Turbulent mixing is a primary constituent of a huge variety of different phenomena, such as the diffusion of pollutants in the atmosphere or the blending of chemical reactants. Further investigation is needed to better understand its properties, especially its ability to enhance transport. The Lagrangian perspective appears appropriate for this kind of study, since it makes it possible to directly follow the motion of tracers which experience mixing. Complex networks have been already successfully applied to a range of different fields, including biology and social network. Their exploitation in the study of turbulence is rather recent but promising, since they allow one to properly represent the temporal and spatial complexity of turbulent flows and are an innovative tool to enrich classical statistical analysis.
Network-based investigation of turbulent mixing in an inhomogeneous flow / Perrone, Davide; Kuerten, Hans; Ridolfi, Luca; Scarsoglio, Stefania. - ELETTRONICO. - (2021), pp. 1295-1296. (Intervento presentato al convegno ICTAM 2021 tenutosi a Online nel August 22-27, 2021).
Network-based investigation of turbulent mixing in an inhomogeneous flow
Davide Perrone;Luca Ridolfi;Stefania Scarsoglio
2021
Abstract
Turbulent mixing is a primary constituent of a huge variety of different phenomena, such as the diffusion of pollutants in the atmosphere or the blending of chemical reactants. Further investigation is needed to better understand its properties, especially its ability to enhance transport. The Lagrangian perspective appears appropriate for this kind of study, since it makes it possible to directly follow the motion of tracers which experience mixing. Complex networks have been already successfully applied to a range of different fields, including biology and social network. Their exploitation in the study of turbulence is rather recent but promising, since they allow one to properly represent the temporal and spatial complexity of turbulent flows and are an innovative tool to enrich classical statistical analysis.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2919132