Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions
AU - Pryor, S.C.
AU - Barthelmie, R.J.
AU - Coburn, Jacob J
AU - zhou, xin
AU - Rodgers, Marianne
AU - Norton, Heather
AU - Campobasso, Sergio
AU - Mendez-Lopez, Beatriz
AU - Bay Hassager, Charlotte
AU - Mishnaevsky, Jr., Leon
PY - 2024/12/13
Y1 - 2024/12/13
N2 - 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.
AB - 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.
KW - wind turbine blades
KW - expert judgement
KW - Leading edge erosion
KW - machine learning
KW - PIRT
KW - Wind turbine
KW - meteorology
KW - coatings
KW - turbine control
U2 - 10.3390/en17246285
DO - 10.3390/en17246285
M3 - Journal article
VL - 17
JO - Energies
JF - Energies
SN - 1996-1073
IS - 24
M1 - 6285
ER -