Modelling of atmospheric boundary-layer flow in complex terrain with different forest parameterizations

The Science of Making Torque from Wind 2014

Arroyo, R Chávez^1 ; Rodrigo, J Sanz^1 ; Gankarski, P.^1

CENER (National Renewable Energy Centre), Avenida de la Innovacion 7, Sarriguren Navarra, Spain^1

关键词: Complex terrains;    Neutral stability;    Novel methodology;    Turbulence closures;    Turbulence intensity;    Velocity profiles;    Vertical profile;    Zero-plane displacement;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/524/1/012119/pdf
DOI  :  10.1088/1742-6596/524/1/012119

来源: IOP

【 摘 要 】

This work explores the accuracy of two approaches that account for the effects of the forest canopy on the wind flow by using a RANS-based model. The first approach implements additional terms in the RANS equations (canopy model), whilst the second one uses large values of roughness length and a zero-plane displacement height. The model uses a limited-length-scale k-Ε turbulence closure that considers processes occurring in the Atmospheric Boundary-Layer (ABL) such as the Coriolis effects. Both the forest and the ABL implementations are compared with experimental data obtained from 118 m high met masts installed in a large mountain- range site with mixed forest characteristics for neutral stability cases. In order to perform a meaningful comparison at multiple mast locations, a novel methodology is presented which allows the selection of a velocity bin for a given wind direction and a stability class that minimizes the error of using short-term measurement periods at some masts compared to long-term wind statistics from a reference mast. Based on the outcome of the model validation it is possible to conclude that more consistent results are obtained by the canopy model since it reduces the uncertainty in the selection of correct input parameters in the large-roughness approach. The errors in the vertical profiles of velocity and turbulence intensity are reduced by the forest model by almost 63% and 11%, respectively, compared to the standard configuration (no forest). The large-roughness method reduces the error in the velocity profiles by 54% while the predictions of turbulence intensity are barely improved.