A mass transport study on the turbulent flow after a cylinder using recurrence CFD

  • Sanaz Abbasi (Speaker)

Activity: Talk or presentationContributed talkscience-to-science

Description

Even though the application of computational fluid dynamics (CFD) spans a wide range from scientific studies to complex industrial-scale problems, it is still an open question how to best reduce the high computational cost of simulations to make the long-term investigations possible. A new proposed method called recurrence CFD (rCFD) aims to tackle this problem by extrapolating a system’s behavior for a long time just based on a short-time full simulation. It can be observed that many flows show reappearing structures after evolving over some time, e.g. vortices behind blunt bodies. In order to have comparable data about such system’s degree of periodicity, first we do a short-term detailed simulation which covers several recurrent patterns, then we compare the flow field at two times by carrying out a recurrence analysis. Here, the turbulent flow over the cylinder has been studied by performing large eddy simulations (LES) using OpenFOAM’s pisoFoam solver with conventional Smagorinsky as the sub-grid scale model. Consequently, according to the recurrence statistics acquired from the full simulation, a recurrence process is deduced which enables the time extrapolation of fluid flow for much longer times. Therefore, with the information of the extended flow field, species transport can be traced by rCFD with solving a passive scalar transport equation. It is notable that our recent study on the mass transport by rCFD is in a good agreement with LES results with a significant difference in run- times. The rCFD method allows us to increase the temporal and spatial scales, and as a result the simulation gets approximately 125 times faster than LES in this work.
Period11 Sept 2018
Event titleEFMC12 – 12th European Fluid Mechanics Conference
Event typeConference
LocationAustriaShow on map

Fields of science

  • 203 Mechanical Engineering

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Engineering and Natural Sciences (in general)