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Machine learning has been evolving as a forefront of research across various fields in recent times. We have also ventured into this domain to explore its diverse potential applications and contributions.
Machine learning has been evolving as a forefront of research across various fields in recent times. We have also ventured into this domain to explore its diverse potential applications and contributions.
Fluid Structuration Explains a Story at the Nanoscale
We aim to unravel the key connecting nanoscale fluid structure interactions to the macroscopic slip lengths. For this, we perform EMD and NEMD simulations and discover that density distribution in the vicinity of the wall is the key to explain the patterns. This work is now available at The Journal of Chemical Physics.
More details at: http://dx.doi.org/10.1063/5.0140765
Neural Network helps Generate New Model for Roughness Induced Flow in Microchannels
We show how the ML can find the hidden patterns inside data and help to develop a new model. We perform extensive CFD simulations and utilise them to predict the pressure drop across the channels. This extensive data ultimately results in discovery of new model. This work is accepted in Physics of Fluids and will be available soon.
More details at: https://doi.org/10.1063/5.0208554
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