Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions
Sara C. Pryor (sp2279@cornell.edu),
Rebecca J. Barthelmie,
Jacob J. Coburn,
Xin Zhou,
Marianne Rodgers,
Heather Norton,
M. Sergio Campobasso,
Beatriz Méndez López,
Charlotte Bay Hasager and
Leon Mishnaevsky
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Sara C. Pryor: Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
Rebecca J. Barthelmie: Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
Jacob J. Coburn: Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
Xin Zhou: Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
Marianne Rodgers: Wind Energy Institute of Canada, Tignish, PE C0B 2B0, Canada
Heather Norton: Wind Energy Institute of Canada, Tignish, PE C0B 2B0, Canada
M. Sergio Campobasso: School of Engineering, University of Lancaster, Lancaster LA1 4YW, UK
Beatriz Méndez López: National Renewable Energy Center (CENER), 31621 Sarriguren, Spain
Charlotte Bay Hasager: Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Leon Mishnaevsky: Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Energies, 2024, vol. 17, issue 24, 1-29
Abstract:
An enhanced understanding of the mechanisms responsible for wind turbine blade leading-edge erosion (LEE) and advancing technology readiness level (TRL) solutions for monitoring its environmental drivers, reducing LEE, detecting LEE evolution, and mitigating its impact on power production are a high priority for all wind farm owners/operators and wind turbine manufacturers. Identifying and implementing solutions has the potential to continue historical trends toward lower Levelized Cost of Energy (LCoE) from wind turbines by reducing both energy yield losses and operations and maintenance costs associated with LEE. Here, we present results from the first Phenomena Identification and Ranking Tables (PIRT) assessment for wind turbine blade LEE. We document the LEE-relevant phenomena/processes that are deemed by this expert judgment assessment tool to be the highest priorities for research investment within four themes: atmospheric drivers, damage detection and quantification, material response, and aerodynamic implications. The highest priority issues, in terms of importance to LEE but where expert judgment indicates that there is a lack of fundamental knowledge, and/or implementation in measurement, and modeling is incomplete include the accurate quantification of hydrometeor size distribution (HSD) and phase, the translation of water impingement to material loss/stress, the representation of operating conditions within rain erosion testers, the quantification of damage and surface roughness progression through time, and the aerodynamic losses as a function of damage morphology. We discuss and summarize examples of research endeavors that are currently being undertaken and/or could be initiated to reduce uncertainty in the identified high-priority research areas and thus enhance the TRLs of solutions to mitigate/reduce LEE.
Keywords: blades; expert judgment; LEE; machine learning; PIRT; TRL; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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