We present a formulation of the physics-informed neural network (PINN) method for learning the effective viscosity of the generalized Newtonian fluid from measurements of velocity and pressure in time-dependent three-dimensional flows and apply it to estimating viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) in shear flow between two parallel plates using only velocity measurements from numerical simulations. The PINN-inferred viscosity models agree with empirical models for shear rates with large absolute values but deviate for shear rates near zero where empirical models have an unphysical singularity. We show that once the unknown physics is learned the PINN method can be used to solve the momentum conservation equation governing flow of non-Newtonian fluids.
ASJC Scopus subject areas
- Computational Mechanics
- Modeling and Simulation
- Fluid Flow and Transfer Processes