One test to predict them all: Rheological characterization of complex fluids via artificial neural network

Abstract

The rheological behavior of complex fluids, including thixotropy, viscoelasticity, and viscoplasticity, poses significant challenges in both measurement and prediction due to the transient nature of their stress responses. This study introduces an artificial neural network (ANN) designed to digitally characterize the rheology of complex fluids with unprecedented accuracy. By employing a data-driven approach, the ANN is trained using transient rheological tests with step inputs of shear rate. Once trained, the network adeptly captures the intricate dependencies of rheological properties on time and shear, enabling rapid and accurate predictions of various rheological tests. In contrast, traditional phenomenological structural kinetic constitutive models often fail to accurately describe the evolution of nonlinear rheological properties, particularly as material complexity increases. The ANN demonstrates high flexibility, reliability and robustness by accurately predicting transient rheology of varied materials with different shear histories. Our findings illustrate that ANNs can not only complement and validate traditional rheological characterization methods but also potentially replace them, thereby paving the way for more efficient material development and testing.