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UID:submissions.pasc-conference.org_PASC23_sess162_msa256@linklings.com
SUMMARY:A Scale Separation Approach: A Machine-Learned Surrogate Model for
  an Injector Coupled with Spray Simulations
DESCRIPTION:Minisymposium\n\nSudeepta Mondal, Gina M. Magnotti, Bethany Lu
 sch, Romit Maulik, and Roberto Torelli (Argonne National Laboratory)\n\nIn
  this work, we use machine learning to develop a surrogate model for an ex
 pensive portion of a computational fluid dynamics simulation. In particula
 r, we learn a surrogate model for fuel injection, predicting the flowfield
 s at the exit of an automotive injector. The output of the surrogate model
  is then the boundary conditions for reacting spray simulations. Static co
 upling between the injector flow and external spray via a spatiotemporal b
 oundary condition reduces the range of scales that need to be resolved for
  the spray domain. The surrogate model includes uncertainty quantification
  and is able to conserve the injected mass flow rate. The surrogate model 
 results in an O(10^6) speedup for predicting internal flow fields for a ne
 w test case, and an O(10) speedup for the full simulation.\n\nDomain: Comp
 uter Science, Machine Learning, and Applied Mathematics 
\n\nSession Chair
 s: Timothy C Germann (Los Alamos National Laboratory) and Ramesh Balakrish
 nan (Argonne National Laboratory)
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