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.


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Recent Submissions

  • Evaluation of Log-Of-Power Extremum Seeking Control for Wind Turbines Using Large Eddy Simulations 

    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 ...
  • Coupling of Mesoscale Weather Research and Forecasting Model to a High Fidelity Large Eddy Simulation 

    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 ...
  • Effect of the Turbine Scale on Yaw Control 

    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 ...
  • Large-Eddy Simulations of Two In-Line Turbines in a Wind Tunnel with Different Inflow Conditions 

    Ciri, Umberto; Petrolo, Giovandomenico; Salvetti, Maria Vittoria; 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 ...
  • Data-Driven RANS for Simulations of Large Wind Farms 

    Iungo, Giacomo V.; Viola, F.; 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 ...
  • Data-Driven Reduced Order Model for Prediction of Wind Turbine Wakes 

    Iungo, Giacomo V.; Santoni-Ortiz, Christian; Abkar, M.; Porté-Agel, F.; 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 ...