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
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.
