Effects of the Subgrid-Scale Modeling in the Large-Eddy Simulations of Wind Turbines

Ciri, Umberto; Salvetti, Maria Vittoria; Carrasquillo, Kenneth; Santoni, Christian; Iungo, Giacomo Valerio; Leonardi, Stefano

doi:10.1007/978-3-319-63212-4_13

Cited by 17 publications

(14 citation statements)

References 8 publications

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“…More details on the numerical discretization can be found in Orlandi. 18 The rotating actuator disk model 19 is used to reproduce the forces generated by the turbine blades. The aerodynamic forces are computed with a blade-element approach using the local relative velocity, angle of attack, and airfoil look-up tables.…”

Section: Methodsmentioning

confidence: 99%

Effect of the turbine scale on yaw control

2018

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Yaw misalignment between the incoming wind and the rotor of a turbine causes a lateral displacement of the wake. This effect can be exploited to avoid or mitigate wake interactions in wind farms, so that power losses are minimized. We performed large‐eddy simulations to evaluate yaw control for a three‐turbine wind farm. We used two different turbine models to assess how the size of the turbine rotor affects the farm efficiency and the effectiveness of the control strategy. A utility‐scale wind turbine with rotor diameter of 126 m is compared with a scaled research wind turbine with rotor diameter of 27 m. In both cases, a model‐free algorithm is used to determine the turbine yaw set point, which maximizes total power production. The algorithm is the nested extremum‐seeking control (NESC), which allows for the coordinated optimization of the wind turbine operating points. The results achieved with NESC are validated by computing a static performance map for different yaw angles. NESC converges to optimal operating conditions, which are in good agreement with the static map benchmark. Numerical results show that a larger rotor diameter induces larger wake deflection, thus achieving higher power improvements. From the analysis of the turbine structural loads, an increase in damage equivalent load is observed for both the yawed turbine and the waked one. Present results suggest that there is a cost‐effective trade‐off between performance and loads for large turbines.

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Section: Methodsmentioning

confidence: 99%

Effect of the turbine scale on yaw control

2018

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“…Full description of the numerical method can be found in Orlandi and Leonardi . The turbines are modeled using the rotating actuator disk model as in Ciri et al The generator torque, blade pitch angle, and yaw orientation are managed by an adapted version of Laks et al baseline controller. The angular velocity of the turbines is determined by the rotor dynamics, balancing the aerodynamic and generator torque.…”

Section: Methodsmentioning

confidence: 99%

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

et al. 2020

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One-way nested mesoscale to microscale simulations of an onshore wind farm have been performed nesting the Weather Research and Forecasting (WRF) model and our in-house high-resolution large-eddy simulation code (UTD-WF). Each simulation contains five nested WRF domains, with the largest domain spanning the North Texas Panhandle region with a 4 km resolution, while the highest resolution (50 m) nest simulates microscale wind fluctuations and turbine wakes within a single wind farm. The finest WRF domain in turn drives the UTD-WF LES higher-resolution domain for a subset of six turbines at a resolution of ∼ 5 m. The wind speed, direction, and boundary layer profiles from WRF are compared against measurements obtained with a met-tower and a scanning Doppler wind LiDAR located within the wind farm. Additionally, power production obtained from WRF and UTD-WF are assessed against supervisory control and data acquisition (SCADA) system data. Numerical results agree well with the experimental measurements of the wind speed, direction, and power production of the turbines. UTD-WF high-resolution domain improves significantly the agreement of the turbulence intensity at the turbines location compared with that of WRF. Velocity spectra have been computed to assess how the nesting allows resolving a wide range of scales at a reasonable computational cost. A domain sensitivity analysis has been performed. Velocity spectra indicate that placing the inlet too close to the first row of turbines results in an unrealistic peak of energy at the rotational frequency of the turbines. Spectra of the power production of a single turbine and of the cumulative power of the array have been compared with analytical models.

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“…The rotating actuator disk model is used to represent the turbine blades. The forces generated by the blades are computed with a blade‐element approach and then distributed as volume forces in the computational domain over a rotating disk, which mimics the rotor motion.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations

2019

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The extremum seeking control (ESC) algorithm has been proposed to determine operating parameters that maximize power production below rated wind speeds (region II). This is usually done by measuring the turbine's power signal to determine optimal values for parameters of the control law or actuator settings. This paper shows that the standard ESC with power feedback is quite sensitive to variations in mean wind speed, with long convergence time at low wind speeds and aggressive transient response, possibly unstable, at high wind speeds. The paper also evaluates the performance, as measured by the dynamic and steady state response, of the ESC with feedback of the logarithm of the power signal (LP-ESC). Large eddy simulations (LES) demonstrate that the LP-ESC, calibrated at a given wind speed, exhibits consistent robust performance at all wind speeds in a typical region II. The LP-ESC is able to achieve the optimal set-point within a prescribed settling time, despite variations in the mean wind speed, turbulence, and shear. The LES have been conducted using realistic wind input profiles with shear and turbulence. The ESC and LP-ESC are implemented in the LES without assuming the availability of analytical gradients. KEYWORDScontrol of wind turbines, extremum-seeking control, large-eddy simulations, logarithmic power feedback INTRODUCTIONWind turbines work over a wide range of conditions and have a long operating life. Thus, the optimal settings for the parameters of the control system may change from initial design specifications. For instance, turbine aging, local topography, and atmospheric conditions affect the turbine performance. In these cases, tuning the control parameters to the site-specific operating conditions can reduce power losses and/or excessive loads at low cost.Extremum seeking control (ESC) has been proposed to tune control system parameters for wind turbines. 1-3 The ESC is well suited for power maximization below rated conditions because it requires feedback of the power signal only, and it is essentially a model-free algorithm that can be commissioned with data from the turbine step response and knowledge of the spectral characteristics of the wind fluctuations. The convergence rate of the basic ESC depends on the magnitude of the gradient of the performance index 3 with respect to the control parameters (eg, generator torque gain, blade pitch angles, yaw angle). For a turbine in below-rated conditions (region II), the power is the performance index. The rotor power is proportional to the power available in the wind, which in turn is proportional to the cube of the effective wind speed. Thus, the gradient of the power signal with respect to the ESC control parameters, is also roughly proportional to the power available in the wind, which can vary from very little power (cut-in wind speed) to almost rated power (close to rated wind speed).* This wide variation in available wind power results in unpredictable performance of the gradient-based ESC algorithm, with slow convergence at low wind spee...

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Effects of the Subgrid-Scale Modeling in the Large-Eddy Simulations of Wind Turbines

Cited by 17 publications

References 8 publications

Effect of the turbine scale on yaw control

Effect of the turbine scale on yaw control

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations

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