DeePN$^2$: A deep learning-based non-Newtonian hydrodynamic model

TL;DR

DeePN$^2$ is a deep learning model that captures complex non-Newtonian polymer flow behaviors by encoding micro-scale molecular dynamics into macro-scale features, improving modeling accuracy without manual intervention.

Contribution

This work extends DeePN$^2$ to more complex micro-structural models, enabling accurate, interpretable non-Newtonian hydrodynamics modeling of polymer suspensions.

Findings

Successfully captures viscoelastic differences from molecular mechanics

Faithfully models complex micro-structural effects

Retains multi-scale features in hydrodynamic predictions

Abstract

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN$^2$, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN$^2$ using more complex micro-structural models. We show that DeePN$^2$ can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention.