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Worldwide wind energy generation capacity has grown rapidly over the past several decades, and wind turbines installed at the beginning of this wave of growth are approaching the end of their design lifetimes. As an increasing number of wind power plants reach their end of life, both decommissioning and repowering (i.e., dismantling or refurbishing existing turbines and commissioning new ones) will produce waste material from the retired wind turbines, foundations, and balance of plant. However, the amount and type of waste, particularly for wind blades, is often mischaracterized. Although wind turbine components are largely recyclable, the blades are typically made of fiberglass composites, which can present challenges for material recovery and reuse. Within the USA, the accumulation of wind turbine blades in landfills has raised questions about whether the continued expansion of wind energy is sustainable if it results in substantial future waste. This study compares the mass and volume of potential global wind blade waste to other waste streams. It also discusses the materials used to manufacture wind turbine blades and summarizes current options for material redesign, recycling (recovery and reuse), repurposing, and disposal of used blades. The analysis indicates that, although wind turbine blades could represent 14% of the composite market by 2027, the potential future mass and volume of wind turbine blade waste is relatively small compared to other industries. These findings suggest that although the development of scalable, economically viable, and environmentally sustainable methods for wind turbine manufacturing, repurposing, and recycling is important, it may make sense to take advantage of synergies among multiple industries in recycling composite waste, rather than focusing solely on wind turbine blades. From a global perspective, larger sustainability, recycling, and waste stream reduction impacts can be made in other industries, such as transportation and construction. 

Addressing resource intermittency is crucial for designing effective and economical renewable energy systems for many applications. Hydrogen as long-term energy storage medium shows promise for increasing renewables penetration into the grid. Cost-effective hybrid wind-hydrogen microgrids (HWHMs) require system-level sizing of each subcomponent. This study employs low-order HWHM component models in a system-level framework to predict HWHM performance. It introduces a novel approach to investigate the optimal sizing of HWHMs. The study uniquely addresses the impact of wind speed fluctuation amplitudes and frequency variations on system design – an area not previously explored. The model is run for 7 days using several different wind speed profiles and real load demand data from an off-grid Naval facility on an island in California. In our test cases, the findings indicate that fewer wind turbines and more hydrogen tanks are required to successfully meet demand when wind speed fluctuations increase. For example, when the wind speed fluctuation increases from 0.68 to 2.04 m/s, and the wind turbine is expected to maintain an average power equivalent to 90% of the peak load, the turbine capacity drops by 17%, requiring a 304% rise in the number of tanks. However, the frequency of wind speed variation has a negligible effect on the optimal HWHM configuration. Through a rule-based optimization algorithm, this research offers important insights for designing reliable microgrids capable of meeting critical loads despite highly variable wind conditions. 

A response surface methodology was used to analyze the flow rate, power, and time factors of plasma surface treatment. Surface free energy (SFE) of treated glass fiber-reinforced composites showed a strong quadratic dependence on flow rate, power, and time, with significant interaction between time and power. Optimized factors predicted a maximum SFE of 78.63 mN/m, which matched well with the measured value of 77.42 mN/m, accounting for 2.46 times increase in SFE against untreated case. Moreover, with plasma treatment, the SFE’s polar component became dominant (99%) as also confirmed with FTIR spectroscopy. Fracture toughness testing of fresh and aged adhesive joints proved a more stable interface for plasma-treated specimens due to the covalent bonds facilitated by the functional groups formed during the treatment. Consequently, the fracture toughness of the plasma-treated specimens did not drop after seawater immersion, while that for the untreated and sand-treated specimens showed about a 15% drop. 

To meet 2050 decarbonization goals, Massachusetts will not be able to rely on carbon intensive energy sources (e.g. natural gas and gasoline) and hydrogen has been considered a replacement. To produce hydrogen without carbon emissions, renewable energy sources will be used to power electrolyzer stacks. However, renewable energy sources will also be in high demand for other energy sectors, such as automobiles and electrification. This paper estimates the amount of wind energy needed to replace natural gas with hydrogen and electrify automobiles. Comparisons are also made for a scenario in which heat pumps are used to replace natural gas. These energy sectors represent the bulk of energy consumed within Massachusetts and are of high interest to stakeholders globally. The analysis reveals the daunting amount of wind energy needed for replacement and that it is highly unlikely for hydrogen to replace natural gas in time to meet the state’s climate goals. 

Carbon fiber reinforced composites often exhibit large amounts of property scatter. Attempts at understanding composite property scatter have led researchers to generate many 2D models which ignore the 3D phenomenon of entanglement. Previous studies of entanglement have suggested it is correlated to a length scale, but have not had large enough samples to determine its size. In this study, fiber paths of long, entangled, continuous fibers were extracted from CT data of an automotive grade, heavy tow composite. Descriptive metrics of these fiber paths were used to quantify the entanglement as a function of position along the fiber direction. Using this data, several minimum length scales for capturing the behavior of multiple descriptors were determined. These length scales revealed where statistical representation of 3D fiber models provides superior information to that of 2D models. 

This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.