TY - JOUR
T1 - Optimization of DNS code and visualization of entrainment and mixing phenomena at cloud edges
AU - Kumar, Bipin
AU - Rehme, Matt
AU - Suresh, Neethi
AU - Cherukuru, Nihanth
AU - Jaroszynski, Stanislaw
AU - Li, Samual
AU - Pearse, Scott
AU - Scheitlin, Tim
AU - Rao, Suryachandra A.
AU - Nanjundiah, Ravi S.
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/10
Y1 - 2021/10
N2 - Entrainment and mixing processes occur during the entire life of a cloud. These processes change the droplet size distribution, which determines rain formation and radiative properties. Since it is a microphysical process, it cannot be resolved in large scale weather forecasting models. Small scale simulations such as Direct Numerical Simulations (DNS) are required to resolve the most minute scale of these processes. The DNS of cloud dynamics are performed by integrating two mathematical models, Eulerian and Lagrangian, in a coupled way. Running DNS is a tedious task as it requires a huge amount of computational resources. In this work, we provide a projection of the required resources for running DNS in different size domains. Visualizing these large simulations presents an added challenge, as they generate petabytes of data. Visualization plays a vital role in analyzing and understanding these huge data outputs. Here, we experimented with multiple tools to conduct a visual analysis of this data. Two of these tools are well established and tested technologies: ParaView and VAPOR. The others are emergent technologies in the development phase. This data simulation and visualization, in addition to exploring DNS as mentioned above, provided an opportunity to test and improve development of several tools and methods.
AB - Entrainment and mixing processes occur during the entire life of a cloud. These processes change the droplet size distribution, which determines rain formation and radiative properties. Since it is a microphysical process, it cannot be resolved in large scale weather forecasting models. Small scale simulations such as Direct Numerical Simulations (DNS) are required to resolve the most minute scale of these processes. The DNS of cloud dynamics are performed by integrating two mathematical models, Eulerian and Lagrangian, in a coupled way. Running DNS is a tedious task as it requires a huge amount of computational resources. In this work, we provide a projection of the required resources for running DNS in different size domains. Visualizing these large simulations presents an added challenge, as they generate petabytes of data. Visualization plays a vital role in analyzing and understanding these huge data outputs. Here, we experimented with multiple tools to conduct a visual analysis of this data. Two of these tools are well established and tested technologies: ParaView and VAPOR. The others are emergent technologies in the development phase. This data simulation and visualization, in addition to exploring DNS as mentioned above, provided an opportunity to test and improve development of several tools and methods.
UR - https://www.scopus.com/pages/publications/85111270051
U2 - 10.1016/j.parco.2021.102811
DO - 10.1016/j.parco.2021.102811
M3 - Article
AN - SCOPUS:85111270051
SN - 0167-8191
VL - 107
JO - Parallel Computing
JF - Parallel Computing
M1 - 102811
ER -