Thesis

Raindrop erosion of wind turbine blades in various environments

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Awarding institution
  • University of Strathclyde
Date of award
  • 2023
Thesis identifier
  • T16624
Person Identifier (Local)
  • 201856442
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Renewable energy is the solution to a greener, more sustainable future and wind energy is a massive contributor, especially in Scotland. One of the limiting factors to wind turbines is the leading-edge erosion of the blades. This is a developing and growing issue in large multi mega-watt wind turbines within the wind industry as it affects blade performance and thus generating efficiency. The research presented aims to contribute to the scientific community investigating this topic by developing a fuller picture of the problem encountered in laboratory testing, analysing data and utilising the results in predictive models. This research work builds upon previous investigations into the erosion of wind turbine blades by determining the effect that stress has on the erosion rate and therefore the efficiency of the turbine this is particularly applicable with the new larger turbine blades. To achieve this objective a custom rain erosion test facility and stressed sample holder were designed, manufactured and commissioned as part of this research to fully understand the problem. The results demonstrated that the applied bending stress increases the erosion rate and the trend does not follow the general accepted trends seen within the literature. This research therefore investigates the development of an alternative erosion-based model centred around the theta projection method which followed the trends observed in the experimental results more closely. Theta projection model and geographical rain data were utilised to develop site specific erosion predictions for the individual blade size and geometry which determines the impact velocity of the droplet and the stress within the material. Future developments are aimed at further expanding the research and providing knowledge to optimise turbine blade maintenance and thus efficiency.
Advisor / supervisor
  • Stack, M. M. (Margaret M.)
Resource Type
DOI
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