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.
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Browsing Leonardi, Stefano by Author "Santoni-Ortiz, Christian"
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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, StefanoNumerical 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.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, StefanoIn 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.