Descrizione del progetto
Strategie innovative di controllo del flusso per ridurre le emissioni
Il settore dell’aviazione ha un notevole impatto economico e ambientale a causa delle elevate emissioni di CO2. È urgente migliorare le prestazioni aerodinamiche delle ali degli aerei per ridurre il consumo di carburante e le emissioni. Una delle strategie consiste nell’eseguire il controllo del flusso. Il progetto DEEPCONTROL, finanziato dal CER, utilizzerà simulazioni ad alta fedeltà e apprendimento di rinforzo profondo per sviluppare un quadro per la previsione e il controllo in tempo reale del flusso intorno a sezioni alari e ali tridimensionali, basandosi solo su poche misurazioni. Il progetto mira a scoprire nuove soluzioni in termini di attuazione del flusso e di progettazione della geometria delle alette per migliorare la sostenibilità dell’aviazione. DEEPCONTROL eseguirà anche esperimenti complessi nella galleria del vento presso il KTH Royal Institute of Technology, in Svezia, allo scopo di valutare questo quadro per applicazioni in tempo reale.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
100 44 Stockholm
Svezia