Large-eddy simulations (LES) are performed on the flow over a wind farm sited behind an abrupt rough-to-smooth surface roughness jump. The change in surface roughness affects both the first-order and second-order turbulent statistics. The usual deficit, i.e., the difference between the velocities upstream of the entire wind farm and downstream of a turbine, attains negative values close to the ground, which makes it difficult for modeling within the usual Gaussian radial-shape framework. A different definition, i.e., the difference in velocity at the same location with and without a turbine on a heterogeneous surface, is always positive and is amenable to Gaussian shape-based modeling. For the setup considered here, wind farms sited downstream of a surface roughness jump produce more power than a wind farm sited on a homogeneously rough surface. This increase is primarily because of the larger power generated by the downstream turbines and only slightly due to the increased power of the first-row turbine. The farm performance is affected by the distance between the abrupt change in surface roughness and the position of the first row of turbines. The wind farm performance is also dependent on the aerodynamic roughness upstream of the surface roughness jump. Two single-turbine analytical models and three wake-merging strategies are evaluated for their ability to predict the velocity deficits. A corrected form of the standard Gaussian model with a recently proposed wake-merging methodology, applicable for a varying background field, is found to be insensitive to the tunable model parameter and is consistently in line with the LES results.
Skip Nav Destination
Article navigation
Research Article|
May 14 2024
Effect of an abrupt rough-to-smooth surface roughness transition on wind farm wakes: An LES and analytical modeling study
Naveen N. Kethavath
;
Naveen N. Kethavath
a)
(Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing)
1
Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad
, Kandi, Sangareddy, Telangana 502285, India
a)Authors to whom correspondence should be addressed: knaveen1694@gmail.com and nghaisas@mae.iith.ac.in
Search for other works by this author on:
Niranjan S. Ghaisas
Niranjan S. Ghaisas
a)
(Conceptualization, Formal analysis, Funding acquisition, Writing – review & editing)
2
Department of Mechanical and Aerospace Engineering, and Department of Climate Change, Indian Institute of Technology Hyderabad
, Kandi, Sangareddy, Telangana 502285, India
a)Authors to whom correspondence should be addressed: knaveen1694@gmail.com and nghaisas@mae.iith.ac.in
Search for other works by this author on:
a)Authors to whom correspondence should be addressed: knaveen1694@gmail.com and nghaisas@mae.iith.ac.in
J. Renewable Sustainable Energy 16, 033302 (2024)
Article history
Received:
February 06 2024
Accepted:
April 26 2024
Citation
Naveen N. Kethavath, Niranjan S. Ghaisas; Effect of an abrupt rough-to-smooth surface roughness transition on wind farm wakes: An LES and analytical modeling study. J. Renewable Sustainable Energy 1 May 2024; 16 (3): 033302. https://doi.org/10.1063/5.0202733
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
435
Views
Citing articles via
A review of tidal energy—Resource, feedbacks, and environmental interactions
Simon P. Neill, Kevin A. Haas, et al.
Wind tunnel testing of wind turbine and wind farm control strategies for active power regulation
J. Gonzalez Silva, D. van der Hoek, et al.
Machine learning for modern power distribution systems: Progress and perspectives
Marija Marković, Matthew Bossart, et al.
Related Content
Large-eddy simulation and analytical modeling study of the wake of a wind turbine behind an abrupt rough-to-smooth surface roughness transition
Physics of Fluids (December 2022)
Wind farm yaw control set-point optimization under model parameter uncertainty
J. Renewable Sustainable Energy (July 2021)
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
Physics of Fluids (October 2024)
Data-driven wake model parameter estimation to analyze effects of wake superposition
J. Renewable Sustainable Energy (November 2023)
A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines
Physics of Fluids (March 2024)