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.