Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators
Abstract
We present our ongoing work aimed at accelerating a particle-resolved direct numerical simulation model designed to study aerosol-cloud-turbulence interactions. The dynamical model consists of two main components - a set of fluid dynamics equations for air velocity, temperature, and humidity, coupled with a set of equations for particle (i.e., cloud droplet) tracing. Rather than attempting to replace the original numerical solution method in its entirety with a machine learning (ML) method, we consider developing a hybrid approach. We exploit the potential of neural operator learning to yield fast and accurate surrogate models and, in this study, develop such surrogates for the velocity and vorticity fields. We discuss results from numerical experiments designed to assess the performance of ML architectures under consideration as well as their suitability for capturing the behavior of relevant dynamical systems.
Cite
@article{arxiv.2312.12412,
title = {Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators},
author = {Mohammad Atif and Vanessa López-Marrero and Tao Zhang and Abdullah Al Muti Sharfuddin and Kwangmin Yu and Jiaqi Yang and Fan Yang and Foluso Ladeinde and Yangang Liu and Meifeng Lin and Lingda Li},
journal= {arXiv preprint arXiv:2312.12412},
year = {2023}
}