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Using HPC/AI-Accelerated Particle-Resolved Direct Numerical Simulations to Study Microphysics-Turbulence Interactions in Clouds

Urheber*innen

Liu,  Yangang
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Zhang,  Tao
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Yu,  Kwang Min
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lopez-Marrero,  Vanessa
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Atif,  Mohammad
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lin,  Meifeng
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Li,  Lingda
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Yang,  Fan
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Sharfuddin,  Abdullah
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Ladeinde,  Foluso
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Zitation

Liu, Y., Zhang, T., Yu, K. M., Lopez-Marrero, V., Atif, M., Lin, M., Li, L., Yang, F., Sharfuddin, A., Ladeinde, F. (2023): Using HPC/AI-Accelerated Particle-Resolved Direct Numerical Simulations to Study Microphysics-Turbulence Interactions in Clouds, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2622


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019272
Zusammenfassung
Clouds continue to pose challenges to predict weather, climate and renewable energy due partly to knowledge gaps in microphysics-turbulence interactions. Particle-resolved direct numerical simulations (PR-DNS) that not only resolve the smallest turbulent eddies but also track growing histories of individual particles arguably constitute a fundamental tool to address the special challenges facing microphysics-turbulence interactions in clouds. This study consists of two parts. First, a major bottleneck issue of existing PR-DNS models is the small model domain size (e.g., less or about 1m3) due to high computational cost, which is smaller than many energy-containing eddies and typical grid sizes of large-eddy simulation (LES) models. We will introduce our HPC/AI accelerated PR-DNS model that aims to simulate a domain size of ~ 103m3 to address this computational challenge. Second, we will apply this PR-DNS to investigate three outstanding microphysics-turbulence problems: stochastic condensation/evaporation, turbulent entrainment-mixing, and droplet clustering. Results under different turbulence intensity (e.g., energy dissipation rate), environment conditions (e.g., relative humidity), and microphysical properties (e.g., initial droplet concentration and spectral shape of cloud droplet size distributions) will be analyzed. Also explored will be the role of turbulence-induced supersaturation fluctuations in determining aerosol activation into cloud droplets and droplet deactivation into aerosol particles. The AI/HPC accelerated PR-DNS further allows for examination of dependence of the results on the DNS domain size (or Reynolds number), by running the model with the domain spanning over several order of magnitudes (linear dimension varying from a few centimeters to about 10 meter).