Iungo, Giacomo V.
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/4790
Giacomo Iungo is currently an Associate Professor of Mechanical Engineering and head of the WindFluX (Wind, Fluids, and eXperiments) Lab. His research interests include wind energy, flow instability, bluff body aerodynamics; atmospheric boundary layer, reduced order models; signal processing; wind tunnel design; experimental fluid mechanics; and wind LiDAR technology.
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Browsing Iungo, Giacomo V. by Author "0000-0002-9809-7191 (Leonardi, S)"
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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 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, StefanoIn 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.