基于径向基神经网络的叶轮轴面投影图优化.docx

基于径向基神经网络的叶轮轴面投影图优化
摘要
本文研究了基于径向基神经网络的叶轮轴面投影图优化问题。传统的叶轮设计需要大量的试验和经验来确定合适的叶片形状，这种设计方式效率低下且存在许多不确定性。为了解决这个问题，本文提出了一种基于径向基神经网络的优化方法，旨在找到最佳的叶片形状，使得叶轮具有更高的效率和更好的性能。通过实验验证，本文所提出的方法具有较高的精度和可靠性，可以为叶轮设计和优化提供有力的支持和指导。
关键词：径向基神经网络，叶轮设计，投影图优化，精度，可靠性。
Introduction
Thedesignofimpellerinpumpsandturbinesisacomplexandchallengingtask,whichrequiresadeepunderstandingoffluiddynamicsandmechanicalengineering.Traditionally,impellerdesigninvolvesalargenumberofexperimentsandtrial-and-errorprocesses,whicharetime-consumingandsomewhatuncertain.Therefore,amoreefficientandreliableapproachisneededtooptimizetheimpellerbladeshapeandimprovetheimpeller'sperformance.
Inrecentyears,neuralnetworkshaveemergedaspowerfultoolsforoptimization,modeling,andpredictioninvariousfields.Inparticular,radialbasisfunction(RBF)neuralnetworkshavebeenwidelyusedinengineeringandscienceduetotheirexcellentinterpolationandapproximationcapabilities.RBFneuralnetworksarebasicallyatypeofartificialneuralnetworkthatusesradialbasisfunctionsasactivationfunctions.Theyareparticularlyeffectiveinsolvingnonlinearproblemsandcanapproximateanycontinuousfunctionwitharbitraryprecision.
Inthispaper,weapplyRBFneuralnetworkstooptimizetheimpellerbladeshapeinpumpsandturbines,specifically,theaxialprojectionoftheimpellerbladeshape.Themainobjectiveistofindtheoptimalbladeshapethatmaximizestheimpeller'sefficiencyandperformance.TheproposedmethodisbasedonacombinationofRBFneuralnetworksandoptimizationalgorithms,includingthegeneticalgorithmandtheparticleswarmoptimizationalgorithm.
Methodology
ThemethodologyforimpellerbladeshapeoptimizationbasedonRBFneuralnetworkscanbedividedintothefollowingsteps:
Step1:DataCollection
Thefirststepistocollectthenecessarydataforimpellerbladeshapeoptimization,includingthegeometricparametersoftheimpeller,theflowrate,andthehead.Theseparametersareusedtosimulatetheflowfieldaroundtheimpellerandgeneratetheaxialprojectionoftheimpellerbladeshape.
Step2:RBFNeuralNetworkTraining
ThesecondstepistotraintheRBFneuralnetworkusingthecollectedda