首页|Fast prediction of rain erosion in wind turbine blades using a data-based computational tool

Fast prediction of rain erosion in wind turbine blades using a data-based computational tool

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Wind turbines(WTs)face a high risk of failure due to environmental factors like erosion,particularly in high-precipitation areas and offshore scenarios.In this paper we introduce a novel computational tool for the fast prediction of rain erosion damage on WT blades that is useful in operation and maintenance decision making tasks.The approach is as follows:Pseudo-Direct Numerical Simulation(P-DNS)simulations of the droplet-laden flow around the blade section profile are employed to build a high-fidelity data set of impact statistics for potential operating conditions.Using this database as training data,a machine learning-based surrogate model provides the feature of the impact pattern over the 2-D section for given wind and rain conditions.With this information,a fatigue-based model estimates the remaining lifetime and erosion damage for both homogeneous and coating-substrate blade materials.This prediction is done by quantifying the accumulated droplet impact energy and evaluating operative conditions over time periods for which the weather at the installation site is known.In this work,we describe the modules that compose the prediction method,namely the database creation,the training of the surrogate model and their coupling to build the prediction tool.Then,the method is applied to predict the remaining lifetime and erosion damage to the blade sections of a reference WT.To evaluate the reliability of the tool,several site locations(offshore,coastal,and inland),the coating material and the coating thickness of the blade are investigated.In few minutes we are able to estimate erosion after many years of operation.The results are in good agreement with field observations,showing the promise of the new rain erosion prediction approach.

Wind turbine bladesleading edge erosionfatiguePseudo-Direct Numerical Simulation(P-DNS)machine learningeblader

Juan M.Gimenez、Sergio R.Idelsohn、Eugenio O?ate

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Centre Internacional de Mètodes Numèrics en Enginyeria(CIMNE),Edifici C1 Campus Nord UPC C/Gran Capità,S/N,Barcelona,Spain

CERCA programme of the Generalitat de CatalunyaSpanish Ministry of Economy and CompetitivenessMCIN/AEI and FEDER Una manera de hacer Europa

CEX2018-000797-SPID2021-122676NB-I00

2024

10.1007/s42241-024-0044-4

水动力学研究与进展B辑
中国船舶科学研究中心

水动力学研究与进展B辑

CSTPCDEI
影响因子：0.596
ISSN：1001-6058
年,卷(期)：2024.36(3)