Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the random...Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability assessment.First,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random excitations.Then,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic systems.The probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,respectively.Furthermore,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of system.Finally,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other methods.It is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue problems.The rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.展开更多
Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining ...Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining subsidence using D-InSAR technique and probability integral method. The details of the algorithm are as follows：the control points set, containing correct phase unwrapping points on the subsidence basin edge generated by D-InSAR and several observation points （near the maximum subsidence and inflection points）, was established at first; genetic algorithm （GA） was then used to optimize the parameters of probability integral method; at last, the surface subsidence was deduced according to the optimum parameters. The results of the experiment in Huaibei mining area, China, show that the presented method can generate the correct mining subsidence basin with a few surface observations, and the relative error of maximum subsidence point is about 8.3%, which is much better than that of conventional D-InSAR （relative error is 68.0%）.展开更多
In order to study the law of mining subsidence and ground movement, to provide the basis of coal mining under building, railway and water, we used the probability integration method to make comprehensive evaluation of...In order to study the law of mining subsidence and ground movement, to provide the basis of coal mining under building, railway and water, we used the probability integration method to make comprehensive evaluation of ground stability. Take Yingcheng Coal Mine of Jiutai as an example. Mining-induced movement and horizontal movement are analyzed on the basis of the measurement data. The resuhs of prediction can pro- vide reference and basis for prevention of coal mining subsidence and future restoration and treatment.展开更多
In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive ...In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.展开更多
Based on the hazard development mechanism, a water solution area is closely related to the supporting effect of pressure-bearing water, the relaxing and collapsing effect of orebody interlayer, the collapsing effect o...Based on the hazard development mechanism, a water solution area is closely related to the supporting effect of pressure-bearing water, the relaxing and collapsing effect of orebody interlayer, the collapsing effect of thawless material in orebody, filling effect caused by cubical expansibility of hydrate crystallization and uplifting effect of hard rock layer over cranny belt. The movement and deformation of ground surface caused by underground water solution mining is believed to be much weaker than that caused by well lane mining, which can be predicted by the stochastic medium theory method. On the basis of analysis on the engineering practice of water solution mining, its corresponding parameters can be obtained from the in-site data of the belt water and sand filling mining in engineering analog approach.展开更多
This paper proposes a hybrid algorithm based on the physics-informed kernel function neural networks(PIKFNNs)and the direct probability integral method(DPIM)for calculating the probability density function of stochast...This paper proposes a hybrid algorithm based on the physics-informed kernel function neural networks(PIKFNNs)and the direct probability integral method(DPIM)for calculating the probability density function of stochastic responses for structures in the deep marine environment.The underwater acoustic information is predicted utilizing the PIKFNNs,which integrate prior physical information.Subsequently,a novel uncertainty quantification analysis method,the DPIM,is introduced to establish a stochastic response analysis model of underwater acoustic propagation.The effects of random load,variable sound speed,fluctuating ocean density,and random material properties of shell on the underwater stochastic sound pressure are numerically analyzed,providing a probabilistic insight for assessing the mechanical behavior of structures in the deep marine environment.展开更多
基金supports of the National Natural Science Foundation of China(Nos.12032008,12102080)the Fundamental Research Funds for the Central Universities,China(No.DUT23RC(3)038)are much appreciated。
文摘Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability assessment.First,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random excitations.Then,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic systems.The probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,respectively.Furthermore,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of system.Finally,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other methods.It is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue problems.The rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.
基金Project (BK20130174) supported by the Basic Research Project of Jiangsu Province (Natural Science Foundation) Project (1101109C) supported by Jiangsu Planned Projects for Postdoctoral Research Funds,China+1 种基金Project (201325) supported by the Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping,ChinaProject (SZBF2011-6-B35) supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining subsidence using D-InSAR technique and probability integral method. The details of the algorithm are as follows：the control points set, containing correct phase unwrapping points on the subsidence basin edge generated by D-InSAR and several observation points （near the maximum subsidence and inflection points）, was established at first; genetic algorithm （GA） was then used to optimize the parameters of probability integral method; at last, the surface subsidence was deduced according to the optimum parameters. The results of the experiment in Huaibei mining area, China, show that the presented method can generate the correct mining subsidence basin with a few surface observations, and the relative error of maximum subsidence point is about 8.3%, which is much better than that of conventional D-InSAR （relative error is 68.0%）.
文摘In order to study the law of mining subsidence and ground movement, to provide the basis of coal mining under building, railway and water, we used the probability integration method to make comprehensive evaluation of ground stability. Take Yingcheng Coal Mine of Jiutai as an example. Mining-induced movement and horizontal movement are analyzed on the basis of the measurement data. The resuhs of prediction can pro- vide reference and basis for prevention of coal mining subsidence and future restoration and treatment.
基金the Research and Innovation Program for College and University Graduate Students in Jiangsu Province (No.CX10B_141Z)the National Natural Science Foundation of China (No.41071273) for support of this project
文摘In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.
基金Project(40404001) supported by the National Natural Science Foundation of China
文摘Based on the hazard development mechanism, a water solution area is closely related to the supporting effect of pressure-bearing water, the relaxing and collapsing effect of orebody interlayer, the collapsing effect of thawless material in orebody, filling effect caused by cubical expansibility of hydrate crystallization and uplifting effect of hard rock layer over cranny belt. The movement and deformation of ground surface caused by underground water solution mining is believed to be much weaker than that caused by well lane mining, which can be predicted by the stochastic medium theory method. On the basis of analysis on the engineering practice of water solution mining, its corresponding parameters can be obtained from the in-site data of the belt water and sand filling mining in engineering analog approach.
基金the National Natural Science Foundation of China,Grant Number:12372196,12302258,52325803,U22A20229,12402238State Key Laboratory of Ocean Engineering(Shanghai Jiao Tong University),Grant Number:GKZD010089+2 种基金the Six Talent Peaks Project in Jiangsu Province of China,Grant Number:2019-KTHY-009Jiangsu Funding Program for Excellent Postdoctoral Talent,Grant Number:2023ZB506Postdoctoral Fellowship Program of CPSF,Grant Number:GZC20230667。
文摘This paper proposes a hybrid algorithm based on the physics-informed kernel function neural networks(PIKFNNs)and the direct probability integral method(DPIM)for calculating the probability density function of stochastic responses for structures in the deep marine environment.The underwater acoustic information is predicted utilizing the PIKFNNs,which integrate prior physical information.Subsequently,a novel uncertainty quantification analysis method,the DPIM,is introduced to establish a stochastic response analysis model of underwater acoustic propagation.The effects of random load,variable sound speed,fluctuating ocean density,and random material properties of shell on the underwater stochastic sound pressure are numerically analyzed,providing a probabilistic insight for assessing the mechanical behavior of structures in the deep marine environment.
