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DTSTART:19700308T020000
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DTSTAMP:20190719T085744Z
LOCATION:HG F 1
DTSTART;TZID=Europe/Stockholm:20190613T164500
DTEND;TZID=Europe/Stockholm:20190613T171500
UID:submissions.pasc-conference.org_PASC19_sess140_msa234@linklings.com
SUMMARY:Hierarchical Modeling for Simulating High Reynolds Number Wall-Bou
 nded Turbulent Flows: Toward Predictive Exascale Flow Simulations
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Physi
 cs, Engineering\n\nHierarchical Modeling for Simulating High Reynolds Numb
 er Wall-Bounded Turbulent Flows: Toward Predictive Exascale Flow Simulatio
 ns\n\nBalakrishnan\n\nOn massively parallel computing platforms, the focus
  has been on DNS and WRLES of canonical flows at increasing <em>Re</em>. W
 hile the intent behind these efforts has been to use the DNS/WRLES data to
  inform the development of sub-grid models, turbulence modeling efforts ha
 ve, regrettably, fallen by the wayside. Secondly, despite the promise of s
 uperior parallel scaling and higher arithmetic intensity, higher-order met
 hods for CFD have, to date, had much of their successes for low to moderat
 e Reynolds number simulations on relatively simple geometries. The bulk of
  the complex flow simulations still employ nominally second-order accurate
  schemes in unstructured mesh finite-volume flow solvers or finite-element
  based solvers with modest polynomial order for the basis functions. Hence
 , there is a pressing need for better sub-grid models that can improve the
  predictive capability of both the higher-order flow solvers and existing 
 second-order accurate flow solvers, for simulations of complex turbulent f
 lows. A hierarchical approach is required, whereby the DNS and WRLES of ca
 nonical flows inform the development of sub-grid models for hybrid RANS/LE
 S at the desired Reynolds number, and on the actual complex geometry. This
  talk presents an approach to create a framework for hierarchical subgrid 
 modeling for predictive simulation of high-Re flows.
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