Follow
Lu Zhan
Lu Zhan
Argonne National Lab
Verified email at umn.edu
Title
Cited by
Cited by
Year
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
662017
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
492020
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
252018
Optimal tuning of engineering wake models through lidar measurements
L Zhan, S Letizia, GV Iungo
Wind Energy Science 5 (4), 1601-1622, 2020
242020
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
192020
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
102018
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
82021
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
52020
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
52018
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
12019
Can Automated Vehicles
W Northrop, L Zhan, S Haag, D Zarling
Minnesota. Department of Transportation. Office of Research & Innovation, 2022
2022
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
2021
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
The system can't perform the operation now. Try again later.
Articles 1–18