Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology
arXiv (Cornell University)(2023)
摘要
We optimize the jet mixing using large eddy simulations (LES) at a Reynolds
number of 3000. Key methodological enablers consist of Bayesian optimization,
a surrogate model enhanced by deep learning, and persistent data topology for
physical interpretation. The mixing performance is characterized by an
equivalent jet radius (R_ eq) derived from the streamwise velocity in a
plane located 8 diameters downstream. The optimization is performed in a
22-dimensional actuation space that comprises most known excitations. The plant
benefits from a 22-dimensional actuation space that comprises most known
excitations. This search space parameterizes distributed actuation imposed on
the bulk flow and at the periphery of the nozzle in the streamwise and radial
directions. The momentum flux measures the energy input of the actuation. The
optimization quadruples the jet radius R_ eq with a 7-armed blooming
jet after around 570 evaluations. The control input requires 2% momentum
flux of the main flow, which is one order of magnitude lower than an ad hoc
dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space
is associated with a double-helix jet, a new flow pattern. This jet pattern
results in a mixing improvement comparable to the blooming jet. A
state-of-the-art Bayesian optimization converges towards this double helix
solution. The learning is accelerated and converges to another better optimum
by including surrogate model trained along the optimization. Persistent data
topology extracts the global and many local minima in the actuation space.
These minima can be identified with flow patterns beneficial to the mixing.
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关键词
jet,mixing,deep learning,bayesian optimization,topology
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