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

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 o...

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Bibliographic Details
Published in:Materials 2019-10, Vol.12 (20), p.3367
Main Authors: Zhang, Chun-Yi, Yuan, Zhe-Shan, Wang, Ze, Fei, Cheng-Wei, Lu, Cheng
Format: Article
Language:English
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
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Summary: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.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma12203367