Coastal Wind Power in Southern Santa Catarina, Brazil
Authors: César Henrique Mattos Pires, Felipe M. Pimenta, Carla A. D’Aquino, Osvaldo R. Saavedra, Xuerui Mao and Arcilan T. Assireu
Energies (2020) 13(19), 5197; https://www.mdpi.com/1996-1073/13/19/5197; [published]
Abstract: A light detection and ranging (LIDAR) wind profiler was used to estimate the wind speed in the southern coast of Santa Catarina State, Brazil. This profiler was installed on a coastal platform 250 m from the beach, and recorded wind speed and direction from January 2017 to December 2018. The power generation from three wind turbines was simulated, to obtain estimations of the average power, energy generation and capacity factor, as well as to assess the performance of a hypothetical wind farm. The scale and shape parameters of the Weibull distribution were evaluated and compared with those of other localities in the state. The prevailing winds tend to blow predominantly from the northeast and southwest directions. Wind magnitudes are higher for the NE and SW ocean sectors where the average wind power density can reach 610–820 W m−2. The Vestas 3.0 turbine spent the largest percentage of time in operation (>76%). The higher incidence of strong northeasterly winds in 2017 and more frequent passage of cold fronts in 2018 were attributed to the cycle of the South Atlantic subtropical high. The results demonstrate a significant coastal wind power potential, and suggest that there is a significant increase of resources offshore.
Wind farm layout optimization based on CFD simulations
Authors: Luís Eduardo Boni Cruz, Bruno Souza Carmo
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2020) 42:433; https://link.springer.com/epdf/10.1007/s40430-020-02506-z; [published]
Abstract: This work shows the development of a tool for wind farm layout optimization based on computational fluid dynamics (CFD) simulations of the atmospheric wind flow and inter-turbine interference. Due to the high computational power required to simulate a whole wind farm using the complete geometry of the wind turbines, simplified models were developed to repre-sent the turbine behavior, and the most commonly used model is the actuator disk model and its variations. The procedure for wind turbine behavior evaluation using a CFD model was implemented in the OpenFOAM software, and this model was coupled with the Dakota optimization toolkit. A genetic algorithm was selected for the optimization task due to its robust-ness and the characteristics of the problem solved. With this new tool in hand, three different terrain cases, with growing complexity, were tested considering different numbers of turbines on a cylindrical domain in order to achieve the best wind farm layout in terms of annual energy production that respects the imposed physical restrictions. As expected, the layout efficiency decreased as turbines were added to the domain, meaning that wake losses are introduced in this process. For the simpler domains, we observed that this efficiency decrease was approximately linear with respect to the number of turbines. Complex phenomena were captured during the optimization, such as wake deflection, different wake recoveries depending on the wind speed and interaction between the disturbances in the flow field caused by the terrain and by the turbines. These phenomena are not observed when using the tools available in the wind market and are seen as an improvement in the field of wind farm layout optimization, yielding more accurate results, and should decrease the level of uncertainty in the design of a wind farm.
Enhancing wind speed temporal extrapolation using multi fidelity gaussian process regression
Authors: Based Elshafei, Donald Giddings, Xuerui Mao
ICAWESE 2020: international conference on advanced wind energy systems engineering, 10-11, 2020, New York, United States [submitted]
Abstract: In wind resource assessments, the physical measurement is expensive and time consuming while the numerical simulations suffer the well known modelling problem due to the coupling of multi-scales and multi-physics. In this work, the merit of the physical measurements and simulations are combined to produce a hybrid solution. Three methods including a state-of-the-art data fusion algorithm based on the Gaussian process are applied. Using the west coastal region of Denmark as the test site, it is found that the root mean square of the predicted wind is within a 10% deviation from the physical measurements.
Thick Strip Method for Efficient Large-Eddy Simulations of Flexible Wings in Stall
Authors: Mohsen Lahooti, Rafael Palacios, Spencer J. Sherwin, Imperial College London
AIAA SciTech conference [submitted]
Brazil Offshore Wind Resources and Atmospheric Surface Layer Stability
Authors: Felipe M. Pimenta, Allan R. Silva, Arcilan T. Assireu,Vinicio de S. e Almeida, Osvaldo R. Saavedra
Energies 2019, 12(21), 4195; https://doi.org/10.3390/en12214195 [published]
Brazil’s offshore wind resources are evaluated from satellite winds and ocean heat flux datasets. Winds are extrapolated to the height of modern turbines accounting for atmospheric stability. Turbine technical data are combined with wind and bathymetric information for description of the seasonal and latitudinal variability of wind power. Atmospheric conditions vary from unstable situations in the tropics, to neutral and slightly stable conditions in the subtropics. Cabo Frio upwelling in the southeast tends to promote slightly stable conditions during the spring and summer. Likewise, Plata plume cold-water intrusions in southern shelf tends to create neutral to slightly stable situations during the fall and winter. Unstable (stable) conditions are associated with weaker (stronger) vertical wind shear. Wind technical resource, accounting for atmospheric stability and air density distribution, is 725 GW between 0–35 m, 980 GW for 0–50 m, 1.3 TW for 0–100 m and 7.2 TW for the Brazilian Exclusive Economic Zone (EEZ). Resources might vary from 2 to 23% according to the chosen turbine. Magnitudes are 20% lower than previous estimates that considered neutral atmosphere conditions. Strong winds are observed on the north (AP, PA), northeast (MA, PI, CE, RN), southeast (ES, RJ) and southern states (SC, RS). There is significant seasonal complementarity between the north and northeast shelves. When accounting for shelf area, the largest integrated resource is located on the north shelf between 0–20 m. Significant resources are also found in the south for deeper waters.
SHARPy: A dynamic aeroelastic simulation toolbox for very flexible aircraft and wind turbines
Authors: Alfonso del Carre, Arturo Muñoz-Simón, Norberto Goizueta, Rafael Palacios [all Imperial College London]
Journal of Open Source Software, 4(44), 1885, https://doi.org/10.21105/joss.01885 [published]
Aeroelasticity is the study of the dynamic interaction between unsteady aerodynamics and structural dynamics on flexible streamlined bodies, which may include rigid-body dynamics. Industry standard solutions in aeronautics and wind energy are built on the assumption of small structural displacements, which lead to linear or quasi-linear theories. However, advances in areas such as energy storage and generation, and composite material manufacturing have fostered a new kind of aeroelastic structures that may undergo large displacements under aerodynamic forces.
SHARPy (Simulation of High-Aspect Ratio aeroplanes in Python) is a dynamic aeroelasticity
simulation toolbox for aircraft and wind turbines.