Understanding the rheology of kaolinite clay suspensions using Bayesian inference
Abstract
Mud is a suspension of fine-grained particles (sand, silt, and clay) in water. The interaction of clay minerals in mud gives rise to complex rheological behaviors, such as yield stress, thixotropy, and viscoelasticity. Here, we experimentally examine the flow behaviors of kaolinite clay suspensions, a model mud, using steady shear rheometry. The flow curves exhibit both yield stress and rheological hysteresis behaviors for various kaolinite volume fractions (ϕk). Further understanding of these behaviors requires fitting to existing constitutive models, which is challenging due to numerous fitting parameters. To this end, we employ a Bayesian inference method, Markov chain Monte Carlo, to fit the experimental flow curves to a microstructural viscoelastic model. The method allows us to estimate the rheological properties of the clay suspensions, such as viscosity, yield stress, and relaxation time scales. The comparison of the inherent relaxation time scales suggests that kaolinite clay suspensions are strongly viscoelastic and weakly thixotropic at relatively low ϕk, while being almost inelastic and purely thixotropic at high ϕk. Overall, our results provide a framework for predictive model fitting to elucidate the rheological behaviors of natural materials and other structured fluids.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 17, 2022
- Source ID
- 10.1122/8.0000556
Entities
People
- Brendan C. Blackwell
- Christoph Kammer
- Douglas J. Jerolmack
- Paulo E Arratia
- Ranjiangshang Ran
- Shravan Pradeep
- Sébastien Kosgodagan Acharige
Organizations
- Army Research Office
- National Science Foundation
- University of Pennsylvania