For unacceptable computational efficiency and accuracy on the probabilistic analysis of multi-component system with multi-failure modes,this paper proposed multi-extremum response surface method(MERSM).MERSM model was established based on quadratic polynomial function by taking extremum response surface model as the sub-model of multi-response surface method.The dynamic probabilistic analysis of an aeroengine turbine blisk with two components,and their reliability of deformation and stress failures was obtained,based on thermal-structural coupling technique,by considering the nonlinearity of material parameters and the transients of gas flow,gas temperature and rotational speed.The results show that the comprehensive reliability of structure is 0.9904 when the allowable deformations and stresses of blade and disk are 4.78×10–3 m and 1.41×109 Pa,and 1.64×10–3 m and 1.04×109 Pa,respectively.Besides,gas temperature and rotating speed severely influence the comprehensive reliability of system.Through the comparison of methods,it is shown that the MERSM holds higher computational precision and speed in the probabilistic analysis of turbine blisk,and MERSM computational precision satisfies the requirement of engineering design.The efforts of this study address the difficulties on transients and multiple models coupling for the dynamic probabilistic analysis of multi-component system with multi-failure modes.
To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.
To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method (AMRSM) was proposed for multi-failure mode sensitivity analysis for reliability. The mathematical model of AMRSM was established and the basic principle of multi-failure mode sensitivity analysis for reliability with AMRSM was given. The important parameters of turbine blisk failures are obtained by the multi-failure mode sensitivity analysis of turbine blisk. Through the reliability sensitivity analyses of multiple failure modes (deformation, stress and strain) with the proposed method considering fluid-thermal-solid interaction, it is shown that the comprehensive reliability of turbine blisk is 0.9931 when the allowable deformation, stress and strain are 3.7 x 10(-3) m, 1.0023 x 10(9) Pa and 1.05 x 10(-2) m/m, respectively; the main impact factors of turbine blisk failure are gas velocity, gas temperature and rotational speed. As demonstrated in the comparison of methods (Monte Carlo (MC) method, traditional response surface method (RSM), multiple response surface method (MRSM) and AMRSM), the proposed AMRSM improves computational efficiency with acceptable computational accuracy. The efforts of this study provide the AMRSM with high precision and efficiency for multi-failure mode reliability analysis, and offer a useful insight for the reliability optimization design of multi-failure mode structure. (C) 2016 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
