PySDM v1: particle-based cloud modelling package for warm-rain microphysics and aqueous chemistry

PySDM is an open-source Python package for simulating the dynamics of particles undergoing condensational and collisional growth, interacting with a fluid flow and subject to chemical composition changes. It is intended to serve as a building block for process-level as well as computational-fluid-dy...

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Vydáno v:arXiv.org
Hlavní autoři: Bartman, Piotr, Bulenok, Oleksii, Górski, Kamil, Jaruga, Anna, Łazarski, Grzegorz, Olesik, Michael, Piasecki, Bartosz, Singer, Clare E, Talar, Aleksandra, Arabas, Sylwester
Médium: Paper
Jazyk:angličtina
Vydáno: Ithaca Cornell University Library, arXiv.org 22.10.2021
Témata:
Algorithms
Atmospheric models
Chemical composition
Computational fluid dynamics
Fluid flow
Microphysics
Monte Carlo simulation
Representations
Two dimensional flow
Two dimensional models
ISSN:2331-8422
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Popis
Shrnutí:PySDM is an open-source Python package for simulating the dynamics of particles undergoing condensational and collisional growth, interacting with a fluid flow and subject to chemical composition changes. It is intended to serve as a building block for process-level as well as computational-fluid-dynamics simulation systems involving representation of a continuous phase (air) and a dispersed phase (aerosol), with PySDM being responsible for representation of the dispersed phase. The PySDM package core is a Pythonic high-performance implementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm for representing collisional growth, hence the name. PySDM has two alternative parallel number-crunching backends available: multi-threaded CPU backend based on Numba and GPU-resident backend built on top of ThrustRTC. The usage examples are built on top of four simple atmospheric cloud modelling frameworks: box, adiabatic parcel, single-column and 2D prescribed flow kinematic models. In addition, the package ships with tutorial code depicting how PySDM can be used from Julia and Matlab.
Bibliografie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2103.17238