Leonardi, Stefano

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/4788

Stefano Leonardi is currently an Associate Professor of Mechanical Engineering. His research interests include turbulence, computational fluid mechanics, wind energy, drag reduction, super hydrophobic surfaces, and heat transfer.

ORCID Page

Browse

Recent Submissions

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Evaluation of Log-Of-Power Extremum Seeking Control for Wind Turbines Using Large Eddy Simulations
    (John Wiley & Sons Ltd) Ciri, Umberto; Leonardi, Stefano; Rotea, Mario A.; 0000-0002-9809-7191 (Leonardi, S); 0000-0002-4239-0591 (Rotea, MA); Ciri, Umberto; Leonardi, Stefano; Rotea, Mario A.
    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. ©2019 John Wiley & Sons, Ltd.
  • Thumbnail Image
    Item
    Coupling of Mesoscale Weather Research and Forecasting Model to a High Fidelity Large Eddy Simulation
    (Institute of Physics Publishing) Santoni-Ortiz, Christian; Garcia-Cartagena, Edgardo Javier; Ciri, Umberto; Iungo, Giacomo V.; Leonardi, Stefano; 0000-0002-0990-8133 (Iungo, GV); 0000-0002-9809-7191 (Leonardi, S); Santoni-Ortiz, Christian; Garcia-Cartagena, Edgardo Javier; Ciri, Umberto; Iungo, Giacomo V.; Leonardi, Stefano
    Numerical simulations of the flow in a wind farm in north Texas have been performed with WRF (Weather Research and Forecasting model) and our in-house LES code. Five nested domains are solved with WRF to model the meso-scale variability while retaining a resolution of 50 meters in the wind farm region. The computational domain of our in-house LES code is nested into the inner most domain of the WRF simulation from where we get the inlet boundary conditions. The outlet boundary conditions are radiative and at this stage the coupling between the two codes is one-way. The turbines in WRF are mimicked using a modified Fitch approach, while in our in-house LES we have used a rotating actuator disk combined with immersed boundaries for tower and nacelle. Numerical results agree well with meteorological data from the met tower. The power production obtained numerically on each turbine compares well with SCADA data with an index of agreement ranging between 80% to 90%. The power production from the numerical results of our in-house LES code is slightly closer to SCADA data than that of WRF.
  • Thumbnail Image
    Item
    Effect of the Turbine Scale on Yaw Control
    (John Wiley & Sons Ltd) Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano; 0000-0002-9809-7191 (Leonardi, S); Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano
    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. © 2018 John Wiley & Sons, Ltd.
  • Thumbnail Image
    Item
    Large-Eddy Simulations of Two In-Line Turbines in a Wind Tunnel with Different Inflow Conditions
    (MDPI AG) Ciri, Umberto; Petrolo, Giovandomenico; Salvetti, Maria Vittoria; Leonardi, Stefano; Ciri, Umberto; Leonardi, Stefano
    Numerical simulations reproducing a wind tunnel experiment on two in-line wind turbines have been performed. The flow features and the array performances have been evaluated in different inflow conditions. Following the experimental setup, different inlet conditions are obtained by simulating two grids upstream of the array. The increased turbulence intensity due to the grids improves the wake recovery and the efficiency of the second turbine. However, the inlet grid induces off-design operation on the first turbine, decreasing the efficiency and increasing fatigue loads. Typical grid flow patterns are observed past the rotor of the first turbine, up to the near wake. Further downstream, the signature of the grid on the flow is quite limited. An assessment of numerical modeling aspects (subgrid scale tensor and rotor parameterization) has been performed by comparison with the experimental measurements.
  • Thumbnail Image
    Item
    Data-Driven RANS for Simulations of Large Wind Farms
    (Institute of Physics Publishing) Iungo, Giacomo V.; Viola, F.; Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano; 0000-0002-0990-8133 (Iungo, GV); 0000-0002-9809-7191 (Leonardi, S); Sorensen J.N.; Ivanell S.; Barney A.; Iungo, Giacomo V.; Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano
    In the wind energy industry there is a growing need for real-time predictions of wind turbine wake flows in order to optimize power plant control and inhibit detrimental wake interactions. To this aim, a data-driven RANS approach is proposed in order to achieve very low computational costs and adequate accuracy through the data assimilation procedure. The RANS simulations are implemented with a classical Boussinesq hypothesis and a mixing length turbulence closure model, which is calibrated through the available data. High-fidelity LES simulations of a utility-scale wind turbine operating with different tip speed ratios are used as database. It is shown that the mixing length model for the RANS simulations can be calibrated accurately through the Reynolds stress of the axial and radial velocity components, and the gradient of the axial velocity in the radial direction. It is found that the mixing length is roughly invariant in the very near wake, then it increases linearly with the downstream distance in the diffusive region. The variation rate of the mixing length in the downstream direction is proposed as a criterion to detect the transition between near wake and transition region of a wind turbine wake. Finally, RANS simulations were performed with the calibrated mixing length model, and a good agreement with the LES simulations is observed.
  • Thumbnail Image
    Item
    Data-Driven Reduced Order Model for Prediction of Wind Turbine Wakes
    (Institute of Physics Publishing) Iungo, Giacomo V.; Santoni-Ortiz, Christian; Abkar, M.; Porté-Agel, F.; Rotea, Mario A.; Leonardi, Stefano; Iungo, Giacomo V.; Santoni-Ortiz, Christian.; Rotea, Mario A.; Leonardi, Stefano
    In this paper a new paradigm for prediction of wind turbine wakes is proposed, which is based on a reduced order model (ROM) embedded in a Kalman filter. The ROM is evaluated by means of dynamic mode decomposition performed on high fidelity LES numerical simulations of wind turbines operating under different operational regimes. The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within a Kalman filter in order to produce a time-marching algorithm for prediction of wind turbine wake flows. This data-driven algorithm enables data assimilation of new measurements simultaneously to the wake prediction, which leads to an improved accuracy and a dynamic update of the ROM in presence of emerging coherent wake dynamics observed from new available data. Thanks to its low computational cost, this numerical tool is particularly suitable for real-time applications, control and optimization of large wind farms.

Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).