Towards accelerating particle‐resolved direct numerical simulation with neural operators

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...

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Bibliographic Details
Published in:Statistical analysis and data mining 2024-06, Vol.17 (3), p.n/a
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: Article
Language:English
Subjects:
ENVIRONMENTAL SCIENCES
fluid dynamics
machine learning
neural operators
particle resolved direct numerical simulation
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Summary: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.
ISSN:1932-1864
1932-1872
DOI:10.1002/sam.11690