
The authors explore the possibility of simulating the grade-two fluid model in a geometry related to a contraction rheometer, and provide details on several key aspects of the computation. They show how the results can be used to determine the viscosity ν from experimental data, and explore the identifiability of the grade-two parameters α1 and α2 from experimental data.


coremsg

computational-analysis-of-a-contraction-rheometer-for-the-grade-two-fluid-model


Computational Analysis of a Contraction Rheometer for the Grade-Two Fluid Model


title_rewrite


The core message of this paper is to design a helicity-conservative physics-informed neural network (PINN) model for solving the incompressible Navier-Stokes equations, which can exactly preserve the fluid helicity without any discretization error.


helicity-preserving-physics-informed-neural-network-model-for-incompressible-navier-stokes-equations


Helicity-Preserving Physics-Informed Neural Network Model for Incompressible Navier-Stokes Equations
