contribution to global warming is currently 2-3%, although may increase 10-20% by 2050 due to growth in air transport.

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In addition, ground-level emissions at airports (from aircraft, buildings & surface vehicles) are increasing.

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Technology advances have been successful in reducing atmospheric and ground emissions from aircraft, but this is offset by growth in aviation.

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Introduction to Sustainability: Pollution Impact of aviation on climate change is complex and we will examine the key elements in this course.

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Introduction to Sustainability: Pollution Major progress on developing more efficient aircraft: lower fuel consumption (cost-saving) & less greenhouse emissions. We will examine how these improvements have been achieved. Comet

Percentage of Base-Line Comet

100

80

engine fuel consumption

60

40 aircraft fuel burn per seat

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0 1950

1960

1970

1980

1990

2000

2010

Year of Introduction © RMIT University


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Introduction to Sustainability: Pollution Historic trends in fuel burn for new jet aircraft (1960-2008).

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Introduction to Sustainability: Pollution CO2 Emission Projections

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Introduction to Sustainability: Pollution Inverting the CO2 emission trend with new technologies

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Introduction to Sustainability: Noise

•

120

Public opposition to aviation tends to focus on (airport) noise. Modern aircraft are quieter than their predecessors. However, the increase in aircraft traffic compounds public exposure to noise (particularly people living close to airports).

115

105

Noise can dominate the relationship between airports and local residents, and can lead to local operating regulations constraining aircraft operations.

B707-320

100 B747-200

95

•

B707-120

110

Aircraft Noise (dB)

•

A300 B777

90

A380 B787

A350

85

80 1960 1970 1980 1990 2000 2010 2020

Year of First Flight 10 dB reduction = 50% less noise

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Introduction to Sustainability: Noise •

We will examine noise reduction for aircraft (engine, airframe and operations).

© RMIT University

•

We will discover that some technology evolutions are driven by contradicting forces (e.g. open rotor or NOx vs. CO2).

•

This is where sound engineering judgement is needed.


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Sustainable Aviation Technologies ATM Systems and Procedures

Aircraft Structures, Propulsion and Systems

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Airport Design and Operations


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Sustainable Aviation Technologies ATM Technologies  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.

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Sustainable Aviation Technologies ATM Technologies (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 increase 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.

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Sustainable Aviation Technologies 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.

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Sustainable Aviation Technologies Aircraft Technologies  Energy management gains: •

Evolution of aircraft systems to increase fuel economy and reduce spurious offtakes 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.

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Sustainable Aviation Technologies Airport Technologies  • • • • •

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).

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Direct health effects


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Sustainable Aviation Technologies Airport Technologies  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).

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Sustainable Aviation Technologies Airport Technologies  Airport water quality impact mitigations

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De-icing fluids amount, collection and disposal

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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

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Vegetation management

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Controlling the establishment of landfills and waste disposal sites

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Airport fencing

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Animal distress calls

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Loud sounds (?), chemical repellents (?)

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Sustainable Aviation Organisations

http://www.icao.int/Pa ges/default.aspx

http://www.sustainabl eaviation.co.uk/

www.sesarju.eu/

www.cleansky.eu/

www.faa.gov/nextgen/

http://www.aeron autics.nasa.gov/i srp/era/

http://www.icao.int/publ ications/journalsreports /2013/6802_en.pdf

© RMIT University

www.sesarju.eu/envir onment/aire http://www.faa.gov/nextgen/impleme ntation/programs/aire/

http://www.cleansky.eu/categ ory/tags/skyline


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Experience in Sustainable Aviation Technology • 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 Technology 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 Technology

© RMIT University


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

© RMIT University


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Experience in Sustainable Aviation Technology ENDEAVOUR Project Next Generation Flight Management Systems for Manned and Unmanned Aircraft

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Research Evolutions

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CNS+A 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)

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Operational Concepts

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School of Aerospace, Mechnical and Manufacturing Engineering Sir Lawrence Wackett Aerospace Research Centre

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Operational Concepts  Key SESAR/NextGen concepts: •

Moving from Airspace to Trajectory/Intent Based Operations

•

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

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SESAR Evolutionary Roadmap

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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)

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CNS+A Operational Tasks Time Domain EMERGENCY

TACTICAL ONLINE

STRATEGIC ONLINE

10 minutes before hazard 20 minutes before hazard

CNS+A Systems are conceived for the ONLINE phase and focus on the STRATEGIC and TACTICAL scenarios TACTICAL OFFLINE

STRATEGIC OFFLINE

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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

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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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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

Aircraft Performance Model Earth Model Atmospheric Model

Cost Functions Min. Fuel/Emission Min. Cost

Min. Contrail

© RMIT University

Min. Time Min. Noise

Noise Model

Trajectory Uncertainty Buffer (RNP Manager) PDE

NSE

PSE

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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NG-FMS Functions and Objectives CO2 emissions reduction -1 to 2% during cruise No persistent condensation trails formation

Noise reduction -2dB for low altitude segment

NOx emissions: any reduction, to assess

CO2 emissions reduction 15% during climb phase

Optimised multi step

Noise reduction -3dB for low altitude segment CO2 emissions reduction 10% during descent and approach phases NOx emissions: any reduction, to assess

NOx emissions: any reduction, to assess

Improved CDA

MCDP

T/O

Climb

Cruise

Descent

Approach

Env. Performance targets broken down into function objectives

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NG-FMS 4D Trajectory Optimisation •

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NG-FMS 4D Trajectory Optimisation Weightings

Priority

Kfuel

Ktime

Knoise

Kweather

cell

Priority 1

Kfuel1

Ktime1

Knoise1

Kweather

cell1

Priority 2

Kfuel2

Ktime2

Knoise2

Kweather

cell2

Priority 3

Kfuel3

Ktime3

Knoise3

Kweather

cell3

•

© RMIT University

School of Aerospace, Mechnical and Manufacturing Engineering Sir Lawrence Wackett Aerospace Research Centre

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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

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NG-FMS 3-DOF Error Propagation wx Lon Rt wy Lat Rm wh

h

n phi

Hdg FPA

g CL rho

K CD CDz

D

V

S

C

5 fuel consumption coefficients P

wx: along-track wind velocity Rt: Transverse radius of curvature wy: cross-track wind velocity Rm: Meridional radius of curvature wz: vertical wind velocity n: load factor phi: bank angle g: gravity CL: lift coefficient K: drag factor CDz: lift-independent drag S: gross wing area P: engine power setting rho: air density D: drag c: specific-fuel consumption T: thrust h: altitude FPA: flight path angle Hdg: heading V: velocity m: aircraft mass Lat: Latitude Lon: Longitude State variables

T

m

12 thrust coefficients

Control variables Other parameters

Parametric Uncertainty Propagation

© RMIT University


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NG-FMS 4D Trajectory Optimisation For validation, an AIRBUS A380 aircraft taking off from London Heathrow airport (EGLL) and landing at Atlanta International Airport (KATL) is simulated 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

© RMIT University


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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

Fuel reduction : Minimised CO2: 2204 Kg – Minimised and Nox: 10 Kg Time required for each trajectory generation :

Advanced Aerospace Technologies

Advanced Aerospace Technologies

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