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...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.展开更多
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 integr...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.展开更多
基金co-supported by the National Natural Science Foundation of China (No. 51275138)the Science Foundation of Heilongjiang Provincial Department of Education (No. 12531109)+1 种基金the funding of Hong Kong Scholars Programs (Nos. XJ2015002 and G-YZ90)China’s Postdoctoral Science Funding (No. 2015M580037)
文摘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.
基金Projects(51275138,51475025)supported by the National Natural Science Foundation of ChinaProject(12531109)supported by the Science Foundation of Heilongjiang Provincial Department of Education,China+1 种基金Projects(XJ2015002,G-YZ90)supported by Hong Kong Scholars Program,ChinaProject(2015M580037)supported by Postdoctoral Science Foundation of China
文摘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.
