Toward flow control: An assessment of the curled wake model in the FLORIS framework

Bay, Christopher J.; King, Jennifer; Martínez‐Tossas, Luis A.; Mudafort, Rafael; Hulsman, Paul; Kühn, Martin; Fleming, Paul

doi:10.1088/1742-6596/1618/2/022033

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…The constant C is used to take into account all of the extra terms in Equation 11. Testing from Bay et al (2020) has shown that for all of the cases tried in the manuscript, a value of C = 8 has provided good agreement between the model and experiments/simulations. This value is consistent with what is suggested by Bay et al (2019).…”

Section: Turbulence Modelmentioning

confidence: 84%

“…The curled wake is known to affect not only a turbine immediately downstream, but also subsequent turbines within a wind plant. This effect is known as secondary steering and it is important to capture it when using wake models to unravel the full potential of wake steering (Fleming et al, 2018a;Bay et al, 2020;King et al, 2020). The curled wake model uses a simplified version of the RANS equations to predict the wake of a wind turbine in yaw (Martínez-Tossas et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The curled wake model uses a simplified version of the RANS equations to predict the wake of a wind turbine in yaw (Martínez-Tossas et al, 2019). Several improvements have been proposed to the original formulation of the model, including: 1) a decay model for the vortices, 2) tuning of the viscous term based on turbulence intensity, and 3) adding a pressure gradient term to account for wake expansion (Bay et al, 2019;Bay et al, 2020;Hulsman et al, 2020). Also, the new Gauss-Curl Hybrid model has shown to provide a good compromise between an analytical model (Bastankhah and Porté-Agel, 2016) and some of the physics from the curled wake model (King et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The original formulation of the curled wake model was for a single wind turbine wake. In the case of a wind plant, the wakes are first computed individually, then superposed to obtain the flow field of the entire wind plant (Bay et al, 2020). Most wake models are used in the same manner by first computing the wake of the individual turbines and using a superposition method afterward to obtain the flow over the entire domain (Annoni et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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The curled wake model: A three-dimensional and extremely fast steady-state wake solver for wind plant flows

Martínez‐Tossas¹,

King²,

Quon³

et al. 2020

Preprint

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Abstract. This work focuses on minimizing the computational cost of steady-state wind power plant flow simulations that take into account wake steering physics. We present a simple wake solver with a computational cost on the order of seconds for large wind plants. The solver uses a simplified form of the Reynolds-averaged Navier-Stokes equations to obtain a parabolic equation for the wake deficit of a wind plant. We compare results from the model to supervisory control and data acquisition (SCADA) from the Lillgrund wind plant; good agreement is obtained. Results for the solver in complex terrain are also shown. Finally, the solver is demonstrated for a case with wake steering showing good agreement with power reported by large-eddy simulations. This new solver minimizes the time – and therefore the related cost – it takes to conduct a steady-state wind plant flow simulation to about a second on a personal laptop. This solver can be used for different applications including wake steering for wind power plants and layout optimization, and it will soon be available within the FLOw Redirection and Induction in Steady State (FLORIS) framework.

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Section: Turbulence Modelmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The curled wake model: A three-dimensional and extremely fast steady-state wake solver for wind plant flows

Martínez‐Tossas¹,

King²,

Quon³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed model has shown an acceptable agreement with the results obtained by large eddy simulations (LES) [28], LiDAR measurements [24,29], and wind tunnel [28,30]. It presents a significant improvement with respect to the models commonly used in commercial packages such as WAsP or OpenWind.…”

Section: Wake Effect Modelmentioning

confidence: 51%

Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model

2021

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This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.

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Wake steering of multirotor wind turbines

et al. 2021

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In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multirotor turbines, a new degree of freedom is introduced to wind farm control such that wakes can be expanded, channelled or redirected to improve inflow conditions for downstream turbines. Five different yaw configurations are investigated (including a baseline case) by employing large‐eddy simulations (LES) to generate a detailed representation of the velocity field downwind of a multirotor wind turbine. Two lower‐fidelity models from single‐rotor yaw studies (curled‐wake model and analytical Gaussian wake model) are extended to the multirotor case, and their results are compared with the LES data. For each model, the wake is analysed primarily by examining wake cross‐sections at different downwind distances. Further quantitative analysis is carried out through characterisations of wake centroids and widths over a range of streamwise locations and through a brief analysis of power production. Most significantly, it is shown that rotor yaw can have a considerable impact on both the distribution and magnitude of the wake velocity deficit, leading to power gains for downstream turbines. The lower‐fidelity models show small deviation from the LES results for specific configurations; however, both are able to reasonably capture the wake trends over a large streamwise range.

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Toward flow control: An assessment of the curled wake model in the FLORIS framework

Cited by 5 publications

References 26 publications

The curled wake model: A three-dimensional and extremely fast steady-state wake solver for wind plant flows

The curled wake model: A three-dimensional and extremely fast steady-state wake solver for wind plant flows

Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model

Wake steering of multirotor wind turbines

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