CDS&E: AI-RHEO: Learning coarse-graining of complex fluids

University Of Texas At Austin, Austin TX

Investigators

Abstract

Stokesian complex fluid flows describe transport phenomena at small scales. Examples include flows inside biological cells, blood flow in capillaries and microfluidic devices, DNA hydrodynamics, lab-on-a-chip industrial and medical devices, bacterial flows, and polymer flows. Understanding and predicting the behavior of Stokesian complex fluids using numerical simulations is fundamental in understanding mechano-biological mechanisms, design of microfluidic devices, medical robotics, and many other applications. Complex fluid flows are challenging to simulate because they involve solid-fluid interaction, moving interfaces and complex geometries, non-local and multiscale couplings in both space and time, and highly nonlinear and often chaotic dynamics. This project focuses on the development of methods that will significantly advance the state-of-the-art of complex fluid simulation technologies. The specific goals of this project include the following. (A) The design of high-performance computing (HPC) algorithms that integrate dimension reduction and deep learning methods with integral equation methods and result in orders-of-magnitude speedups of predictive simulations of Stokesian complex fluid flows. (B) The design and deployment of HPC software infrastructure that automates configuration sampling, operator splitting, deep network training, and inference for a large class of complex fluids. (C) Evaluation of the proposed methodology on three problems: calculation of effective properties, parameter estimation, and shape optimization of microfluidic devices. The complex fluid solvers developed in this project, will impact a broad spectrum of disciplines in sciences and engineering that involve problems with moving interfaces and microstructure evolution. Furthermore, the project aims to engage a number of graduate and undergraduate students at the interface of HPC, machine and deep learning, integral equations, and complex fluids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.