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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160559Z
LOCATION:Track 11
DTSTART;TZID=America/New_York:20201113T115000
DTEND;TZID=America/New_York:20201113T121500
UID:submissions.supercomputing.org_SC20_sess229_ws_ai4s101@linklings.com
SUMMARY:Predictions of Steady and Unsteady Flows Using Machine-Learned Sur
 rogate Models
DESCRIPTION:Workshop\n\nPredictions of Steady and Unsteady Flows Using Mac
 hine-Learned Surrogate Models\n\nBhushan\n\nThe research focuses on invest
 igation of the ability of neural networks to learn correlation between des
 ired modeling variables and flow parameters, thereby providing a surrogate
  model that can used in computational fluid dynamics (CFD) simulations. In
 vestigation is performed for two different classes of problem: turbulence 
 modeling and rotor modeling. For the turbulence modeling, a machine-learne
 d model is applied for unsteady boundary layer flow, and the predictions a
 re validated against direct numerical simulation (DNS) data and compared w
 ith 1-Eq. unsteady Reynolds Averaged Navier-Stokes (URANS) results. The ma
 chine-learned model performs much better than the 1-Eq. model due to its a
 bility to incorporate the non-linear correlation between the turbulent str
 esses and rate-of-strain. The ongoing research is focusing on generating a
  larger high-fidelity solution database encompassing steady and unsteady b
 oundary layer flows to train a generic machine-learned regression map, inc
 luding investigation of approaches for data reductions. The development of
  the surrogate rotor model builds on the hypothesis that if a model can mi
 mic the axial and tangential momentum deficit generated by a blade resolve
 d model, then it should produce a qualitatively and quantitatively similar
  wake recovery. An initial validation of the hypothesis was performed, and
  the ongoing research is focusing on development of a regression map for t
 he rotor axial and tangential momentum deficit. The final version of the p
 aper will include results from the ongoing work.\n\nRegistration Category:
  Workshop Reg Pass
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