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      High-fidelity CFD simulations for the wake characteristics of the NTNU BT1 wind turbine

      , ,
      Energy
      Elsevier BV

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          Numerical Modeling of Wind Turbine Wakes

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            Review of computational fluid dynamics for wind turbine wake aerodynamics

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              Is Open Access

              Wind-Turbine and Wind-Farm Flows: A Review

              Wind energy, together with other renewable energy sources, are expected to grow substantially in the coming decades and play a key role in mitigating climate change and achieving energy sustainability. One of the main challenges in optimizing the design, operation, control, and grid integration of wind farms is the prediction of their performance, owing to the complex multiscale two-way interactions between wind farms and the turbulent atmospheric boundary layer (ABL). From a fluid mechanical perspective, these interactions are complicated by the high Reynolds number of the ABL flow, its inherent unsteadiness due to the diurnal cycle and synoptic-forcing variability, the ubiquitous nature of thermal effects, and the heterogeneity of the terrain. Particularly important is the effect of ABL turbulence on wind-turbine wake flows and their superposition, as they are responsible for considerable turbine power losses and fatigue loads in wind farms. These flow interactions affect, in turn, the structure of the ABL and the turbulent fluxes of momentum and scalars. This review summarizes recent experimental, computational, and theoretical research efforts that have contributed to improving our understanding and ability to predict the interactions of ABL flow with wind turbines and wind farms.
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                Author and article information

                Contributors
                Journal
                Energy
                Energy
                Elsevier BV
                03605442
                February 2023
                February 2023
                : 265
                : 126285
                Article
                10.1016/j.energy.2022.126285
                c1c6b46f-9d70-4628-ac8a-ed7cf8edc74c
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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