Projektbeschreibung
Innovative Strömungssteuerungsstrategien für reduzierte Emissionen
Der Luftverkehr hat aufgrund hoher CO2-Emissionen erhebliche wirtschaftliche und ökologische Auswirkungen. Es besteht ein dringender Bedarf, die aerodynamische Leistung von Flugzeugflügeln zu verbessern, um den Treibstoffverbrauch und die Emissionen zu verringern. Eine der Strategien besteht darin, eine Flusssteuerung durchzuführen. Das vom Europäischen Forschungsrat finanzierte Projekt DEEPCONTROL wird hochpräzise Simulationen und Deep Reinforcement Learning einsetzen, um einen Rahmen für die Echtzeitvorhersage und Steuerung der Strömung um Flügelabschnitte und dreidimensionale Flügel zu entwickeln, die nur auf spärlichen Messungen basiert. Das Projekt zielt darauf ab, neuartige Lösungen in Bezug auf die Strömungsbetätigung und das Design der Winglet-Geometrie zu entdecken, um die Nachhaltigkeit der Luftfahrt zu verbessern. DEEPCONTROL wird außerdem detaillierte Windkanalexperimente an der Königlichen Technischen Hochschule in Schweden durchführen, um den Rahmen für Echtzeitanwendungen zu bewerten.
Ziel
Over the past decades, aviation has become an essential component of today’s globalized world: before the current pandemic of coronavirus disease 2019 (COVID-19), over 100,000 flights took off everyday worldwide, and a number of studies indicate that after the pandemic its relevance in the transportation mix will be similar to that before COVID-19. Aviation alone is responsible for 12% of the carbon dioxide emissions from the whole transportation sector, and for 3% of the total CO2 emissions in the world. Due to the major environmental and economical impacts associated to aviation, there is a pressing need for improving the aerodynamic performance of airplane wings to reduce fuel consumption and emissions. This implies reducing the force parallel to the incoming flow, i.e. the drag, and one of the strategies to achieve such a reduction is to perform flow control.
DEEPCONTROL aims at using high-fidelity simulations and deep reinforcement learning to develop a framework for real-time prediction and control of the flow around wing sections and three-dimensional wings based only on sparse measurements. We will first perform high-order spectral-element simulations of wing sections and three-dimensional wings at high Reynolds numbers. Using sparse measurements at the wall, we will reconstruct the velocity fluctuations above the wall within a region of interest. To this end, we will employ a generative adversarial network (GAN), together with a fully-convolutional network (FCN) and modal decomposition. Then, we will perform flow control based on deep reinforcement learning (DRL), which will enable discovering novel solutions in terms of flow actuation and design of winglet geometry. In order to assess the robustness of the framework for real-time applications, we will carry out detailed wind-tunnel experiments at KTH.
This framework will constitute a breakthrough in aviation sustainability, and will enable developing more efficient aeronautical solutions worldwide.
Wissenschaftliches Gebiet
- medical and health scienceshealth sciencespublic healthepidemiologypandemics
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsfluid dynamics
- medical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses
- engineering and technologyenvironmental engineeringenergy and fuels
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC - HORIZON ERC GrantsGastgebende Einrichtung
100 44 Stockholm
Schweden