26 citations · 64 across the 6 of their papers we have counts for
7 papers
Modeling Wind Turbine Performance and Wake Interactions with Machine Learning
C. Moss, R. Maulik, G. V. Iungo
Different machine learning (ML) models are trained on SCADA and meteorological data collected at an onshore wind farm and then assessed in terms of fidelity and accuracy for predic…
Identification of the energy contributions associated with wall-attached eddies and very-large-scale motions in the near-neutral atmospheric surface layer through wind LiDAR measurements
Matteo Puccioni, Marc Calaf, Eric R. Pardyjak +4
Recent works on wall-bounded flows have corroborated the coexistence of wall-attached eddies, whose statistical features are predicted through Townsend's attached eddy hypothesis (…
Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows
Stefano Letizia, Giacomo Valerio Iungo
Next-generation models of wind farm flows are increasingly needed to assist the design, operation, and performance diagnostic of modern wind power plants. Accuracy in the descripti…
Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements
G. Valerio Iungo, Romit Maulik, S. Ashwin Renganathan +1
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and…
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning
S. Ashwin Renganathan, Romit Maulik, Stefano Letizia +1
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models tha…
LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part II: Applications to synthetic and real LiDAR data of wind turbine wakes
Stefano Letizia, Lu Zhan, Giacomo Valerio Iungo
The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in Letizia et al., is a procedure for the optimal design of LiDAR scans and calculation over a Cartesian grid of…
LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part I: Theoretical framework
Stefano Letizia, Lu Zhan, Giacomo Valerio Iungo
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for optimal design of LiDAR scans and retrieval of the velocity statistical moments is proposed. The LiSBOA represents an ada…