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Towards Enabling Digital Twins Capabilities for a Cloud Chamber
SessionPoster Reception
DescriptionParticle-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.
Authors
Jiaqi Yang
Emory University
Mohammad Atif
Brookhaven National Laboratory
Vanessa Lopez-Marrero
Brookhaven National Laboratory
Tao Zhang
Brookhaven National Laboratory
Kwang Min Yu
Brookhaven National Laboratory
Meifeng Lin
Brookhaven National Laboratory
Lingda Li
Brookhaven National Laboratory
Fan Yang
Brookhaven National Laboratory
Yangang Liu
Brookhaven National Laboratory
Abdullahalmut Sharfuddin
Stony Brook University
Foluso Ladeinde
Stony Brook University
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
LocationDEF Concourse
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