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dc.contributor.advisorShek, Jonathan
dc.contributor.advisorThies, Philipp
dc.contributor.advisorLazakis, Iraklis
dc.contributor.authorEwing, Fraser J.
dc.date.accessioned2021-11-11T15:52:40Z
dc.date.available2021-11-11T15:52:40Z
dc.date.issued2021-11-27
dc.identifier.urihttps://hdl.handle.net/1842/38256
dc.identifier.urihttp://dx.doi.org/10.7488/era/1522
dc.description.abstractMarine renewable energy has the potential to form a key part of the renewable electricity generation arsenal that is required for society to prevent catastrophic global warming. Device reliability is one of the great engineering hurdles facing the sector; operating cost effectively in the harsh open ocean is very challenging. The nascent stage of the sector means that accurate reliability assessment of devices is difficult because of a lack of relevant data, uncertainty about loading conditions and a lack of consensus on device designs and architectures. This research develops and applies a suite of reliability prediction methods for use across the tidal turbine development life cycle. A standards based statistical approach is developed for the assessment of the entire turbine; the pitch system is found to have a particularly high failure rate. An empirical approach is developed and applied on a pitch system to allow for the relevant reliability influencing design parameters to be uncovered. A Bayesian framework is developed to enable new information to be incorporated into existing models; this is of particular interest to nascent technologies. Finally a Physics of Failure approach is developed for the pitch system roller bearing unit; measured flow data is used to predict the fatigue life and reliability via a hydrodynamic model. The methods presented along with the results will assist reliability assessment and prediction during tidal turbine design and early operation. Improved reliability assessment at each stage of turbine development can hopefully assist the industry to cross the ’valley of death’ and become commercially viable.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionFraser Ewing, P.R. Thies, J.K. Shek and Claudio Bittencourt-Ferreira, Probabilistic failure rate model of a tidal turbine pitch system, Renewable Energy, 2020en
dc.relation.hasversionFraser Ewing, P.R. Thies, J.K. Shek and Claudio Bittencourt-Ferreira, A Physics-based prognostics approach for tidal turbines, Proceedings of 2019 IEEE Conference on Prognostics and Health Managementen
dc.relation.hasversionFraser Ewing, P.R. Thies, Iraklis Lazakis and Claudio Bittencourt-Ferreira, Bayesian reliability modelling of a tidal turbine pitch system, Proceedings of 2018 Asian Wave & Tidal Energy Conferenceen
dc.relation.hasversionFraser Ewing, P.R. Thies and Benson Waldron, A Bayesian updating framework for simu lating marine energy converter drive train reliability, Proceedings of 2018 Marine Energy Technology Symposiumen
dc.relation.hasversionFraser Ewing, P.R. Thies, J.K. Shek, Benson Waldron and Michael Wilkinson, Reliability prediction for offshore renewable energy: data driven insights, Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineeringen
dc.subjectturbine assessmenten
dc.subjectpitch systemen
dc.subjectPhysics of Failureen
dc.subjectreliability assessmenten
dc.subjecttidal turbine design assessmenten
dc.titleReliability prediction for tidal turbinesen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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