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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Machine-learning building-block-flow model for lar
 ge-eddy simulation - Adrian Lozano-Duran (Massachu
 setts Institute of Technology)
DTSTART;TZID=Europe/London:20230427T143000
DTEND;TZID=Europe/London:20230427T153000
UID:TALK198400AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/198400
DESCRIPTION:A wall/SGS model for large-eddy simulation (LES) i
 s proposed by devising the flow as a collection of
  building blocks whose information enables the pre
 diction of the wall stress. The core assumption of
  the model is that simple canonical flows contain 
 the essential physics to provide accurate wall-str
 ess predictions in more complex flows. The model i
 s constructed to predict wall-attached turbulence\
 , favorable/adverse pressure gradient turbulence\,
  separation\, statistically unsteady turbulence\, 
 and laminar flow. The approach is implemented usin
 g two interconnected artificial neural networks: a
  classifier\, which identifies the contribution of
  each building block in the flow\; and a predictor
 \, which estimates the wall stress via combination
  of the building-block units. The training data ar
 e directly obtained from wall-modeled LES with exa
 ct modeling for mean quantities to guarantee consi
 stency with the numerical discretization. The outp
 ut of the model is accompanied by the confidence i
 n the prediction. The model is validated in two re
 alistic aircraft-like configurations: High-lift Co
 mmon Research Model and NASA Juncture Flow Experim
 ent.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:
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