Simulation of a conventionally neutral boundary layer with two-equation URANS

Simulating conventionally neutral boundary layers (CNBLs) with the unsteady Reynolds-Averaged Navier-Stokes (URANS) technique is investigated in this paper using a modified two-equation linear eddy viscosity turbulence model. For CNBLs over a flat and uniform surface, as typically used as the inflow...

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Bibliographic Details
Published in:Journal of physics. Conference series 2024-06, Vol.2767 (5), p.52013
Main Authors: Baungaard, M, Van Der Laan, M P, Kelly, M, Hodgson, E L
Format: Article
Language:English
Subjects:
Anisotropy
Atmospheric boundary layer
Atmospheric correction
Eddy viscosity
Large eddy simulation
Modelling
Reynolds averaged Navier-Stokes method
Simulation
Turbulence models
Vortices
Wind power
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Summary:Simulating conventionally neutral boundary layers (CNBLs) with the unsteady Reynolds-Averaged Navier-Stokes (URANS) technique is investigated in this paper using a modified two-equation linear eddy viscosity turbulence model. For CNBLs over a flat and uniform surface, as typically used as the inflow to wind farm simulations, the governing equations of URANS can be solved with a one-dimensional solver, which makes the simulation of a typical CNBL five to six orders of magnitude faster than with large-eddy simulation (LES) approaches. However, URANS on the other hand requires more modelling than LES, and its accuracy is heavily dependent on the turbulence model employed. Through a cross-code study of a CNBL case with data from five different LES codes, it is found that the length-scale limiter of the employed turbulence model should be removed to correctly predict the atmospheric boundary layer (ABL) height evolution and the qualitative shape of various atmospheric profiles. A parametric study of simulations with varying initial ABL height further demonstrates the prediction capabilities of URANS, although a comparison with LES data shows that modelling of turbulence anisotropy and near-surface turbulence could be improved.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2767/5/052013