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SUMMARY:Machine-learning building-block-flow model for large-eddy simulati
 on - Adrian Lozano-Duran (Massachusetts Institute of Technology)
DTSTART:20230427T133000Z
DTEND:20230427T143000Z
UID:TALK198400@talks.cam.ac.uk
DESCRIPTION:A wall/SGS model for large-eddy simulation (LES) is proposed b
 y devising the flow as a collection of building blocks whose information e
 nables the prediction of the wall stress. The core assumption of the model
  is that simple canonical flows contain the essential physics to provide a
 ccurate wall-stress predictions in more complex flows. The model is constr
 ucted to predict wall-attached turbulence\, favorable/adverse pressure gra
 dient turbulence\, separation\, statistically unsteady turbulence\, and la
 minar flow. The approach is implemented using two interconnected artificia
 l neural networks: a classifier\, which identifies the contribution of eac
 h building block in the flow\; and a predictor\, which estimates the wall 
 stress via combination of the building-block units. The training data are 
 directly obtained from wall-modeled LES with exact modeling for mean quant
 ities to guarantee consistency with the numerical discretization. The outp
 ut of the model is accompanied by the confidence in the prediction. The mo
 del is validated in two realistic aircraft-like configurations: High-lift 
 Common Research Model and NASA Juncture Flow Experiment.
LOCATION:Seminar Room 1\, Newton Institute
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