Monin-Obukhov similarity theory and its application to wind flow modelling over complex terrain

Abstract

In the wind resource industry Computational Fluid Dynamics (CFD) has gained widespread use to model the Atmospheric Boundary Layer (ABL). These models primarily focus on the neutrally stratified surface layer and ignore physical process such as buoyancy and the Coriolis force. Reductions in uncertainties of turbine suitability and energy production can be achieved if these processes are included. The present work focuses on the development and validation of an ABL CFD model using Monin-Obukhov Similarity Theory (MOST) in which atmospheric stability and the Coriolis force are included. MOST is applied to measured time series data obtained from a commercially proposed wind farm to determine the prevalence and impact of atmospheric stability. The analyses provide the inputs for the CFD model. The CFD model uses the standard k − turbulence model. To account for atmospheric stability modifications based on MOST are introduced to the standard CFD model equations. Two MOST models modifications are investigated. The modifications are successfully validated using the empty domain horizontal homogeneity test of the inlet profiles. The model is thereafter applied to the complex terrain of the proposed wind farm. The models are successfully validated by cross-prediction of the stability-dependent wind velocity profiles between two onsite meteorological masts.

Factors determining nest‐site selection of surface‐nesting seabirds: a case study on the world's largest pelagic bird, the Wandering Albatross (Diomedea exulans)

“…The model included considerations for the atmospheric boundary layer and the effect of the Coriolis force (Breedt et al . 2018, Goddard et al . 2022), and generated estimates of wind velocity and turbulence for 30 × 30‐m cells across Marion Island (with mean errors of 26.9% for velocity and 32.6% for turbulence; Goddard et al .…”

“…The model included considerations for the atmospheric boundary layer and the effect of the Coriolis force (Breedt et al 2018, Goddard et al 2022, and generated estimates of wind velocity and turbulence for 30 9 30-m cells across Marion Island (with mean errors of 26.9% for velocity and 32.6% for turbulence; Goddard et al 2022). From these analyses, we extracted wind characteristics at 1 m above the ground because we considered this relevant to adult albatrosses when on the ground and chicks on nests.…”

Exposing wind stress as a driver of fine‐scale variation in plant communities

“…A westerly wind was assumed as the free‐stream condition with a reference speed of 5 m/s at 1.5 m above‐ground, based on measured data from the wind stations. The model includes considerations for the atmospheric boundary layer and the effect of the Coriolis force (Breedt et al., 2018). This estimate of grid‐level wind speed was multiplied by quadrat‐level wind index indices (for minimum, maximum, mean and range wind stress) to estimate wind stress values that are comparable across and within grids (i.e.…”

Multiple Consequences Related to Atmospheric Turbulence Induced by the Climate Change in the Heatwaves Emergence and in the Cooling Effect of Aerosols

“…The models commonly used, such as those based on Richardson -Kolmogorov energy cascade of eddies theory and the Monin-Obukhov Similarity Theory (MOST), do not offer satisfactory predictions in cases of extremely unstable conditions dominated by high turbulence intensity generated in turbulent mixing zones in which many different eddies interact [19,20]. MOST is applied to measured time series data from meteorological masts at the proposed wind farm location to understand the effects of atmospheric stability and to obtain the characteristic values (Table 1), [20].…”