Theory Reference

The wake velocity deficit, deflection, and turbulence models implemented in the FLORIS framework are listed below along with accompanying publications.

Jensen

The Jensen wake model is defined in [1].

Multi Zone Wake Model

The multi-zone wake model is defined in [2], [3].

Gaussian Wake Models

Several Gaussian wake models are now implemented within FLORIS. Literature on the Gaussian model can be found in [4], [5], [6], [7], [8], [9] and [10].

Gauss Curl Hybrid Model

The Gauss-Curl-Hybrid model combines Gaussian wake models to capture second-order effects of wake steering using curl-based methods, as described in [11].

Jimenez Wake Model

The Jimenez model of wake deflection is defined in [12].

Curled Wake Model

The curled wake model solves a linearized version of the Reynolds-averaged Navier-Stokes equations to obtain the wake velocity deficit. This is the computationally most expensive option (1-10 seconds) in the FLORIS framework because it solves a parabolic system of equations. This model can be used when accurate modeling of yawed wakes is of interest and comptutational expense is not a priority. See [13] for a full description.

References:

1

Niels Otto Jensen. A note on wind generator interaction. Risø National Laboratory, 1983.

2

Pieter MO Gebraad, FW Teeuwisse, Jan-Willem van Wingerden, Paul A Fleming, Shalom D Ruben, Jason R Marden, and Lucy Y Pao. A data-driven model for wind plant power optimization by yaw control. In 2014 American Control Conference, 3128–3134. IEEE, 2014.

3

Pieter MO Gebraad, FW Teeuwisse, JW Van Wingerden, Paul A Fleming, SD Ruben, JR Marden, and LY Pao. Wind plant power optimization through yaw control using a parametric model for wake effects—a cfd simulation study. Wind Energy, 19(1):95–114, 2016.

4

Majid Bastankhah and Fernando Porté-Agel. A new analytical model for wind-turbine wakes. Renewable Energy, 70:116–123, 2014.

5

Mahdi Abkar and Fernando Porté-Agel. Influence of atmospheric stability on wind-turbine wakes: a large-eddy simulation study. Physics of fluids, 27(3):035104, 2015.

6

Amin Niayifar and Fernando Porté-Agel. Analytical modeling of wind farms: a new approach for power prediction. Energies, 9(9):741, 2016.

7

Majid Bastankhah and Fernando Porté-Agel. Experimental and theoretical study of wind turbine wakes in yawed conditions. Journal of Fluid Mechanics, 806:506–541, 2016.

8

Deepu Dilip and Fernando Porté-Agel. Wind turbine wake mitigation through blade pitch offset. Energies, 10(6):757, 2017.

9

F. Blondel and M. Cathelain. An alternative form of the super-gaussian wind turbine wake model. Wind Energy Science Discussions, 2020:1–16, 2020. URL: https://www.wind-energ-sci-discuss.net/wes-2019-99/, doi:10.5194/wes-2019-99.

10

Guo-Wei Qian and Takeshi Ishihara. A new analytical wake model for yawed wind turbines. Energies, 11(3):665, 2018.

11

Jennifer King, Paul Fleming, Ryan King, and Luis A. Martinez-Tossas. Controls-oriented model to capture secondary effects of wake steering. Submitted to Wind Energy Science, 2019.

12

Ángel Jiménez, Antonio Crespo, and Emilio Migoya. Application of a les technique to characterize the wake deflection of a wind turbine in yaw. Wind energy, 13(6):559–572, 2010.

13

Luis A Martínez-Tossas, Jennifer Annoni, Paul A Fleming, and Matthew J Churchfield. The aerodynamics of the curled wake: a simplified model in view of flow control. Wind Energy Science (Online), 2019.