Authors: Jiaqi Yang (Emory University); Mohammad Atif, Vanessa Lopez-Marrero, Tao Zhang, Kwang Min Yu, Meifeng Lin, Lingda Li, Fan Yang, and Yangang Liu (Brookhaven National Laboratory); and Abdullahalmut Sharfuddin and Foluso Ladeinde (Stony Brook University)
Abstract: Particle-resolved direct numerical simulations (PR-DNS), which resolve not only the smallest turbulent eddies but also track the development and motion of individual particles, are arguably an essential tool for exploring aerosol-cloud-turbulence interactions at the fundamental level. For instance, PR-DNS may complement experimental facilities designed to study key physical processes in a controlled environment and therefore serve as digital twins for such cloud chambers. In this poster we present our ongoing work aimed at enabling the use of a PR-DNS model for this purpose. We consider two approaches: traditional HPC techniques and emerging machine learning methods. Future research directions are outlined as well.
Best Poster Finalist (BP): no
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