Abstract:Abstract.To improve the precision of reliability analysis on turbine blades, the fuzzy response surface method of reliability analysis is proposed by considering the fuzziness of the input variables and the vagueness of the limit state variables. Initially, the fuzzy basic variables were converted into equivalent random variables according to the method of equivalent transformation. Additionally, the mathematic model of the fuzzy response surface for structural reliability analysis was established, based on th… Show more
“…16,17 The dynamic comprehensive reliability analysis of multi-failure modes is obtained by simulating multi-extremum response surface models by using linkage Monte Carlo (MC) method. 18,19 Figure 2.Flow chart of probabilistic analysis based on MERSM.Figure 3.Sample set generated with central composite design.Note: { x i } i = 1,2 is the i th random input variable; the round dot denotes computational point; the square dot are samples of random input variables; {#x000B5; i } i = 1,2 and {#963; i } i = 1,2 are respectively the mean and standard deviation of the ith random input variable; f is the interpolation coefficient.…”
This study develops multi-extremum response surface method (MERSM) for the probabilistic analysis of aeroengine turbine blisk with the coexistence of many failure modes by considering two-way fluid-thermal-solid coupling under complex working condition. The mathematical model of MERSM was established with respect to the random input variables of inlet temperature, inlet velocity, density of material, and rotor speed, and the output responses of the maximum values of deformation, stress, and strain. The comprehensive probabilistic analysis of turbine blisk was completed based on MERSM. The results demonstrate that the comprehensive reliability degree of turbine blisk is 0.9944 when the allowable deformation, stress, and strain are 2.6 × 10−3 m, 1.26 × 109 Pa, and 6.75 × 10−3 m/m, respectively. The main factors influencing the comprehensive reliability degree of turbine blisk are rotor speed and gas temperature. The secondary factors are inlet velocity and density of material. As revealed from the comparison of methods, MERSM improves the computational speed and efficiency with the guarantee of accuracy. The efforts of this paper provide a promising approach for the nonlinear transient reliability analysis of complex structures with multi-failure modes and two-way fluid-thermal-solid coupling.
“…16,17 The dynamic comprehensive reliability analysis of multi-failure modes is obtained by simulating multi-extremum response surface models by using linkage Monte Carlo (MC) method. 18,19 Figure 2.Flow chart of probabilistic analysis based on MERSM.Figure 3.Sample set generated with central composite design.Note: { x i } i = 1,2 is the i th random input variable; the round dot denotes computational point; the square dot are samples of random input variables; {#x000B5; i } i = 1,2 and {#963; i } i = 1,2 are respectively the mean and standard deviation of the ith random input variable; f is the interpolation coefficient.…”
This study develops multi-extremum response surface method (MERSM) for the probabilistic analysis of aeroengine turbine blisk with the coexistence of many failure modes by considering two-way fluid-thermal-solid coupling under complex working condition. The mathematical model of MERSM was established with respect to the random input variables of inlet temperature, inlet velocity, density of material, and rotor speed, and the output responses of the maximum values of deformation, stress, and strain. The comprehensive probabilistic analysis of turbine blisk was completed based on MERSM. The results demonstrate that the comprehensive reliability degree of turbine blisk is 0.9944 when the allowable deformation, stress, and strain are 2.6 × 10−3 m, 1.26 × 109 Pa, and 6.75 × 10−3 m/m, respectively. The main factors influencing the comprehensive reliability degree of turbine blisk are rotor speed and gas temperature. The secondary factors are inlet velocity and density of material. As revealed from the comparison of methods, MERSM improves the computational speed and efficiency with the guarantee of accuracy. The efforts of this paper provide a promising approach for the nonlinear transient reliability analysis of complex structures with multi-failure modes and two-way fluid-thermal-solid coupling.
“…The concept of the FS has also received considerable attention from system reliability analysis researchers such as Mahapatra and Roy ( 2012 ), Pan et al. ( 2015 ), Pramanik et al. ( 2019 ) and El-Damcese et al.…”
Section: Introductionmentioning
confidence: 99%
“…The estimation methods for reliability characteristics must be modified based on the fuzzy lifetimes to attain a more realistic analysis and exploit the uncertainty or imprecision in the data. The concept of the FS has also received considerable attention from system reliability analysis researchers such as Mahapatra and Roy (2012), Pan et al (2015), Pramanik et al (2019) and El-Damcese et al (2014).…”
In this paper, the two-parameter Pareto lifetime distribution is considered with vague shape and scale parameters, where parameters are set as generalized intuitionistic fuzzy numbers. A new L-R type intuitionistic fuzzy number is introduced, and cuts of the new fuzzy set are provided. The generalized intuitionistic fuzzy reliability characteristics such as reliability, conditional reliability, hazard rate and mean time to failure functions are defined, along with the special case of the two-parameter Pareto generalized intuitionistic fuzzy reliability analysis. Furthermore, the series and parallel system reliability are evaluated by the generalized intuitionistic fuzzy sets. Finally, for certain cases of the fuzzy shape and scale parameters and cut set values, the generalized intuitionistic fuzzy reliability characteristics are provided and compared through several illustrative plots.
“…Numerical simulation-based probability analysis approaches are the most applied for fatigue life prediction and reliability evaluation. The probability analysis methods mainly include first-order reliability method (FORM) (Cornell 1970), second-order reliability method (SORM) (Fu 2018), response surface method (RSM) (Pan 2015), Monte-Carlo method (MCM) (Alessandri 2018) and distributed collaborative response surface method (DCRSM) (Gao 2015(Gao , 2016(Gao , 2018(Gao and 2019. However, the primary differences among the approaches are numerical accuracy and computation efficiency.…”
Low-cycle fatigue (LCF) of turbine blades involves several disciplines with multi-uncertain factors and is a high-nonlinear complex problem. To improve prediction accuracy and computational efficiency, this paper develops DC-LSSVR approach, integrating least squares support vector regression (LSSVR) into the distributed collaborative (DC) strategy, for the LCF life prediction and reliability evaluation of turbine blades. Considering the influence of the uncertain factors, i.e., design sizes, applied loads and material properties, the reliability assessment framework is constructed. Through the integration of the DC-LSSVR reliability method with the theoretical models, including the Smith-Watson-Topper (SWT) mean stress correction and linear cumulative damage (LCD) rule, the LCF life is predicted and reliability evaluation is completed. Finally, the DC-LSSVR is proved to be a promising approach for the reliability assessment of complex structures.
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