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4 papers · 1 filter
Combining direct and indirect sparse data for learning generalizable turbulence models
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
Learning turbulence models from observation data is of significant interest in discovering a unified model for a broad range of practical flow applications. Either the direct obser…
Ensemble Kalman method for learning turbulence models from indirect observation data
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven tu…
Impact of Time-Dependent Wettability Alteration on the Dynamics of Capillary Pressure
Abay M. Kassa, Sarah E. Gasda, K. Kumar +1
Wettability is a pore-scale property that has an important impact on capillarity, residual trapping, and hysteresis in porous media systems. In many applications, the wettability o…
An upscaled model for permeable biofilm in a thin channel and tube
David Landa-Marbán, Gunhild Bødtker, Kundan Kumar +2
In this paper, we derive upscaled equations for modelling biofilm growth in porous media. The resulting macro-scale mathematical models consider permeable multi-species biofilm inc…