Description du projet :
Drones will soon ll our aerial ecosystem in the eld imaging/cartography, parcel delivery (La Poste,
Amazon), and passenger transport (Uber Elevate). Drones will need to operate around the clock in
arbitrary atmospheric conditions, especially in adverse weather conditions during emergency situations.
In cities, they will be required to overcome urban canyon as well as road vehicle turbulence. At higher
altitudes, winds can be unpredictable: drones are much smaller than conventional aircraft and are thus
more sensitive to weather conditions.
Today, traditional drone testing techniques are of poor quality and do not reflect conditions that may
be encountered in real world applications. Test drones are either own outdoors in not well documented,
uncontrolled and unpredictable weather conditions (and quite remote from the observer), or tightly
strapped onto a support in a conventional wind tunnel with laminar and uniform wind ows. Such wind
flows have been devised for conventional aircraft and they are inadequate representations of atmospheric
conditions relevant to drones. In summary, existing tests entail a high risk of reaching false conclusions
about drone performance and introduce additional risks on drone design that may lead to catastrophic
situations.
We have submitted a proposal to the Federal Office of Civil Aviation (FOCA) to develop drone
certi cation protocols in a controlled environment, using a Simulator of Autonomous Flight Environments
(SAFE) based on a multifan wind facility commercialized by a Swiss company, WindShape. The wind
facility consists of an array of a large number of fans that may be arranged in various patterns on
demand. SAFE will subject drones to winds of variable intensity and direction (as well as various
weather conditions such as rain, snow, hail, fog etc.) that re ect real world situations. These tests would
then rate drones according to their capacity in maintaining a proper flight attitude and tackling flight
perturbations in an urban, countryside, or high altitude environment.
In order to achieve these goals, SAFE needs to replicate real environmental conditions. In this context,
two key steps need to be undertaken: 1. supply this facility with actual atmospheric turbulence data at
drone scales (in particular, those encountered in an urban environment), and 2. develop a methodology
to reproduce these ows in a multifan facility. The objective of this Bridge proposal is to achieve these
two steps by :
1. Collecting actual urban turbulence data using a facility (motus.epfl.ch) installed at the Ecole Polytechnique
Fédérale de Lausanne: a meteorological mast, instrumented with sonic anemometers
with high sampling frequency, which provides wind intensity and direction in an urban setup;
2. Training thousands of fans in a WindShape facility to produce given 3D flows in space and time,
using Machine Learning algorithms and a vast experimental data set collected within the facility.
Ultimately, the facility will be able to generate realistic atmospheric flowfields and in particular,
genuine urban wind turbulence. SAFE will be the first ever facility in the world to ensure drones
airworthiness according to their capacity in staying aloft in given wind and weather conditions. The
infrastructure (hardware), the flowfi elds (software), as well as the testing and certi cation procedure
(processes) can then be commercialized worldwide, either as a service or as a franchise.
Forschungsteam innerhalb von HES-SO:
Noca Flavio
Partenaires académiques: Koumoutsakos Petros, ETHZ; Mauree Dasaraden, EPFL
Durée du projet:
01.01.2020 - 31.12.2022
Montant global du projet: 1'000'000 CHF
Statut: Laufend
SWITCH edu-ID
Verwaltung
