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1. A Decommissioned Wind Blade as a Second-Life Construction Material for a Transmission Pole

   https://doi.org/10.3390/constrmater1020007  

   Alshannaq, Ammar A.; Bank, Lawrence C.; Scott, David W.; Gentry, Russell (September 2021, Construction Materials)

   null (Ed.)

   This paper demonstrates the concept of adaptive repurposing of a portion of a decommissioned Clipper C96 wind turbine blade as a pole in a power transmission line application. The current research program is aimed at creating a path towards sustainable repurposing of wind turbine blades after they are removed from service. The present work includes modelling and analysis of expected load cases as prescribed in ASCE 74 and NESC using simplified boundary conditions for tangent pole applications. Load cases involving extreme wind, concurrent ice and wind, extreme ice, differential ice, broken conductor, and broken shield have been analyzed and governing load cases for bending, shear, and torsion have been examined. Relative stiffnesses of different parts forming the wind blade’s cross section (i.e., shell, web, and spar cap) are determined. The corresponding stresses associated with each part under the governing loads are compared to allowable strength values which are determined from composite laminate theory and modelling of the known laminate structure of the E-Glass FRP material. Stresses and deflections obtained are compared with governing reliability-based design criteria and code requirements. The results of the structural analysis indicate that the wind blade can resist the expected loads with reasonable safety factors and that the expected deflections are within permissible limits. Recommendations are provided for detailing and modification of the wind blade for a power pole application in which crossarm and davit connections are highlighted, and foundation details are emphasized. 

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2. Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

   https://doi.org/10.1109/DCAS53974.2022.9845651  

   Karami, Farzad; Kehtarnavaz, Nasser; Rotea, Mario (June 2022, 2022 IEEE 15th Dallas Circuit And System Conference (DCAS))

   Each year a growing number of wind farms are being added to power grids to generate sustainable energy. The power curve of a wind turbine, which exhibits the relationship between generated power and wind speed, plays a major role in assessing the performance of a wind farm. Neural networks have been used for power curve estimation. However, they do not produce a confidence measure for their output, unless computationally prohibitive Bayesian methods are used. In this paper, a probabilistic neural network with Monte Carlo dropout is considered to quantify the model or epistemic uncertainty of the power curve estimation. This approach offers a minimal increase in computational complexity and thus evaluation time. Furthermore, by adding a probabilistic loss function, the noise or aleatoric uncertainty in the data is estimated. The developed network captures both model and noise uncertainty which are found to be useful tools in assessing performance. Also, the developed network is compared with the existing ones across a public domain dataset showing superior performance in terms of prediction accuracy. The results obtained indicate that the developed network provides the quantification of uncertainty while maintaining accurate power estimation. 

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3. Structural Re-Use of De-Commissioned Wind Turbine Blades in Civil Engineering Applications

   Alshannaq, Ammar; Scott, David; Bank, Lawrence; Bermek, Mehmet; Gentry, Russell (September 2019, Proceedings, American Society for Composites 2019 Annual Meeting)

   The production of wind energy worldwide has increased 20-fold since 2001. Composite material wind turbine blades, typically designed for a 20-year fatigue life, are beginning to come out of service in large numbers. In general, these de-commission blades, composed primarily of glass fibers in a thermoset matrix, are demolished and landfilled. There is little motivation for recycling the composite materials, as the processes for reclaiming the fibers (solvolysis, pyrolysis) have not been proven to be economically viable. This research seeks to establish structural re-use applications for wind turbine blades in civil engineering infrastructure, hypothesizing that advanced composite materials may be an attractive alternative to conventional infrastructure materials (e.g. steel, reinforced concrete). This paper presents an analysis and materials characterization of a 47 meter Clipper C96 wind blade. The primarily numerical analysis is accompanied by materials characterization taken from an un-used Clipper blade donated to the project from the Wind Turbine Testing Center (WTTC). The paper presents a brief background on wind turbine blade adaptive re-use, proposing a hypothetical load bearing application of the Clipper wind blade as an electrical transmission tower structure carrying axial compression, along with flapwise and edgewise bending forces. The paper summarizes the composite laminates and cross-section geometries of the blade and establishes the axial and flexural stiffnesses of the blade at multiple sections along the blade length. From a first-order estimation of applied loads for the tower application, the resulting stresses in the composite materials are estimated and compared to the design material properties for the wind blade as originally constructed. 

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4. Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

   https://doi.org/10.1002/nav.21938  

   Pan, Qiyun; Byon, Eunshin; Ko, Young Myoung; Lam, Henry (August 2020, Naval Research Logistics (NRL))

   Abstract Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study. 

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5. On Wind Turbine Loads During Thunderstorm Downbursts in Contrasting Atmospheric Stability Regimes

   https://doi.org/10.3390/en12142773  

   Lu, Nan-You; Hawbecker, Patrick; Basu, Sukanta; Manuel, Lance (July 2019, Energies)

   Severe winds produced by thunderstorm downbursts pose a serious risk to the structural integrity of wind turbines. However, guidelines for wind turbine design (such as the International Electrotechnical Commission Standard, IEC 61400-1) do not describe the key physical characteristics of such events realistically. In this study, a large-eddy simulation model is employed to generate several idealized downburst events during contrasting atmospheric stability conditions that range from convective through neutral to stable. Wind and turbulence fields generated from this dataset are then used as inflow for a 5-MW land-based wind turbine model; associated turbine loads are estimated and compared for the different inflow conditions. We first discuss time-varying characteristics of the turbine-scale flow fields during the downbursts; next, we investigate the relationship between the velocity time series and turbine loads as well as the influence and effectiveness of turbine control systems (for blade pitch and nacelle yaw). Finally, a statistical analysis is conducted to assess the distinct influences of the contrasting stability regimes on extreme and fatigue loads on the wind turbine. 

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