Probabilistic Fatigue/Creep Optimization of Turbine Bladed Disk with Fuzzy Multi-Extremum Response Surface Method

• Published in: Materials Vol. 12; no. 20; p. 3367
• Main Authors: Zhang, Chun-Yi, Yuan, Zhe-Shan, Wang, Ze, Fei, Cheng-Wei, Lu, Cheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 15.10.2019; MDPI
• Subjects: Accuracy; Boundary conditions; Collaboration; Component reliability; Computational efficiency; Computer simulation; Computing time; Coupling; Creep (materials); Damage; Design optimization; Design parameters; Disks; Efficiency; Failure modes; Fatigue failure; Mathematical models; Methods; Model accuracy; Neural networks; Optimization; Response surface methodology; Turbines
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma12203367

To effectively perform the probabilistic fatigue/creep coupling optimization of a turbine bladed disk, this paper develops the fuzzy multi-extremum response surface method (FMERSM) for the comprehensive probabilistic optimization of multi-failure/multi-component structures, which absorbs the ideas of the extremum response surface method, hierarchical strategy, and fuzzy theory. We studied the approaches of FMERSM modeling and fatigue/creep damage evaluation of turbine bladed disks, and gave the procedure for the fuzzy probabilistic fatigue/creep optimization of a multi-component structure with FMERSM. The probabilistic fatigue/creep coupling optimization of turbine bladed disks was implemented by regarding the rotor speed, temperature, and density as optimization parameters; the creep stress, creep strain, fatigue damage, and creep damage as optimization objectives; and the reliability and GH4133B fatigue/creep damages as constraint functions. The results show that gas temperature T and rotor speed ω are the key parameters that should be controlled in bladed disk optimization, and respectively reduce by 85 K and 113 rad/s after optimization, which is promising to extend bladed disk life and decrease failure damages. The simulation results show that this method has a higher modeling accuracy and computational efficiency than the Monte Carlo method (MCM). The efforts of this study provide a new useful method for overall probabilistic multi-failure optimization and enrich mechanical reliability theory.