top of page


  • Writer's pictureRefMap

Sustainable Aviation Plan in Progress

Can we envision airliners that will operate in an environment-neutral manner? Can we picture electrically powered smaller air vehicles (drones) sharing the skies with classic aircraft? Can we help airlines make more eco-friendly decisions, such as using optimal flight trajectories that minimise climate impact and air pollution and also sustainable aviation fuels (SAF)? 

Our research in the RefMap project aims to answer these questions, and below you can find out our progress!

How do humans respond to drone noise?

RefMap’s UK partner, the University of Salford has completed the first series of experiments aimed at understanding the human response to drone noise.  The ultimate goal is to develop a model to predict drone noise annoyance and aid the trajectory optimisation of drones to reduce the impact on communities.  This first experiment investigated how varying UAS (drone) flight operations, vehicle types, and event quantities could affect noticeability and noise annoyance judgments in a controlled experiment incorporating 3D spatially-rendered acoustic environments.  The University of Salford is also working on the development of a framework for the auralisation and acoustic simulation of drone operations (see Figure 1) to aid drone noise assessment and decision-making.

Measured and auralised sound spectrograms. Measured (left), auralisation with filtered broadband white noise (centre), and auralisation with cross-correlated broadband noise (right)

The optimisation of drone trajectories with low-fidelity Computational Fluid Dynamics (CDF) models

Using a low-fidelity Computational Fluid Dynamics (CDF) model, RefMap’s partners, the Delft University of Technology together with AgentFly Technologies, are now able to predict the wind around realistic urban areas (in their case, the TUDelft campus) which can be used to explore and optimise Urban Air Vehicles (UAV) trajectories. In the figure below, we present a directionally-averaged velocity heatmap where red regions mark high-velocity regions at different vertical locations in the area of interest. In addition to the TUDelft campus, they are currently also working on two other cases with realistic urban areas namely, Delftshaven (a sub-set of the city of Rotterdam) and the entire city of Rotterdam.

Directionally-averaged velocity heat map at various vertical coordinates for the area of interest. The black colour marks the buildings, the red colour marks locations with high-velocity magnitude, and the blue colour marks locations with low-velocity magnitude


Computing in-flight emissions for aircraft using sustainable aviation fuels

The Boeing fuel flow method 2 (BFFM2) which computes the emissions along a given trajectory, has been adapted by RefMap’s partner, KTH Royal Institute of Technology, to consider sustainable aviation fuels at different blending ratios. The SAF-adapted BBFM2 is in its final phase of implementation aiming to reach higher levels of accuracy in emissions predictions for SAF. In this way, we will not only be able to compute the emissions generated by a given trajectory when using fossil fuel and its corresponding climate and air quality impact but also consider the use of more sustainable approaches such as SAF.


High-fidelity simulations to characterise the turbulent flows in urban areas

Using high-fidelity Large Eddy Simulations (LES), we are able to predict the wind turbulence around buildings with high accuracy. This allows us to better describe the turbulence phenomena in the proximity of the buildings and use these results for the training of the optimisation tool for the trajectories of drones.


What’s next?

The Aircraft Noise and Climate Effects (ANCE) section of the Delft University of Technology together with AgentFly Technologies, are already planning their next experimental campaign in Czechia to measure the noise emitted by a wide range of Unmanned Air Vehicles (UAV)’ operations. The researchers from both teams will measure the acoustics and the flight dynamics of several drone types. This is a necessary step towards developing data-driven models to predict annoyance caused by UAV operations, bringing us a step closer to fulfilling one of RefMap's main goals. AgentFly Technologies upgraded its simulation framework to include the noise models created by the measurements. Once the models are completed, the simulation will be capable of evaluating the noise over multiple scenarios and simulation runs.




This article was written collaboratively by all of RefMap’s partners.

For more news coming out of the project, stay tuned with us at and our social media.


Linkedin: RefMap Project

X (Twitter): RefMap EU



bottom of page