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arXiv · 2312.12412

Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators

Mohammad Atif·Vanessa López-Marrero·Tao Zhang·Abdullah Al Muti Sharfuddin·Kwangmin Yu·Jiaqi Yang·Fan Yang·Foluso Ladeinde·Yangang Liu·Meifeng Lin·Lingda Li
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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.

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Mohammad Atif, Vanessa López-Marrero, Tao Zhang, Abdullah Al Muti Sharfuddin, Kwangmin Yu, Jiaqi Yang, Fan Yang, Foluso Ladeinde, Yangang Liu, Meifeng Lin, Lingda Li. 2023-12-19. Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators. https://arxiv.org/abs/2312.12412

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