| Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data S El‐Asha, L Zhan, GV Iungo Wind Energy 20 (11), 1823-1839, 2017 | 91 | 2017 |
| LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes L Zhan, S Letizia, G Valerio Iungo Wind Energy 23 (3), 501-527, 2020 | 69 | 2020 |
| Optimal tuning of engineering wake models through lidar measurements L Zhan, S Letizia, GV Iungo Wind Energy Science 5 (4), 1601-1622, 2020 | 34 | 2020 |
| One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data C Santoni, EJ García‐Cartagena, U Ciri, L Zhan, G Valerio Iungo, ... Wind Energy 23 (3), 691-710, 2020 | 32 | 2020 |
| Parabolic RANS solver for low‐computational‐cost simulations of wind turbine wakes GV Iungo, V Santhanagopalan, U Ciri, F Viola, L Zhan, MA Rotea, ... Wind Energy 21 (3), 184-197, 2018 | 28 | 2018 |
| LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics–Part 2: Applications to lidar measurements of wind … S Letizia, L Zhan, GV Iungo Atmospheric Measurement Techniques 14 (3), 2095-2113, 2021 | 15 | 2021 |
| Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations G Valerio Iungo, S Letizia, L Zhan Journal of Physics: Conference Series 1037, 2018 | 9 | 2018 |
| Profitability optimization of a wind power plant performed through different optimization algorithms and a data-driven RANS solver V Santhanagopalan, S Letizia, L Zhan, LY Al-Hamidi, GV Iungo 2018 Wind Energy Symposium, 2018, 2018 | 7 | 2018 |
| Wind lidar measurements of wind turbine wakes evolving over flat and complex terrains: ensemble statistics of the velocity field L Zhan, S Letizia, GV Iungo Journal of Physics: Conference Series 1452 (1), 012077, 2020 | 6 | 2020 |
| Impact of Fog Particles on 1.55 μm Automotive LiDAR Sensor Performance: An Experimental Study in an Enclosed Chamber L Zhan, WF Northrop SAE Technical Paper, 2021 | 2 | 2021 |
| Can Automated Vehicles" See" in Minnesota? Ambient Particle Effects on LiDAR W Northrop, L Zhan, S Haag, D Zarling Minnesota Department of Transportation, 2022 | 1 | 2022 |
| Assessment of wake superposition models through wind tunnel tests and LiDAR measurements S Letizia, L Zhan, E Nanos, C Bottasso, MA Rotea, GV Iungo, TUM Team, ... APS Division of Fluid Dynamics Meeting Abstracts, G42. 006, 2019 | 1 | 2019 |
| An Experimental Study of Lateral Wake Interactions within a Wind Farm EM Nanos, S Letizia, L Zhan, MA Rotea, CL Bottasso, GV Iungo Wind Energy Science Conference 2019, 2019 | | 2019 |
| LiDAR measurements and modeling of onshore wind farms on flat and complex terrains S Letizia, L Zhan, GV Iungo Bulletin of the American Physical Society 63, 2018 | | 2018 |
| RANS simulations of wind turbine wakes: optimal tuning of turbulence closure and aerodynamic loads from LiDAR and SCADA data S Letizia, M Puccioni, L Zhan, F Viola, S Camarri, GV Iungo APS Division of Fluid Dynamics Meeting Abstracts, D17. 008, 2017 | | 2017 |
| Weather Research and Forecasting model simulation of an onshore wind farm: assessment against LiDAR and SCADA data C Santoni, EJ Garcia-Cartagena, L Zhan, GV Iungo, S Leonardi APS Division of Fluid Dynamics Meeting Abstracts, D17. 001, 2017 | | 2017 |
| Proactive monitoring of a wind turbine array with lidar measurements, SCADA data and a data-driven RANS solver G Iungo, EA Said, V Santhanagopalan, L Zhan AGU Fall Meeting Abstracts 2016, GC54B-08, 2016 | | 2016 |
| Proactive monitoring of an onshore wind farm through lidar measurements, SCADA data and a data-driven RANS solver GV Iungo, S Camarri, U Ciri, S El-Asha, S Leonardi, MA Rotea, ... APS Division of Fluid Dynamics Meeting Abstracts, E2. 005, 2016 | | 2016 |
Giacomo Valerio IungoAssociate Professor, WindFluX Lab, University of Texas at DallasE-mail megerősítve itt: utdallas.edu
