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SUMMARY:Data-driven discovery of flow characteristics enhancing plug-flow 
 performance - Nausheen Basha (Imperial College London)
DTSTART:20230427T123000Z
DTEND:20230427T133000Z
UID:TALK198439@talks.cam.ac.uk
DESCRIPTION:Optimisation based on surrogate models is becoming popular for
  engineering problems due to its reduced computational efforts. In this re
 search\, we aim to maximise the plug flow performance of coiled reactors o
 perating under oscillating conditions for a fixed geometry. This is done t
 hrough Bayesian optimisation that uses Gaussian processes as a surrogate m
 odel and is coupled with computational fluid dynamics (CFD) simulations in
  OpenFOAM through the PyFoam library. We run a transient analysis with Sca
 larTransportFoam solver where the tracer is injected into the water as a w
 orking fluid to obtain residence time distribution which is then fitted wi
 th the tank-in-series model to get the plug flow performance. We explore t
 he parameter space for amplitude (1-8 mm) and frequency (2-8 Hz) for a fix
 ed Reynolds number of 50. The optimal conditions for plug-flow performance
  correspond to the Strouhal number St > 1 and oscillatory Reynolds number 
 Re0
LOCATION:Seminar Room 1\, Newton Institute
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