Fast prediction of rain erosion in wind turbine blades using a data-based computational tool
作者机构：Centre Internacional de Mètodes Numèrics en Enginyeria(CIMNE)Edifici C1 Campus Nord UPC C/Gran CapitàS/NBarcelonaSpain
出 版 物：《Journal of Hydrodynamics》 (水动力学研究与进展B辑（英文版）)
年 卷 期：2024年第36卷第3期
页 面：504-518页
核心收录：
学科分类：080801[工学-电机与电器] 0808[工学-电气工程] 08[工学]
基 金：supported by the CERCA programme of the Generalitat de Catalunya,and the Spanish Ministry of Economy and Competitiveness through the“Severo Ochoa Programme for Centres of Excellence in Research and Development”(Grant No.CEX2018-000797-S) Also,the authors acknowledge MCIN/AEI and FEDER Una manera de hacer Europa for funding this work via(Grant No.PID2021-122676NB-I00)
主 题：Wind turbine blades leading edge erosion fatigue Pseudo-Direct Numerical Simulation(P-DNS) machine learning eblader
摘 要：Wind turbines(WTs)face a high risk of failure due to environmental factors like erosion,particularly in high-precipitation areas and offshore *** 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 *** 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 *** 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 *** this information,a fatigue-based model estimates the remaining lifetime and erosion damage for both homogeneous and coating-substrate blade *** 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 *** 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 ***,the method is applied to predict the remaining lifetime and erosion damage to the blade sections of a reference *** evaluate the reliability of the tool,several site locations(offshore,coastal,and inland),the coating material and the coating thickness of the blade are *** few minutes we are able to estimate erosion after many years of *** results are in good agreement with field observations,showing the promise of the new rain erosion prediction approach.
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