Machine-learning-based measurement of relaxation time via particle ordering

The rheological characterization of complex liquids is of great importance in many applications. Among the properties that can be measured, the relaxation time has great relevance, as it provides a measure of fluid elasticity. In this work, we propose a novel method to estimate the longest relaxation time of viscoelastic fluids by applying machine learning to microfluidics. Specifically, we train a long-short term memory (LSTM) neural network to identify the Weissenberg number that characterizes the dynamics of trains of rigid particles suspended in a viscoelastic liquid flowing in a cylindrical microchannel. We first study the effect of the Weissenberg number on the evolution of the microstructure through numerical simulations. An in silico dataset consisting of the distributions of the interparticle distances at different channel sections is built and used to train the network. The performance of the LSTM model is tested on both classification and regression problems. The proposed method is nonintrusive, requires a simple setup, and can in principle be used to measure other properties of the fluid.