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Computational modeling of scalar transport and buoyancy effects in turbulent flows using ODTLES
(2012)
We summarize the group’s progress in applying, analyzing, and improving ODT and ODT-based stochastic turbulence models
like ODTLES. Compared to DNS these models span a wider range of scales while compared to RANS/LES (i) the molecular
effects are retained and (ii) no assumption of scale separation is made. In this regard ODTLES has more properties of DNS than of standard LES.
Direct numerical simulation (DNS), mostly used in fundamental turbulence research, is limited to low turbulent intensities due the current and future computer resources. Standard turbulence models, like RaNS (Reynolds averaged Navier-Stokes) and LES (Large Eddy Simulation), are applied to flows in engineering, but they miss small scale effects, which are frequently of importance, see e.g. the whole area of reactive flows, flows with apparent Prandtl or Schmidt number effects, or even wall bounded flows. A recent alternative to these standard approaches is the one-dimensional turbulence (ODT) model, which is limited to 1D sub-domains. In two papers we will provide a generalized filter strategy, called XLES (extended LES), including a formal theory (part I) and one special approach in the XLES family of models, called ODTLES (in part II (see Glawe et al. (2015))). ODTLES uses an ODT sub-grid model to describe all turbulent scales not represented by XLES, which leaves the larger scales to be simulated in 3D. This allows a turbulence modeling approach with a 3D resolution mainly independent of the turbulent intensity. Thus ODTLES is able to compute highly turbulent flows in domains of moderate complexity affordably and including the full range of turbulent and diffusive scales. The convergence of XLES to DNS is shown and the unconventional XLES advection approach is investigated in basic numerical tests. In part II, highly turbulent channel and duct flow results are discussed and show the future potential of XLES and ODTLES.
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ODT augmented RaNS
(2023)