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Unraveling the Secrets of Turbulence in a Fluid Puff.
Mazzino, Andrea; Rosti, Marco Edoardo.
  • Mazzino A; Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genova, Via Montallegro 1, 16145 Genova, Italy; INFN, Genova Section, Via Montallegro 1, 16145 Genova, Italy.
  • Rosti ME; Complex Fluids and Flows Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Okinawa 904-0495, Japan.
Phys Rev Lett ; 127(9): 094501, 2021 Aug 27.
Article in English | MEDLINE | ID: covidwho-1429386
ABSTRACT
Turbulent puffs are ubiquitous in everyday life phenomena. Understanding their dynamics is important in a variety of situations ranging from industrial processes to pure and applied science. In all these fields, a deep knowledge of the statistical structure of temperature and velocity space-time fluctuations is of paramount importance to construct models of chemical reaction (in chemistry) and of condensation of virus-containing droplets (in virology and/or biophysics) and optimal mixing strategies in industrial applications. As a matter of fact, results of turbulence in a puff are confined to bulk properties (i.e., average puff velocity and typical decay or growth time) and date back to the second half of the 20th century. There is, thus, a huge gap to fill to pass from bulk properties to two-point statistical observables. Here, we fill this gap by exploiting theory and numerics in concert to predict and validate the space-time scaling behaviors of both velocity and temperature structure functions including intermittency corrections. Excellent agreement between theory and simulations is found. Our results are expected to have a profound impact on developing evaporation models for virus-containing droplets carried by a turbulent puff, with benefits to the comprehension of the airborne route of virus contagion.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Biological / Models, Theoretical Type of study: Prognostic study Language: English Journal: Phys Rev Lett Year: 2021 Document Type: Article Affiliation country: PhysRevLett.127.094501

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Models, Biological / Models, Theoretical Type of study: Prognostic study Language: English Journal: Phys Rev Lett Year: 2021 Document Type: Article Affiliation country: PhysRevLett.127.094501