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sandia.gov
official
https://www.sandia.gov/app/uploads/sites/273/2025/02/AIAA2012-1288-SAND2011-3…
Decades of Wind Turbine Load Simulation Matthew Barone∗ , Joshua Paquette† , Brian Resor‡ Sandia National Laboratories§ , Albuquerque, NM 87185 Lance Manuel¶ University of Texas, Austin, TX 78712 A high-performance computer was used to simulate ninety-six years of operation of a five megawatt wind turbine. For example, IEC Design Load Case (DLC) 1.11 requires extrapolation of simulation results for 10-minute extreme blade loads and tip deflections to 50-year return values. Maximum tip deflection versus mean wind speed, derived from 96 years of simulation. Maximum blade bending moments versus mean wind speed, derived from 96 years of simulation. 6 of 11 American Institute of Aeronautics and Astronautics Downloaded by Sandia National Laboratories - Albany, NM on January 24, 2013 | http://arc.aiaa.org | DOI: 10.2514/6.2012-1288 The tower fore-aft and yaw moment extremes are plotted versus mean wind speed in Figure 7. Extreme Load Cases The random seed and mean wind speed used to generate the turbulent wind field were saved for each simulation, allowing any particularly interesting simulations to be reproduced later and studied in detail, if desired.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0167610599000999
The paper describes a horizontal axis wind turbine time domain simulation and fatigue estimation program written using the DelphiTM language.
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ema3d.com
article
https://www.ema3d.com/blog/using-simulation-to-reduce-damage-and-maximize-ava…
Abstract: Lightning effects on wind turbine blades present significant challenges to manufacturers and operators. The industry is skewing
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pmc.ncbi.nlm.nih.gov
official
https://pmc.ncbi.nlm.nih.gov/articles/PMC12155847
# Computational Analysis of a Wind Turbine Blade for Different Advanced Materials. As wind turbine rotors grow in size and Greece advances its offshore wind energy initiatives, this study analyzes the structural behavior of offshore wind turbine blades using fluid–structure interaction (FSI) methods. The blade skin and shear webs of the International Energy Agency (IEA) 15 MW wind turbine, assumed to operate in the Aegean Sea, are examined. Computational fluid dynamics (CFD) simulations are conducted for two steady-state wind speeds based on local weather data, followed by finite element analysis (FEA) to assess advanced materials in terms of strength, cost, and carbon footprint. This is the first study to evaluate bamboo- and basalt-based composite materials under Greek offshore wind conditions using FSI methods. The simulation results indicate that using bamboo composites as blade skin may lead to damage due to the excessive loads on offshore wind turbine blades. **Keywords:**computational analysis, advanced materials, fiber-reinforced composites, IEA 15 MW wind turbine, wind turbine blades, fluid–structure interaction, offshore structures.
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ijournalse.org
research
https://ijournalse.org/index.php/ESJ/article/view/1443
Renewable Energy Structural Dynamics Turbulence Wind Turbines Wind Energy. Wind energy is one of the fastest growing sources of renewable energy because of its cleanliness and sustainability. Due to the turbulent nature of wind, a wind turbine experiences severe dynamic loading and faces the danger of fatigue failure. For this reason, the study of blade deflections under different turbulence conditions is of high importance. In this work, a wind turbine's blade is simulated under different turbulent conditions. Four different wind fields are generated with a mean wind velocity of 12 m/s and turbulence intensities of 1, 10, 25, and 50%. For the 50% turbulence intensity, the standard deviation of the out-of-plane deflection is 600% larger than that of the 1% turbulence intensity case. A maximum of 3.78 m of out-of-plane tip deflection leads to the danger of a tower strike. Continuous monitoring of wind conditions is a must, to put the turbine on brake in cases of gusts and severe turbulence.
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wes.copernicus.org
article
https://wes.copernicus.org/articles/5/503/2020/wes-5-503-2020.pdf
The loads and total number of iterations are normalized with respect to the highest-fidelity results, while the computation time is normalized with re-spect to the lowest-fidelity model (one sub-body, linear case) in combination with the dense matrix solver. R. Verelst: The effects of blade structural model fidelity 509 3.1 Steady-wind-case results Turbine power, blade pitch, blade effective radius change and blade tip torsion results are given for steady, determin-istic wind speed conditions. Figure 3 shows the power, pitch and blade effective radius change results of linear (one body) and nonlinear (30 bodies) blade models for steady-wind-load cases. Figure 7 shows flapwise, edgewise and torsion moment DEL ratio variations by model fidelity (number of sub-bodies in blade model) at blade stations where the maximum devia-tions between linear and nonlinear cases occur for each load component.
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eprints.whiterose.ac.uk
article
https://eprints.whiterose.ac.uk/id/eprint/183343/1/Wind%20Energy%20-%202022%2…
The eventual deviations are expected to be covered by the partial safety factors defined by the IEC standard.3 To address the challenge of providing a reality-bounded uncertainty characterisation and to evaluate the effect of combined structural and aerofoil roughness uncertainties, this study aims to (a) develop a framework to propagate and validate uncertainties in laminate properties with blade full-scale testing; (b) utilise wind-tunnel test data to define rough behaviour for different aerofoils throughout the blade; (c) compare uncer-tainty levels to IEC61400-54 recommended tolerances for blade design; and (d) predict the effect of these uncertainties in wind turbine loads and energy production using aeroelastic simulation. TABLE 2 Uncertainty levels for blade mean structural parameters Parameter Distribution CoV (%) Mass moment Normal 1.2 Mean flapwise stiffness Normal 2.8 Mean edgewise stiffness Normal 3.5 Mean torsional stiffness Normal 2.7 FIGURE 4 Wind turbine blade static test: finite element model GONZAGA ET AL.
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m-selig.ae.illinois.edu
research
https://m-selig.ae.illinois.edu/pubs/FioreSelig-2014-AIAA-2014-2848-SandBugsE…
Nomenclature A = particle reference area AK = particle nondimensional mass c = airfoil chord length COE = Cost of Energy Cd = airfoil drag coefficient CD = particle drag coefficient Cl = airfoil lift coefficient d = particle diameter D = particle drag force E = erosion rate EI = particle impact efficiency ER = insect rupture efficiency f = sand grain shape factor GAEP = Gross Annual Energy Production h = airfoil projected height perpendicular to freestream K = erosion rate constant l = particle length m = particle mass n = erosion rate velocity exponent Q = quantity of insect debris r/R = airfoil radial location on blade RI = impact surface ratio ∗Graduate Student (Ph.D.), Department of Aerospace Engineering, 104 S.