Browsing by Author "Letizia, Stefano"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item Effects of Upstream Buildings on the Performance of a Synergistic Roof-and-Diffuser Augmentation System for Cross Flow Wind Turbines(Elsevier Science BV, 2018-12-10) Zanforlin, Stefania; Letizia, Stefano; Letizia, StefanoIn a previous work we investigated the effectiveness of combining the concentration effects generated by a dual-pitched roof and a convergent-divergent semi-diffuser placed over a cross flow turbine (CFT), which is rooftop mounted with horizontal shaft running close to the roof ridge. By means of 2D CFD we now assess the effects of the upstream buildings on the performance of this concentration system. Three different urban layouts are simulated via a challenging fully-resolved URANS approach that gives unprecedented insight into the complex interactions of the flow around buildings, diffuser and turbine's blades. A promising power augmentation of 40% with respect to the case without diffuser are obtained and the mechanism behind this performance enhancement are analysed. The beneficial effect of the diffuser on the torque fluctuations damping (similar to 60%) is also discussed. Important remarks on the load control strategy for diffuser-augmented CFT are finally presented.Item Quantification of the Axial Induction Exerted by Utility-Scale Wind Turbines by Coupling LiDAR Measurements and RANS Simulations(Institute of Physics Publishing) Iungo, Giacomo V.; Letizia, Stefano; Zhan, Lu; 0000-0002-0990-8133 (Iungo, GV); Iungo, Giacomo V.; Letizia, Stefano; Zhan, LuThe axial induction exerted by utility-scale wind turbines for different operative and atmospheric conditions is estimated by coupling ground-based LiDAR measurements and RANS simulations. The LiDAR data are thoroughly post-processed in order to average the wake velocity fields by using as common reference frame their respective wake directions and the turbine hub location. The various LiDAR scans are clustered according to their incoming wind speed at hub height and atmospheric stability regime, namely Bulk Richardson number. Time-averaged velocity fields are then calculated as ensemble averages of the scans belonging to the same cluster. The LiDAR measurements are coupled with RANS simulations in order to estimate the rotor axial induction for each cluster of the LiDAR data. First, a control volume analysis of the streamwise momentum is applied to the time-averaged LiDAR velocity fields to obtain an initial estimate of the axial induction over the rotor disk. The calculated thrust force is imposed as forcing of an axisymmetric RANS simulation in order to estimate pressure, radial velocity and momentum fluxes. The latter are combined with the LiDAR streamwise velocity field in order to refine the estimate of the rotor axial induction through the control volume approach. This process is repeated iteratively until achieving convergence on the rotor axial induction while minimizing difference between LiDAR and RANS streamwise velocity fields. This procedure allows to single out the reduction in thrust load while the blade pitch angle is increased transitioning from region 2 to 3 of the power curve. Furthermore, an enhanced thrust force is observed for a fixed incoming wind speed and transitioning from stable to convective stability regimes. The presented technique is proposed as a data-driven alternative to the blade element momentum theory typically used with current actuator disk models in order to quantify rotor aerodynamic thrust for different operative and atmospheric conditions. © Published under licence by IOP Publishing Ltd.Item Wind Farm Flow and Power Capture: Optimal Design of LiDAR Experiments, Flow Physics, and Mid-fidelity Modeling(2021-12-01T06:00:00.000Z) Letizia, Stefano; Iungo, Giacomo Valerio; Hamlen, Kevin; Griffith, Todd; Jin, Yaqing; Leonardi, StefanoNowadays there is an urgent need for wind farm flow models with increased accuracy and low computational costs for the prediction of turbine performances and wakes. An improvement of current standards of wind farm simulations can be achieved only through a better understanding and modeling of the complex physical mechanisms governing the wind farm aerodynamics. Low computational requirements are necessary to enable large amount of simulations needed for the optimal design, real-time monitoring and online control of wind power plants. To this aim, a holistic research project has been conceived and implemented, which is the focus of this Ph.D. thesis. The adopted research strategy includes three main tasks: i) optimal design and execution of field experiments for monitoring wind-farm operations through scanning LiDAR, meteorological and SCADA data; ii) statistical analysis of LiDAR field measurements for probing wake evolution, wake interactions, effects of atmospheric stability, and flow distortions due to topography; iii) development of a data-driven RANS model for accurate and low-computational-cost simulations of wind farm operations. This research project has enabled quantifying and modeling effects on wind farm operations connected with the turbine aerodynamics and the atmospheric stability regime, and detecting the occurrence of topography wakes, which are flow regions with reduced wind speed and enhanced turbulence intensity being detrimental for wind turbines installed on complex terrains. The main deliverables of this project are the LiDAR Statistical Barnes Objective Analysis (LiSBOA), a tool for the optimal collection and statistical characterization of LiDAR measurements, and the Pseudo-2D RANS (P2D-RANS) wind farm model, which has been recently distributed among several industrial partners and with the intended uses of simulating and monitoring the operations of several wind power plants.