Learning Unknown Physics of non-Newtonian Fluids

• URL: http://arxiv.org/abs/2009.01658v1
• Date: Wed, 26 Aug 2020 20:41:36 GMT
• Title: Learning Unknown Physics of non-Newtonian Fluids
• Authors: Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
• Abstract summary: We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero. We use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.
• Score: 56.9557910899739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero where the analytical models have an unphysical singularity. Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.

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