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MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows

MPG-Autoren
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van Heerwaarden,  Chiel
Max Planck Research Group Turbulent Mixing Processes in the Earth System, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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van Stratum,  Bart J. H.
Climate Modelling, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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Mellado,  Juan-Pedro
Max Planck Research Group Turbulent Mixing Processes in the Earth System, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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gmd-10-3145-2017.pdf
(Verlagsversion), 932KB

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Zitation

van Heerwaarden, C., van Stratum, B. J. H., Heus, T., Gibbs, J. A., Fedorovich, E., & Mellado, J.-P. (2017). MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows. Geoscientific Model Development, 10, 3145-3165. doi:10.5194/gmd-10-3145-2017.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002D-E494-0
Zusammenfassung
This paper describes MicroHH 1.0, a new and open-source computational fluid dynamics code for the simulation of turbulent flows in the atmosphere. It is primarily made for direct numerical simulation but also supports large-eddy simulation (LES). The paper covers the description of the governing equations, their numerical implementation, and the parameterizations included in the code. Furthermore, the paper presents the validation of the dynamical core in the form of convergence and conservation tests, and comparison of simulations of channel flows and slope flows against well-established test cases. The full numerical model, including the associated parameterizations for LES, has been tested for a set of cases under stable and unstable conditions, under the Boussinesq and anelastic approximations, and with dry and moist convection under stationary and time-varying boundary conditions. The paper presents performance tests showing good scaling from 256 to 32768 processes. The graphical processing unit (GPU)-enabled version of the code can reach a speedup of more than an order of magnitude for simulations that fit in the memory of a single GPU.