风能和前缘侵蚀检测的数字孪生.pdf

1、New York Scientific Data Summit 2024Digital Twins for Wind Energy and Leading Edge Erosion DetectionSusan Minkoff1,Aidan Gettemy,John Zweck,and Todd Griffith2Department of Mathematical SciencesUniversity of Texas at DallasSpecial thanks to Elaine Spiller3and Ipsita Mishra4for their guidance.Septembe

2、r 16-17,20241Department of Applied Math,Computational Science Initiative,BNL2Department of Mechanical Engineering,University of Texas at Dallas3Department of Mathematical and Statistical Sciences,Marquette University4PhD Candidate;Department of Mechanical Engineering,University of Texas at Dallas1/1

3、6Wind Energy:Opportunities and ChallengesUS has 73,000+turbines,which generate over 150 GW.Maintenance costs are a largeshare of the energy price.5Blade faults include:CracksSkin/adhesive debondingDelaminationFiber breakageEdge erosionImage Source:5J.Tautz-Weinert S.J.Watson,Using SCADA data for win

4、d turbine condition monitoring,IET Renewable Power Gen.,11(2017),pp.382-3942/16Remote MonitoringImage Source:Image Source:Early fault detection via remote sensor data improves maintenance andsafety.3/16Leading-Edge Erosionhi Decreases lift/dragratio.7 Difficult to detect.Scarce data.Severe erosionbu

5、ild up.87A.Sareen et al.,Effects of leading edge erosion on wind turbine blade performance,Wind Energy,17(2014),pp.1531-15428A.Shankar Verma et al.,A probabilistic long-term framework for site-specific erosion analysis of wind turbine blades,WindEnergy,24(2021),pp.1315-13364/16SimulationDefinition:O

6、penFAST,from the National Renewable Energy Laboratory(NREL),couples multi-physics modules to model turbine responses torealistic wind/weather simulations.9Inflow WindAeroDynElastoDynServoDynInflowWind:wind conditionsAeroDyn:aerodynamics:lift,dragElastoDyn:structural motionsServoDyn:controlinputs/out