Technological cost%3CU%2B2010%3Ereduction pathways for axial%3CU%2B2010%3Eflow turbines in the marine hydrokinetic environment.

This report considers and prioritizes potential technical costreduction pathways for axialflow turbines designed for tidal, river, and ocean current resources. This report focuses on technical research and development costreduction pathways related to the device technology rather than environmental monitoring or permitting opportunities. Three sources of information were utilized to understand current cost drivers and develop a list of potential costreduction pathways: a literature review of technical work related to axialflow turbines, the U.S. Department of Energy Reference Model effort, and informal webinars and other targeted interactions with industry developers. Data from these various information sources were aggregated and prioritized with respect to potential impact on the lifetime levelized cost of energy. The four most promising costreduction pathways include structural design optimization; improved deployment, maintenance, and recovery; system simplicity and reliability; and array optimization

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Advanced Computational Modeling Methods for Floating Offshore Wind Systems

Floating offshore wind energy is anticipated to become a competitive source of renewable energy by the late 2020s, but the industry must reduce costs and uncertainties associated with the technology to do so. Identifying solutions to these problems frequently relies on computational modeling, which presents technical shortcomings limiting the versatility of the modeling results. This work presents two improvements that augment existing computational tools, allowing researchers to more accurately model floating offshore wind systems and reduce uncertainty. The first study presents a custom optimization algorithm to identify the relationship between cost and reliability for floating offshore wind farms using shared anchoring. The algorithm minimizes the added costs associated with failure by optimizing the strength of anchors for a case study of a large wind farm under simulated survival load conditions. The optimization algorithm merges a genetic algorithm with elements of Bayesian optimization to account for uncertainty from the probability of failure and the resulting repair costs. The second study presents a numerical model created by modifying the OpenFAST floating wind turbine simulation software. This numerical model solves for aerodynamic loading in a real-time hybrid simulation, allowing for integration with physical wave basin experiments with proper scaling and higher fidelity than computer simulations. Validation shows that the model accurately generates aerodynamic loads efficiently enough to be used in a real-time hybrid simulation setup. The study also identifies modifications that will allow for modeling of tower top elasto-servo dynamics in future work

Rising stars in energy research: 2022

Recognising the future leaders of Energy Research is fundamental to safeguarding tomorrow's driving force in innovation. This collection will showcase the high-quality work of internationally recognized researchers in the early stages of their careers. We aim to highlight research by leading scientists of the future across the entire breadth of Energy Research, and present advances in theory, experiment and methodology with applications to compelling problems

Soft Computing approaches in ocean wave height prediction for marine energy applications

El objetivo de esta tesis consiste en investigar el uso de técnicas de Soft Computing (SC) aplicadas a la energía producida por las olas o energía undimotriz. Ésta es, entre todas las energías marinas disponibles, la que exhibe el mayor potencial futuro porque, además de ser eficiente desde el punto de vista técnico, no causa problemas ambientales significativos. Su importancia práctica radica en dos hechos: 1) es aproximadamente 1000 veces más densa que la energía eólica, y 2) hay muchas regiones oceánicas con abundantes recursos de olas que están cerca de zonas pobladas que demandan energía eléctrica. La contrapartida negativa se encuentra en que las olas son más difíciles de caracterizar que las mareas debido a su naturaleza estocástica. Las técnicas SC exhiben resultados similares e incluso superiores a los de otros métodos estadísticos en las estimaciones a corto plazo (hasta 24 h), y tienen la ventaja adicional de requerir un esfuerzo computacional mucho menor que los métodos numérico-físicos. Esta es una de las razones por la que hemos decidido explorar el uso de técnicas de SC en la energía producida por el oleaje. La otra se encuentra en el hecho de que su intermitencia puede afectar a la forma en la que se integra la electricidad que genera con la red eléctrica. Estas dos son las razones que nos han impulsado a explorar la viabilidad de nuevos enfoques de SC en dos líneas de investigación novedosas. La primera de ellas es un nuevo enfoque que combina un algoritmo genético (GA: Genetic Algorithm) con una Extreme Learning Machine (ELM) aplicado a un problema de reconstrucción de la altura de ola significativa (en un boya donde los datos se han perdido, por ejemplo, por una tormenta) utilizando datos de otras boyas cercanas. Nuestro algoritmo GA-ELM es capaz de seleccionar un conjunto reducido de parámetros del oleaje que maximizan la reconstrucción de la altura de ola significativa en la boya cuyos datos se han perdido utilizando datos de boyas vecinas. El método y los resultados de esta investigación han sido publicados en: Alexandre, E., Cuadra, L., Nieto-Borge, J. C., Candil-García, G., Del Pino, M., & Salcedo-Sanz, S. (2015). A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction. Ocean Modelling, 92, 115-123. La segunda contribución combina conceptos de SC, Smart Grids (SG) y redes complejas (CNs: Complex Networks). Está motivada por dos aspectos importantes, mutuamente interrelacionados: 1) la forma en la que los conversores WECs (wave energy converters) se interconectan eléctricamente para formar un parque, y 2) cómo conectar éste con la red eléctrica en la costa. Ambos están relacionados con el carácter aleatorio e intermitente de la energía eléctrica producida por las olas. Para poder integrarla mejor sin afectar a la estabilidad de la red se debería recurrir al concepto Smart Wave Farm (SWF). Al igual que una SG, una SWF utiliza sensores y algoritmos para predecir el olaje y controlar la producción y/o almacenamiento de la electricidad producida y cómo se inyecta ésta en la red. En nuestro enfoque, una SWF y su conexión con la red eléctrica se puede ver como una SG que, a su vez, se puede modelar como una red compleja. Con este planteamiento, que se puede generalizar a cualquier red formada por generadores renovables y nodos que consumen y/o almacenan energía, hemos propuesto un algoritmo evolutivo que optimiza la robustez de dicha SG modelada como una red compleja ante fallos aleatorios o condiciones anormales de funcionamiento. El modelo y los resultados han sido publicados en: Cuadra, L., Pino, M. D., Nieto-Borge, J. C., & Salcedo-Sanz, S. (2017). Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms. Energies, 10(8), 1097

Aeronautics and space report of the President, 1982 activities

Achievements of the space program are summerized in the area of communication, Earth resources, environment, space sciences, transportation, aeronautics, and space energy. Space program activities of the various deprtments and agencies of the Federal Government are discussed in relation to the agencies' goals and policies. Records of U.S. and world spacecraft launchings, successful U.S. launches for 1982, U.S. launched applications and scientific satellites and space probes since 1975, U.S. and Soviet manned spaceflights since 1961, data on U.S. space launch vehicles, and budget summaries are provided. The national space policy and the aeronautical research and technology policy statements are included

Fault detection of a wind turbine generator bearing using interpretable machine learning

A wind turbine is subjected to a number of degradation mechanisms during its operational lifetime. If left unattended, the degradation of components will result in poor performance and potential failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbines are regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally six-month intervals) and the maintenance program (list of tasks) is prepared using experience or risk-based reliability analysis, like risk-based inspection (RBI) and reliability-centered maintenance (RCM). This time-based maintenance program can be improved by incorporating results from condition monitoring (CM) involving data acquisition using sensors and fault detection using data analytics. It is important to ensure quality and quantity of data and to use correct procedures for data interpretation for fault detection to properly carry out condition assessment. This thesis contains the work carried out to develop a machine learning (ML) based methodology for detecting faults in a wind turbine generator bearing. The methodology includes application of ML using supervisory control and data acquisition (SCADA) data for predicting the operating temperature of a healthy bearing, and then comparing the predicted bearing temperature with the actual bearing temperature. Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing. This fault detection can then be used for rescheduling the maintenance tasks. The methodology is discussed in detail using a case study. In this thesis, interpretable ML tools are used to identify faults in a wind turbine generator bearing. Furthermore, variables affecting the generator bearing temperature are investigated. The analysis used two years of operational data from a 2 MW offshore wind turbine located in the Gulf of Guinea off the west coast of Africa. Out of the four ML models that were evaluated, the XGBoost model was determined to be the most effective performer. After utilizing the Shapley additive explanations (SHAP) to analyze the XGBoost model, it was determined that the temperature in the generator phase windings had the most significant effect on the model's predictions. Finally, based upon the deviation between the actual and the predicted temperatures, an anomaly in the generator bearing was successfully identified two months prior to a generator failure occurring.Masteroppgave i havteknologiHTEK3995MAMN-HTEKMAMN-HTE

Market Potential Analysis of a Solar Hybrid Dish-Brayton System

In this study, the market potential of a solar hybrid dish-Brayton system has been analyzed, using a market analysis of four relevant industries and the techno economic analysis of the system, operating in a stand-alone configuration. The industries assessed were desalination, produce drying, Steam Methane Reforming for Hydrogen Production and Compressed air for the mining industry. After taking into consideration, the various factors that affect each industry, the applicability of the technology in industrial processes varies. It was found that the technology would be a good fit in small scale applications in remote locations for both desalination and for supplying compressed air in the mining industry. Produce Drying requires the targeted industries to be well-established, large-scale players wanting to decarbonize their processes. Owing to the prohibitive initial capital of the system, it would not be feasible with small scale market players. In locations where there was high DNI and higher costs of natural gas, the technology can be used in the thermal process in Steam Methane reforming for Hydrogen production. The techno economic analysis was carried out in three different locations, that has high DNI and relevant industries present. It was found that the price of natural gas and the DNI plays the major role in determining the Levelized cost of Energy at a location. The biggest costs factor in the initial capital spent was the expense of the dish. Future developments in cheaper material with similar levels of reflectivity, and the economies of scale, due to increase of production, stemming from increased demand would reduce the cost of the technologyObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminan