Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels. (arXiv:2201.07835v1 [cs.LG])

Accurate pressure drop estimation in forced boiling phenomena is important
during the thermal analysis and the geometric design of cryogenic heat
exchangers. However, current methods to predict the pressure drop have one of
two problems: lack of accuracy or generalization to different situations. In
this work, we present the correlated-informed neural networks (CoINN), a new
paradigm in applying the artificial neural network (ANN) technique combined
with a successful pressure drop correlation as a mapping tool to predict the
pressure drop …

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