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The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample. • The correlation between creep-fatigue life (CFL) of metal hydride reactor (MHR) and design parameters is established. • Numerical models combining BPNN with ASME Code are available for assessment of CFL of MHR. • The sensitivity analysis is applied to measure the effect of design parameters on CFL of MHR. • Optimal design scheme of MHR is performed through an integrated design optimization method. [ABSTRACT FROM AUTHOR]

The creep-fatigue resistance of turbine rotor seriously affects the reliability performance and service lifetime of aircraft engine. Creep-fatigue reliability assessment is an effective measure to quantify the uncertain creep-fatigue damage and evaluate the creep-fatigue reliable life for turbine rotor. To improve the modeling accuracy and simulation efficiency of creep-fatigue reliability assessment, a multi-surrogate collaboration approach (MSCA) is proposed by absorbing the strengths of the proposed dynamic neural network surrogate (DNNS) into distributed collaborative strategy. The creep-fatigue reliability assessment of a typical turbine rotor is regarded as one case to estimate the presented MSCA with respect to the fluctuations of multi-physical variables and the variabilities of multi-model parameters. The assessment results reveal that the creep-fatigue reliable life of turbine rotor under reliability degree of 0.998 7 is 629 cycles, and the fatigue strength coefficient and holding creep time play a leading role on creep-fatigue reliable life since their effect probabilities of 27 % and 19 %, respectively. Comparison of various methods (direct Monte Carlo simulation, response surface, neural network surrogate, DNNS) shows that the presented MSCA holds high efficiency and accuracy in creep-fatigue reliability assessment of turbine rotor.

Abstract: The author and his colleagues have proposed in their recent works the creep-fatigue life prediction method based on the strain range partitioning concept for nonproportionally combined tension and torsion using new inelastic strain range parameters and have been studying their applicability to the materials subjected to nonproportional axial-torsional loading at elevated temperatures. In the present paper are summarized the results of in-phase and 90 deg out-of-phase axial-torsional straining tests conducted on thin-walled tubular specimens of SUS304, 316LC and 2.25Cr-1Mo steels. [Copyright &y& Elsevier]

Abstract: The creep-fatigue properties of a 308L steel weldment after different post-weld heat treatments (PWHT) are investigated. The microstructure and phase transformation of the as-welded and PWHT weldments are examined using scanning and transmission electron microscopy. Special attention is paid to the decomposition of δ-ferrite and the precipitation of σ-phase. Energy dispersive spectroscopy is also used for detecting the elements distribution. The results show that PWHT promotes the creep-fatigue resistance of the weldment. δ-Ferrite mainly transforms into secondary austenite and σ-phase. The decomposition ratio of δ-ferrite shows a tendency to decrease after high temperature PWHT. This can be attributed to the reduction of silicon segregation near the δ/γ interface. [Copyright &y& Elsevier]

In this study, using AISI 316 stainless steel, creep-fatigue tests were carried out under various test conditions (different total strain ranges and hold times) to verify the applicability of the artificial neural network method to creep-fatigue life prediction. Life prediction was also made by the modified Coffin-Manson method and the modified Ostegren method using 21 data points out of a total 27 experimental data points. The six verification data points were carefully chosen for the purpose of evaluating the predictability of each method. The predicted lives were compared with the experimental results and the following conclusions were obtained within the scope of this study. While the creep-fatigue life prediction by the modified Coffin-Manson method and the modified Ostegren method had average errors of 35.8% and 47.7% respectively, the artificial neural network method had only 15.6%. As a result, the artificial neural network method with the adaptive learning rate was found to be far more accurate and effective than any of the others. The validity of the artificial neural network method for life prediction checked with the six verification data points also proved to be very satisfactory. [ABSTRACT FROM AUTHOR]

To improve the modeling accuracy and simulation efficiency of probabilistic creep-fatigue life evaluation, a decomposed collaborative time-variant Kriging surrogate model (DCTKS) is proposed by absorbing the strengths of extremum selection technique and Kriging model into decomposed collaborative strategy. The probabilistic creep-fatigue life evaluation of a typical turbine disk is considered as one case to evaluate the proposed DCTKS method with respect to fluctuation of transient loads, nonlinearity of material properties and variability of models. In respect of this study, we find that the probabilistic creep-fatigue life of the turbine disk under the reliability degree of 0.998 7 is 946 cycles, and the transient fluctuating loads (body temperature and rotor speed) are the main factors of influencing creep-fatigue life. Through the comparison of methods (DCTKS, Monte Carlo simulation method, Kriging surrogate method, decomposed collaborative response surface method), the proposed DCTKS is demonstrated to possess the computational advantages in efficiency and accuracy for probabilistic creep-fatigue life evaluation. [ABSTRACT FROM AUTHOR]