30 research outputs found

    Development of the C-GEN generator technology for vertical axis wind turbines

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    In this thesis, 5 MW, 7.5MWand 10MWat 6 rpm C-GEN generator models were designed and optimized respectively for a vertical axis wind turbine. Although VAWTs have lower rotational speed than HAWTs, the offered C-GEN VAWT generators have significant higher power density compared to conventional PM and superconducting HAWT generators of equivalent power. The inner radius of the 5 MW C-GEN generator is 5.35 m and mass is 41.2 tonnes. The inner radius of the 7.5 MW C-GEN generator is 5.35 m and active mass is 44.1 tonnes. The inner radius of the 10 MW C-GEN generator is 7.50 m and active mass is 41.6 tonnes. Annual generation results show that offshore VAWT can generate as much energy as HAWT of the same power. In addition, by the end of 2022, the most powerful single offshore HAWT is 14 MW and due to the tower head mass of HAWTs, it is limited to increase the power further. Multi-power platform VAWTs can take the power of a single turbine further. The C-GEN model with wavy and comb steel structure has higher power density than the C-GEN model with straight steel structure. In addition, in the multi-stage C-GEN models, the comb steel structure will allow the passage of air between the stages, and since the wavy steel structure has more surface area than straight steel, it can help to increase the thermal performance of the machine. Since machine mass is an important factor in aviation, automotive propulsion systems and renewable energy converters, these structures can provide advantage. The machines are analysed and optimised electromagnetically using 2-D FEA simulations. A software algorithm has been developed for the simulations. This algorithm allows any electrical machine to be modelled and optimized easily and quickly. In addition, this algorithm can be applied to 3-D models and other branches of engineering such as mechanical, civil, naval, aircraft and etc. for use CAD and FEA models

    A Microgrid Energy Management System Based on Non-Intrusive Load Monitoring via Multitask Learning

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    Motivators and Problems of Being a Global Migrant (Outsource) Soccer Labor in Turkey: A Preliminary Study

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    The aim of this study was to reveal the motivators and problems of soccer players that pursue their professional careers in Turkey. In this qualitative research, convenience sampling and criterion sampling, which are kinds of nonrandom sampling methods was used to determine the participants. Eight male soccer players from the Genclerbirligi Soccer Team who were complied with the research criteria, voluntarily participated in the study. The demographic information form and semi-structured interview form were used as data collection tools. All interviews were recorded by audio and video recording devices. Data was analyzed by content analysis. The findings showed that; this study was supported the "mercenary", "ambiotis" and "nomadic cosmopolitan" items, which are in typologies that motivates athletes to migrate to a host country. In addition, language and cultural differences were the main problems for migrant athletes. In conclusion, this study demonstrated that besides the items in the typologies, "positive information" can facilitate the migration of athletes to a host country. Furthermore, it expressed the importance of inquiry on the cultural structure of the host country for migrant athletes, in order to minimize negative influences of problems

    Announcement of Retraction

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    The editorial board announced this article has been retracted on February 2, 2020.If you have any further question, please contact us at: [email protected] Title: Effect of Ballistic Warm-Up on Isokinetic Strength, Balance, Agility, Flexibility and Speed in Elite Freestyle WrestlersAuthor/s: Ebru Çetin, Celal Bulğay, Sezen Çimen Polat, Halil İbrahim Cicioğlu, İmdat YarimJournal Title: Journal of Education and Training StudiesISSN 2324-805X   E-ISSN 2324-8068Volume and Number: Vol. 6, No. 11, 2018Pages: 143-149DOI: 10.11114/jets.v6i11.3635                

    Smart-Building Applications:Deep Learning-Based, Real-Time Load Monitoring

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