About
Our Mission
RefMap aims to reduce the environmental impact of air travel for airlines and Unmanned Aerial Systems by creating a digital service that optimises flight trajectories on both micro and macro levels. By using environmental data, such as wind, noise, CO2 and non-CO2 emissions, RefMap's analytics platform can help airlines make more eco-friendly decisions. This will lead to stricter evidence-based Green policy making in the aviation sector and the development of new aviation business models in line with the EU's Green Agenda.
Objectives
RefMap develops a fuel-based air quality model that accounts for both conventional fossil fuels and sustainable aviation fuels. This model captures primary and secondary pollutants in both polluted and cleaner areas, combining climate impact and aircraft noise modules for trajectory optimisation. REFMAP develops the above solutions to achieve the following objectives:
Trajectory Optimisation
Development of a fuel-based air quality model for both fossil and sustainable aviation fuels to capture primary and secondary pollutants in both polluted and cleaner areas, combining climate impact and aircraft noise modules for trajectory optimisation.
Flow Patterns Prediction
Deep learning will be applied to predict flow patterns in a non-intrusive way in order to optimise drone trajectories with deep reinforcement learning, prioritising cyber security and decentralised data management.
Reduce air travel’s environmental impact
Improved air traffic management through high-fidelity flight models, optimised commercial flight trajectories, an algorithm for airline trajectories in climatic uncertainty, and minimising drone noise in populated areas.
Minimise the noise impact on communities and wildlife
Development of noise models, conducting psychoacoustic testing, and providing guidelines to reduce the noise impact from drones.
New aviation business models
By taking a holistic approach to aviation business models, showing how green technologies can support green management and by aligning aviation needs with stakeholder needs, RefMap is able to extract the full business value of its green technology.
Use Cases
RefMap use cases fall into two different categories, large scale and small scale. Large scale use cases focus on sustainability and aviation regulations for airlines and airports on an EU level, while small scale use cases focus on urban air mobility and the integration of drones to daily activities.
Deliverables
Deliverable Number | Deliverable Name | Type | Status |
|---|---|---|---|
D1.1 | Project Management, Quality Plan and Risk Management | R — Document, report | |
D1.2 | Dissemination and Communication Plan | R — Document, report | Published |
D1.3 | Data Management Plan | DMP — Data Management Plan | Published |
D1.4 | Report on Dissemination and Communication activities of Period 1 | R — Document, report | Published |
D1.5 | Report on Dissemination and Communication activities of Period 2 | R — Document, report | |
D1.6 | Data Management Plan Midterm | DMP — Data Management Plan | |
D1.7 | Data Management Plan Final | DMP — Data Management Plan | |
D1.8 | Research ethics monitoring report | R — Document, report | |
D1.9 | Final evaluation of the ethical compliance | R — Document, report | |
D2.1 | Description of high-fidelity simulations and results | R — Document, report | Published |
D2.2 | Description of RANS-based simulations and results | R — Document, report | |
D2.3 | Description of multi-fidelity approach to combine both types of simulations | R — Document, report | |
D2.4 | Multi-fidelity simulations auto-tuning framework | R — Document, report | |
D2.5 | Local air quality models | R — Document, report | |
D2.6 | Aviation climate change models | R — Document, report | Published |
D3.1 | Description of framework for non-intrusive sensing in cities | R — Document, report | |
D3.2 | Description of approach for super-resolution and optimisation of sensor location | R — Document, report | |
D3.3 | Trajectory optimisation for minimum environmental impact | R — Document, report | |
D3.4 | First report on deep learning inference optimisation | R — Document, report | |
D3.5 | Final deep learning inference optimisation framework | R — Document, report | |
D4.1 | Aircraft noise model and integration in the trajectory optimisation code | R — Document, report | |
D4.2 | Model for emission and propagation of drone noise | R — Document, report | |
D4.3 | Systematic review and framework for testing drone noise impact on human and wildlife | R — Document, report | |
D4.4 | Model for Drone Noise Perception | OTHER | |
D4.5 | Report on targets for public acceptance of drone noise in cities | R — Document, report | |
D4.6 | Report on impact of drone noise on wildlife | R — Document, report | |
D5.1 | Flight dynamics models with environmental impact assessment support | OTHER | |
D5.2 | Multi-scale aviation simulation | DEM — Demonstrator, pilot, prototype | |
D5.3 | Resilient and environmentally optimal aerial network | R — Document, report | |
D5.4 | Report on assessment of impact of optimised trajectories on the performance of UAS operation in urban environment | R — Document, report | |
D5.5 | The RefMap cloud service | R — Document, report | |
D6.1 | Minimum Viable Product requirements | R — Document, report | |
report based on a review of aviation
stakeholder needs and market insights | |||
D6.2 | Presentation of new European business models and products prototypes enabled by RefMap services | R — Document, report | |
D7.1 | OEI - Requirement No. 1 | ETHICS |
Dissemination Material
A1 Poster
Trifold brochure
Roll-up