Paper Number
VP35 

Session
Pre-recorded Flash Presentations (virtual)

Title
On simultaneous fitting of nonlinear and linear rheology data: Preventing a false sense of certainty

Presentation Date and Time
All Week (Asynchronous) Any Time

Track / Room
Pre-recorded Presentation / Virtual

Authors (Click on name to view author profile)

1. Singh, Piyush K. (University of Illinois at Urbana-Champaign, Department of Chemical and Biomolecular Engineering)
2. Ewoldt, Randy H. (University of Illinois at Urbana-Champaign, Department of Mechanical Science and Engineering)

Author and Affiliation Lines (in printed abstract book)
Piyush K. Singh¹ and Randy H. Ewoldt^2
1Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801; ²Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801

Speaker / Presenter 
Singh, Piyush K.

Keywords
theoretical methods; applied rheology; non-Newtonian fluids; polymer melts; rheology methods

Text of Abstract
Model parameter estimates and their uncertainties change depending on the fitting method used, yet proper uncertainty quantification is critical when fitting constitutive models for either inferring molecular information or for flow predictions. Here we study and compare sequential (two-step) versus simultaneous (all at once) fitting methods with linear and weakly-nonlinear rheological data. Using an example of a combined dataset on small-amplitude oscillatory shear (SAOS) and medium-amplitude oscillatory shear (MAOS) for a linear entangled polymer melt (cis-1,4-polyisoprene), we demonstrate with a multi-mode Giesekus model how the fit parameter uncertainties are significantly under-estimated with the sequential fit because of the neglect of model parameter correlations. These results are somewhat surprising given the combination of linear and weakly nonlinear data because one might expect linear data to dominate and result in minimal differences between sequential and simultaneous fitting. The multi-mode model admits low-dimensional average measures in terms of moments of the spectra, and here we derive meaningful average measures of the mobility parameter spectrum of the Giesekus model. These average metrics can show similar uncertainty estimates for sequential and simultaneous fitting with this particular dataset, which we quantify. Our results reveal the importance of using simultaneous fitting for material property inference, even with weakly-nonlinear data.