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

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Recent Submissions

  • 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 ...