Giacomo Valerio Iungo

Assistant Professor

University of Texas at Dallas

United States

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Dr. Giacomo Valerio Iungo is an Assistant Professor at the University of Texas at Dallas, USA. His research areas include wind energy, atmospheric boundary layer dynamics, and advanced measurement techniques using wind LiDAR. He has contributed to various recent publications focusing on enhancing wind turbine performance, noise generation for virtual Lidar scans, and data-driven approaches to characterize wind turbulence.

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2020

Collaborators (10)

Jiarong Hong

University of Minnesota

UNITED STATES

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michele guala

Professor

University of Minnesota

UNITED STATES

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S. Ashwin Renganathan

Assistant Professor

The Pennsylvania State University - University Park Campus

UNITED STATES

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JULIE LUNDQUIST

University of Colorado Boulder

UNITED STATES

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Romit Maulik

Assistant Professr

Pennsylvania State University

UNITED STATES

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S. Letizia

UNITED STATES

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Petra Klein

UNITED STATES

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Michael Heisel

University of Sydney

AUSTRALIA

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Carlo Luigi Bottasso

Professor and Chair of Wind Energy

Technische Universität Kaiserslautern

GERMANY

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Vaughan Voller

Professor

University of Minnesota

UNITED STATES

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Giacomo Valerio Iungo

Research Interests

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About

Articles (21)

Editorial for the collection “Preparatory Work for the American Wake Experiment (AWAKEN)”

Editorial for the collection “Wind tunnel research for renewable energies”

Realistic Noise Generation to Enhance Realism of Virtual Lidar Scans

Augmenting insights from wind turbine data through data-driven approaches

Effects of wind shear and thrust coefficient on the induction zone of a porous disk: A wind tunnel study

Stochastic modal velocity field in rough-wall turbulence

Quantification and assessment of the atmospheric boundary layer height measured during the AWAKEN experiment by a scanning LiDAR

Characterization of wind speed and directional shear at the AWAKEN field campaign site

Quantification of wind turbine energy loss due to leading‐edge erosion through infrared‐camera imaging, numerical simulations, and assessment against SCADA and meteorological data

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

Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines

Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality

Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment

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

Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and yaw actuation

Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework

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 turbine wakes

Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging

Optimal tuning of engineering wake models through lidar measurements

Collaborators (10)

Jiarong Hong

michele guala

S. Ashwin Renganathan

JULIE LUNDQUIST

Romit Maulik

S. Letizia

Petra Klein

Michael Heisel

Carlo Luigi Bottasso

Vaughan Voller

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