A Spatiotemporal Deep Learning-Powered Electrohydrodynamic Framework for Fluid Viscosity Estimation

Fluid characterization is of paramount importance for understanding the behavior of materials in several fields, from biomedical to industrial applications. Fluidic properties, such as viscosity, provide information on fluid stability, flow behavior, and potential biomarkers of physiological conditions. In this context, this study proposes an artificial intelligence (AI)-based system to automatically retrieve rheological properties, in particular, fluid viscosity, by analyzing the fluid spatiotemporal (ST) dynamics that occur during the electrohydrodynamic (EHD) process. To this end, an ST deep learning network, based on a custom 3-D-convolutional neural network (3D-CNN), named ST-EHDeepNET, is developed to analyze sequences of frames capturing fluid droplet deformation caused by the EHD phenomena. ST-EHDeepNET simultaneously extracts spatial and temporal features from video recordings, enabling automated fluid viscosity estimation. Experimental results demonstrate the high predictive accuracy of the model root-mean-squared error (RMSE 0.1 ± 0.02), encouraging its application in challenging industrial and clinical scenarios.