Generalized Lagrangian Heterogenous Multiscale Modeling of Complex Fluids

TL;DR

This paper presents a multiscale modeling approach for complex fluids that integrates microscopic SDPD simulations with macroscopic Navier-Stokes equations, capturing thermal fluctuations and complex rheology without relying on predefined constitutive laws.

Contribution

The authors introduce a full-Lagrangian heterogeneous multiscale method that directly derives stress tensors from microscopic SDPD simulations, enabling accurate modeling of complex fluids at multiple scales.

Findings

Successfully modeled complex fluids including multiphase and polymeric systems.

Validated the method with various flow configurations and fluid types.

Captured micro-to-macro stress transfer and thermal fluctuations effectively.

Abstract

We introduce a full-Lagrangian heterogeneous multiscale method (LHMM) to model complex fluids with microscopic features that can extend over large spatio-temporal scales, such as polymeric solutions and multiphasic systems. The proposed approach discretizes the fluctuating Navier-Stokes equations in a particle-based setting using Smoothed Dissipative Particle Dynamics (SDPD). This multiscale method uses microscopic information derived on-the-fly to provide the stress tensor of the momentum balance in a macroscale problem, therefore bypassing the need for approximate constitutive relations for the stress. We exploit the intrinsic multiscale features of SDPD to account for thermal fluctuations as the characteristic size of the discretizing particles decrease. We validate the LHMM using different flow configurations (reverse Poiseuille flow, flow passing a cylinder array, and flow around a square cavity) and fluid (Newtonian and non-Newtonian). We showed the framework's flexibility to model complex fluids at the microscale using multiphase and polymeric systems. We showed that stresses are adequately captured and passed from micro to macro scales, leading to richer fluid response at the continuum. In general, the proposed methodology provides a natural link between variations at a macroscale, whereas accounting for memory effects of microscales.