[en] Numerical simulations of multiphase flows are crucial in numerous engineering applications, but are often limited by the computationally demanding solution of the Navier-Stokes (NS) equations. The development of surrogate models relies on involved algebra and several assumptions. Here, we present a data-driven workflow where a handful of detailed NS simulation data are leveraged into a reduced-order model for a prototypical vertically falling liquid film. We develop a physics-agnostic model for the film thickness, achieving a far better agreement with the NS solutions than the asymptotic Kuramoto-Sivashinsky (KS) equation. We also develop two variants of physics-infused models providing a form of calibration of a low-fidelity model (i.e. the KS) against a few high-fidelity NS data. Finally, predictive models for missing data are developed, for either the amplitude, or the full-field velocity and even the flow parameter from partial information. This is achieved with the so-called 'gappy diffusion maps', which we compare favourably to its linear counterpart, gappy POD.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Martin-Linares, Cristina P.; Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, United States
Psarellis, Yorgos M.; Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, United States
Karapetsas, George ; Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
KORONAKI, Eleni ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Engineering > Team Stéphane BORDAS ; School of Chemical Engineering, National Technical University of Athens, Athens, Greece
Kevrekidis, Ioannis G.; Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, United States
External co-authors :
yes
Language :
English
Title :
Physics-agnostic and physics-infused machine learning for thin films flows: Modelling, and predictions from small data
This work was partially supported by the US AFOSR and by the US DOE (IGK). CML received the support of a \u2018la Caixa\u2019 Foundation Fellowship (ID 100010434), code LCF/BQ/AA19/11720048. EDK received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No 890676 - DataProMat.
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