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DTSTAMP:20220812T074334Z
LOCATION:Singapore Room
DTSTART;TZID=Europe/Stockholm:20220627T150000
DTEND;TZID=Europe/Stockholm:20220627T153000
UID:submissions.pasc-conference.org_PASC22_sess118_msa197@linklings.com
SUMMARY:A Framework for In-Situ Estimation of Time-Averaging Uncertaintie
 s in Turbulent Flow Simulations
DESCRIPTION:Minisymposium\n\nA Framework for In-Situ Estimation of Time-A
 veraging Uncertainties in Turbulent Flow Simulations\n\nGscheidle, Rezaeir
 avesh, Garcke, Schlatter\n\nStatistical terms in turbulent flow simulation
 s are computed combining different sample-mean estimators (SMEs) that are,
  however, uncertain due to the finite number of time samples. The main cha
 llenges for accurately quantifying uncertainty in SMEs are to account for 
 the autocorrelations in turbulence time-series and at the same time avoidi
 ng frequently saving the time-series samples which reduces the computation
 al efficiency. To meet these requirements, we have recently developed a fr
 amework for in-situ (online/streaming) estimation of the time-averaging un
 certainties in turbulence. The components involved are low-storage updatin
 g UQ (uncertainty quantification) algorithms, adapters for online sampling
  from a CFD (computational fluid dynamics) solver, and interfaces between 
 the CFD and UQ suites. The UQ algorithms include the updating version of t
 he batch-based methods, as well as a novel updating method for modeling th
 e autocorrelation function. Although the framework is independent of the f
 low solver, the tests have been performed using Nek5000, which is an open-
 source highly-scalable spectral-element CFD code. The accuracy and perform
 ance of the in-situ UQ approaches are investigated on the three-dimensiona
 l flow over a cylinder and wing. Details of the UQ techniques and implemen
 tations, and also the performance analysis of the framework as applied to 
 large-scale simulations will be discussed.\n\nDomain: Computer Science and
  Applied Mathematics, Engineering
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