Compliant parts are widely applied to aircraft structures.Due to the ease of deformation of compliant parts in assembly,the prediction of assembly variation is especially important for assembly quality control.A dimen...Compliant parts are widely applied to aircraft structures.Due to the ease of deformation of compliant parts in assembly,the prediction of assembly variation is especially important for assembly quality control.A dimensional variation model considering the clamping force change in assembly is proposed based on the method of influence coefficient(MIC).First,the assembly process is decomposed into several steps including positioning,clamping,joining,and spring-back.Then,the force-displacement relationship is formulated according to the varied force conditions on the parts in each assembly step.Finally,two examples are illustrated to validate the proposed assembly variation model.The results show the impact of clamping force change is significant on the assembly variation,and the proposed model can predict the assembly variation more accurately than the referred method without clamping force correction at the over-constrained locating points of fixture.展开更多
In this paper, we found the numerical solution of three-dimensional coupled Burgers’ Equations by using more efficient methods: Laplace Adomian decomposition method, Laplace transform homotopy perturbation method, va...In this paper, we found the numerical solution of three-dimensional coupled Burgers’ Equations by using more efficient methods: Laplace Adomian decomposition method, Laplace transform homotopy perturbation method, variational iteration method, variational iteration decomposition method and variational iteration homotopy perturbation method. Example is examined to validate the efficiency and accuracy of these methods and they reduce the size of computation without the restrictive assumption to handle nonlinear terms and it gives the solutions rapidly.展开更多
Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77...Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.展开更多
At present,the leakage rate of the water distribution network in China is still high,and the waste of water resources caused by water distribution network leakage is quite serious every year.Therefore,the location of ...At present,the leakage rate of the water distribution network in China is still high,and the waste of water resources caused by water distribution network leakage is quite serious every year.Therefore,the location of pipeline leakage is of great significance for saving water resources and reducing economic losses.Acoustic emission technology is the most widely used pipeline leak location technology.The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters,ignoring periodic noise and components unrelated to pipeline leakage.Aiming at the above problems,this paper proposes a leak location method for water supply pipelines based on a multivariate variational mode decomposition algorithm.This method combines the two parameters of the energy loss coefficient and the correlation coefficient between adjacent modes,and adaptively determines the decomposition mode number K according to the characteristics of the signal itself.According to the correlation coefficient,the effective component is selected to reconstruct the signal and the cross-correlation time delay is estimated to determine the location of the pipeline leakage point.The experimental results show that this method has higher accuracy than the cross-correlation method based on VMD and the cross-correlation method based on EMD,and the average relative positioning error is less than 2.2%.展开更多
Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the mari...Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.展开更多
基金funded by the National Natural Science Foundation of China ( No.51275236)
文摘Compliant parts are widely applied to aircraft structures.Due to the ease of deformation of compliant parts in assembly,the prediction of assembly variation is especially important for assembly quality control.A dimensional variation model considering the clamping force change in assembly is proposed based on the method of influence coefficient(MIC).First,the assembly process is decomposed into several steps including positioning,clamping,joining,and spring-back.Then,the force-displacement relationship is formulated according to the varied force conditions on the parts in each assembly step.Finally,two examples are illustrated to validate the proposed assembly variation model.The results show the impact of clamping force change is significant on the assembly variation,and the proposed model can predict the assembly variation more accurately than the referred method without clamping force correction at the over-constrained locating points of fixture.
文摘In this paper, we found the numerical solution of three-dimensional coupled Burgers’ Equations by using more efficient methods: Laplace Adomian decomposition method, Laplace transform homotopy perturbation method, variational iteration method, variational iteration decomposition method and variational iteration homotopy perturbation method. Example is examined to validate the efficiency and accuracy of these methods and they reduce the size of computation without the restrictive assumption to handle nonlinear terms and it gives the solutions rapidly.
基金by the National Natural Science Foundation of China(No.52174114)the State Key Laboratory of Hydroscience and Engineering of Tsinghua University(No.61010101218).
文摘Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.
基金supported by the three funds:Industry-University-research Project of Anhui Jianzhu University HYB20210116National Key Research and Development Project of China No.2017YFC0704100(entitled New Generation Intelligent Building Platform Techniques)Research Project of Anhui Jianzhu University jy2021-c-017(Project Name:Research and Application ofWater Distribution Network Leakage Detection System Based on DMA Partition).
文摘At present,the leakage rate of the water distribution network in China is still high,and the waste of water resources caused by water distribution network leakage is quite serious every year.Therefore,the location of pipeline leakage is of great significance for saving water resources and reducing economic losses.Acoustic emission technology is the most widely used pipeline leak location technology.The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters,ignoring periodic noise and components unrelated to pipeline leakage.Aiming at the above problems,this paper proposes a leak location method for water supply pipelines based on a multivariate variational mode decomposition algorithm.This method combines the two parameters of the energy loss coefficient and the correlation coefficient between adjacent modes,and adaptively determines the decomposition mode number K according to the characteristics of the signal itself.According to the correlation coefficient,the effective component is selected to reconstruct the signal and the cross-correlation time delay is estimated to determine the location of the pipeline leakage point.The experimental results show that this method has higher accuracy than the cross-correlation method based on VMD and the cross-correlation method based on EMD,and the average relative positioning error is less than 2.2%.
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC2806102)the National Natural Science Foundation of China(Grant Nos.52171287,52325107)+2 种基金High Tech Ship Research Project of Ministry of Industry and Information Technology(Grant Nos.2023GXB01-05-004-03,GXBZH2022-293)the Science Foundation for Distinguished Young Scholars of Shandong Province(Grant No.ZR2022JQ25)the Taishan Scholars Project(Grant No.tsqn201909063)。
文摘Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.
