Great success for FLOW researchers in the latest call of ERC Consolidator Grants
This year more than 2650 researchers applied for an ERC consolidator grant with a success rate of about 12 per cent (313 grants). Among the 15 researchers in Sweden receiving the grant, 7 are active in physical and engineering sciences. Lisa Prahl Wittberg and Ricardo Vinuesa are two out of the four at KTH to receive ERC Consolidator Grant this round.
Lisa and Ricardo will receive 2 million Euros each during the next five years.
The research project fitsCAN (Blood flow induced thrombosis and stenosis due to cannulation – an interdisciplinary study), focuses on the impact of cannulation techniques/strategies on treatment efficiency and complication risk in two lifesaving therapies: namely, hemodialysis and extracorporeal membrane oxygenation (ECMO). The project is strongly interdisciplinary, and it is carried out in close collaboration with specialists within renal medicine, intensive care/ECMO and radiology. The research will reduce treatment complication risks by improving the understanding of the underlying mechanisms behind clinically observed cannula related complications. Bases on the improved understanding, the project is also expected to lead to the further development of new models for clot and stenosis formation in blood carrying artificial components.
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 with aviation, in this project we will focus on improving the aerodynamic performance of airplane wings, with the aim of reducing the fuel consumption and emissions from air travel. We will perform very detailed computer simulations of the flow around three-dimensional wings, so as to understand the complex physical phenomena leading to their aerodynamic properties. Using this very detailed database, we will train artificial-intelligence models to predict the characteristics of the flow based on very few measurements on the wing surface. Having this information, we will be able to design a control (based on reinforcement learning) able to increase the aerodynamic efficiency of the wing. The control is based on actively injecting and removing flow through the wing surface, and it has the potential to revolutionize air travel with the possibility of significantly reducing airplane emissions.