Towards accelerating particle‐resolved direct numerical simulation with neural operators

• Published in: Statistical analysis and data mining Vol. 17; no. 3
• Main Authors: Atif, Mohammad, López‐Marrero, Vanessa, Zhang, Tao, Sharfuddin, Abdullah Al Muti, Yu, Kwangmin, Yang, Jiaqi, Yang, Fan, Ladeinde, Foluso, Liu, Yangang, Lin, Meifeng, Li, Lingda
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.06.2024
• Subjects: fluid dynamics; machine learning; neural operators; particle resolved direct numerical simulation
• ISSN: 1932-1864; 1932-1872
• DOI: 10.1002/sam.11690

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.