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This study uses novel evolutionary algorithms and computational techniques to analyze wind potential on flat, complex coastal, and offshore sites utilizing mast as well as remote sensing data. The wind data were recorded using remote sensing technique and conventional technique. The optimum Weibull parameters are estimated using nine methods. The genetic algorithm, particle swarm optimization, and TLBO algorithms are compared and evaluated. The goodness of fit test, such as root mean square error test (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and chi-square test (X2), is used to evaluate the accuracy of the selected methods. Parameter estimates are used to compute wind densities. The TLBO and PSO algorithms outperformed genetic algorithms in terms of efficiency. This research compares remote sensing measurements to cup anemometer measurements. [ABSTRACT FROM AUTHOR]

A major challenge to the emergence and establishment of new energy technologies such as ocean wave energy is the insufficient or nonexistent database for estimating resource potential. Despite having an extensive coastline, wave energy sources are yet to be developed in India. Hence, the main objective of this study is to identify the potential sites and assess the technical and economic feasibility of harnessing wave energy along the Indian coast. Three hotspots are identified in three coastal regions (east, south and west) based on optimum hotspot index and depth constrain criterion using 19 years of high-resolution wave hindcast. Results indicate that the theoretical potential of location along the west coast (12 kW/m) is highest as compared to a hotspot near the south (8 kW/m) and east coast (6 kW/m). The technical potential and cost of electricity generation at hotspot locations are estimated and compared using four different wave energy converters (WECs): Wavedragon, Pelamis, Oceantec, and Aquabuoy. Oceantec, among all WECs, generates more power (40–57 GWh) and attain a maximum capacity factor (22–31%) as well as the most cost-effective WEC with the lowest Levelized Cost of Energy (LCOE) ranging from 354 to 505 €/MWh at all hotspots. Economic sensitivity analysis reveals that interest rate and operation and maintenance costs are the most and least sensitive parameters, respectively. Outcomes of the present study will contribute to reducing the barriers to the current knowledge of wave energy resources in India. [ABSTRACT FROM AUTHOR]

Purpose: The assessment of the economic feasibility of floating offshore wind farms (FOWFs) plays an important role in the future possible spreading of this challenging technology in the wind power industry. The use of specific economic analyses is fundamental to point out the potential of FOWFs and to sustain their technical value. Within this topic, the implementation of the FOWF life cycle cost model and producibility analysis in a geographic information system is developed, with the aim of carrying out a feasibility analysis at the territorial scale, for different types of floater. Moreover, a simplified model for a quick life cycle cost assessment is proposed and calibrated. Methods: The available cost model is first validated comparing the costs of FOWFs based on different floaters (Semi-Submersible Platform—SSP, Spar Buoy—SB and Tension Leg Platform—TLP) with corresponding results available in the literature. Then, it is implemented in QGIS to be used for territorial-scale analyses and sensitivity analyses of the cost parameters. A feasibility analysis is developed through the main financial parameters. Finally, the results are then used to calibrate a simplified version of the cost model that depends on three main parameters, namely distance to shore, distance from the port of operation and bathymetry. Results and discussion: The FOWF cost values are found to be in good agreement with those coming from analytical methods similar to the one from the authors. However, some discrepancies with those based on average costs are observed. Then, the results of the sensitivity analysis are presented as life cycle cost maps, giving an overall picture of the variation of the total cost of FOWF installations on a reference domain. The results show that among the three types of floaters considered here, the SSP proved to be the most promising one, giving lower costs than the SB and the TLP. Moreover, a good agreement between the results in terms of total cost of FOWFs calculated with the analytical and simplified models for SSPs, SBs and TLPs is observed. Finally, the feasibility analysis showed that the financial parameters are more influenced by the wind speed than by the cost of the farm. Conclusions: The paper aims to provide guidance on how to carry out feasibility analyses of a specific site for FOWF installation, thus supporting decision-making procedures. The approach and the results presented here are meant for use in the early stage of the decision-making process, as a tool for the assessment of the economic feasibility of FOWFs installation. [ABSTRACT FROM AUTHOR]

Surveillance drones are remarkable devices for monitoring, as they have strong spatial and remote sensing capabilities. The prompt detection of peripheral damage to the blades of wind turbines is necessary to reduce downtime and prevent the potential failure of wind farms. Computer vision breakthroughs with deep learning have developed and been refined over time, mainly using convolution neural networks. From this perspective, we suggest a deep learning model for monitoring and diagnosing the blade health of wind turbines based on images captured by surveillance drones. The main limitations of standard monitoring devices are their poor detection accuracy and lack of real-time performance, making it complex to obtain the attributes of blades from aerial images. Based on the foregoing, this study introduces a method for increasing detection accuracy when carrying out operations in real time using You Only Look at Once version 3 (YOLOv3). We train and evaluate three deep learning models on the wind turbine image dataset. We find that many aerial images are unclear because of blurred motion. As avoiding such low-resolution images for training can affect accuracy, we use a super-resolution convolution neural network to reconstruct a blurred picture as a high-resolution one. The computational results demonstrate that YOLOv3 outperforms traditional models in terms of both accuracy and handling time. [ABSTRACT FROM AUTHOR]

The aim of this paper is to drive the discourse towards the increasing shift to renewables, especially offshore wind energy generation, in the emerging international energy order. The Indian Ocean Region (IOR), despite its increasing contribution to onshore wind energy generation and impending policies on offshore wind energy, is reluctant to invest in the latter. Hence, this paper highlights four important aspects that challenge IOR's offshore wind energy development: Indian Ocean's strategic location, environment impacts, blue economy and maritime terrorism. In the background of the geopolitical rivalry existing in the Indian Ocean Region (IOR), with the increasing presence of China and the USA in the Indian Ocean, this paper aims to study if these geopolitical challenges are hindering offshore wind energy generation in IOR. The key findings of the paper include the necessity of addressing the geopolitical rivalry in IOR as an important hindrance in huge investments needed in OWE farms, so that a regional cooperative mechanism is arrived at especially from the point of view of policies towards OWE generation. [ABSTRACT FROM AUTHOR]

Location selection for offshore wind farms is a major challenge for renewable energy policy, marine spatial planning, and environmental conservation. This selection constitutes a multi-criteria decision-making problem, through which parameters like wind velocity, water depth, shorelines, fishing areas, shipping routes, environmental protection areas, transportation, and military zones should be jointly investigated. The aim of the present study was thus to develop an integrated methodology for assessing the siting of bottom-fixed offshore wind farms in two different countries (with different legal, political, and socio/economic characteristics). Our methodology combined multi-criteria decision-making methods and geographical information systems and was implemented in Cyclades (Greece) and in the sea area of İzmir region (Turkey). Experts used fuzzy sets and linguistic terms to achieve more consistent and independent rankings and results. In the Turkish region, the results showed that 519 km2 (10.23%) of the study area is suitable for offshore wind farms, while in the Greek region, only 289 km2 (3.22%) of the study area was found to be suitable. This spatial suitability analysis may contribute to provide some useful recommendations for the spatial marine planning at the regional scale, as well as for the preliminary assessment of new offshore wind farms in both countries. [ABSTRACT FROM AUTHOR]

Solar energy generated by grid-connected photovoltaic (GCPV) systems is considered an important alternative electric energy source because of its clean energy production system, easy installation, and low operating and maintenance costs. This has led to it becoming more popular compared with other resources. However, finding optimal sites for the construction of solar farms is a complex task with many factors to be taken into account (environmental, social, legal and political, technical-economic, etc.), which classic site selection models do not address efficiently. There are few studies on the criteria that should be used when identifying sites for solar energy installations (large grid-connected photovoltaic systems which have more than 100 kWp of installed capacity). It is therefore essential to change the way site selection processes are approached and to seek new methodologies for location analysis. A geographic information system (GIS) is a tool which can provide an effective solution to this problem. Here, we combine legal, political, and environmental criteria, which include solar radiation intensity, local physical terrain, environment, and climate, as well as location criteria such as the distance from roads and the nearest power substations. Additionally, we use GIS data (time series of solar radiation, digital elevation models (DEM), land cover, and temperature) as further input parameters. Each individual site is assessed using a unique and cohesive approach to select the most appropriate locations for solar farm development in the Valencian Community, a Spanish region in the east of Spain. [ABSTRACT FROM AUTHOR]

Biomasses are materials of organic origin that can be used for the production of energy. Among the renewable energy sources, a prominent role is played by woody biomass, which can be retrieved from existing forests or plantations governed in short or middle rotation coppice, the so-called Short and Medium Rotation Forestry. The main environmental benefit resulting from the use of wood biomass consists in the fact that the amount of carbon dioxide released during their combustion process is the same as that absorbed during the development phase. Here, we propose a procedure to manage the traceability of short biomass chains and to schedule the activities for mobile forest construction sites using free and open-source GIS technologies. Firstly, we created a spatial DB to manage the areas where the cutting and logging activities are performed. Then, we overlapped the boundaries of areas with the cadastral sheets to ensure the biomass comes from short chain, i.e., within the range of 70 km from the position of the central. To plan the number of working days required and make an estimation of the production, the total area of the lots, the land clearing, and the net area were calculated. Finally, depending on the characteristics of wooded areas, the type of system to be used for the business of cutting and logging was evaluated. This work demonstrates how GIS allows a fast traceability of short biomass chains and an estimation of the production by improving the efficiency and effectiveness of biomass resource assessment. [ABSTRACT FROM AUTHOR]

The fatigue life of megawatt-scale composite wind turbine blades implemented with shallow-angled laminates is investigated. Full dynamic simulations of a 5-MW 3-bladed horizontal-axis wind turbine were carried out to estimate the incident fatigue loads. The fatigue stress histories experienced by lay up materials were then computed using a finite-element model of the composite blade developed to ply-level details. Thereafter, the fatigue life of blade was estimated considering the 100% availability of the turbine for the site-specific annual wind distribution related to the wind turbine class IB. It is demonstrated that the application of shallow-angled laminates leads to a more durable design. [ABSTRACT FROM AUTHOR]

We report the results of an experimental campaign about the production of hydrogen from methane cracking using a non-thermal plasma. Experiments have been performed using a nanosecond pulse high-voltage generator in a cylindrical dielectric barrier electrode setup. Our experiments show that high methane conversion could be achieved by pulsed electrical discharges in DBD configuration. Conversion could be as high as 60%, with a hydrogen yield of about 25%. The energy costs lie in the range 30-40 eV for molecule. Another set of experiments using a traditional sinusoidal dielectric barrier discharge reactor suggests that argon dilution could improve the performances of plasma methane reforming. A similar suggestion could be inferred by analyzing the results of numerical simulations of the gas-phase chemical kinetics evolution under pulsed electrical discharge conditions. [ABSTRACT FROM AUTHOR]

Thermoelastic stress analysis (TSA) is used to derive the surface stresses in large sandwich structure panels with honeycomb core and carbon fibre face sheets. The sandwich panels are representative of those used for secondary aircraft structure. The panels were subjected to a pressure load, similar to that experienced in-service, using a custom designed test rig. To achieve the necessary adiabatic conditions for TSA, cyclic loading is regarded as an essential feature. As the panels were full-scale, the maximum loading frequency that could be imparted to the panels by the rig was 1 Hz, which is below the usual range recommended to achieve adiabatic behaviour. To assess the effectiveness of TSA at low frequencies two approaches to calibration are investigated and compared with the stress distribution obtained from independently validated FE models. The thermoelastic response was calibrated into stress data using thermoelastic constants derived experimentally from tensile strips of the sandwich panel face sheet material. It is shown that by using thermoelastic constants obtained from the tensile strips manufactured with the same lay-up as the sandwich panel face sheets, and at the same cyclic load frequency used in the full-scale tests, quantitative stress metrics can be derived from the TSA data. More significantly, a deeper insight into the importance of the thermal characteristics in TSA of laminated materials is provided. It is demonstrated that, for the material used in this work, it is possible to use the global material behaviour to obtain quantitative results when adiabatic conditions do not prevail. [ABSTRACT FROM AUTHOR]

It has been observed that there is a systematic detection of weak pressure stimulated electric currents (PSC) in polycrystalline and amorphous solids during or after an application of mechanical stress upon them. An interpretation of the mechanisms governing the electrical behaviour of solid structures when they undergo uniaxial stress tests is presented. In numerous experiments, repetitive stress loadings and unloadings have been conducted, the corresponding PSC have been recorded, and the behaviour of the material is interpreted with respect to PSC emission. The dominant conclusion is that the behaviour of the solid depends on the overall stress it has suffered and that PSC is related with a memory effect which associates current emission with the previous history of the sample. [ABSTRACT FROM AUTHOR]

Mature cataract had caused change in appearance of the eye and complete blindness in an aged African lion. Early surgical removal of the lens is the only effective treatment for mature cataract, and if surgery is not performed soon after maturation of the cataract, total blindness may remain after surgery. The present report describes a mature cataract of unknown cause with anterior lens luxation in a 20-year-old African lion treated by intracapsular extraction of the lens. On clinical examination, immature cataract of the right eye and mature cataract with anterior lens luxation of left eye were diagnosed. The entire lens, including anterior and posterior capsules, was removed by routine surgical techniques described for such a procedure. The lion suffered self-trauma of the eye 48 h after surgery, and three sutures were disrupted. Antibiotic drops and Cefazolin were administrated with extreme difficulty for 1 week. The lion was saved, but keratitis developed in the left eye. [ABSTRACT FROM AUTHOR]