Probabilistic-based combined high and low cycle fatigue assessment for turbine blades using a substructure-based kriging surrogate model
| Autor: | Hai-Feng Gao, Enrico Zio, Chengwei Fei, Anjenq Wang, Guang-Chen Bai |
|---|---|
| Přispěvatelé: | Centre de recherche sur les Risques et les Crises (CRC), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Turbine blade Monte Carlo method Aerospace Engineering 02 engineering and technology 01 natural sciences Substructure method 010305 fluids & plasmas law.invention [SPI]Engineering Sciences [physics] 020901 industrial engineering & automation law Kriging 0103 physical sciences Distributed collaborative strategy Probabilistic analysis of algorithms [SHS.GEST-RISQ]Humanities and Social Sciences/domain_shs.gest-risq Reliability (statistics) ComputingMilieux_MISCELLANEOUS Mathematics business.industry Probabilistic logic Structural engineering Substructure Low-cycle fatigue Combined cycle fatigue business |
| Zdroj: | Aerospace Science and Technology Aerospace Science and Technology, Elsevier, 2020, 104, pp.105957. ⟨10.1016/j.ast.2020.105957⟩ |
| ISSN: | 1270-9638 |
| Popis: | Fatigue assessment for gas turbine blades under combined high and low fatigue cyclic loading is very difficult. In this paper, we propose a probabilistic approach based on a substructure-based kriging surrogate model (SKM) that embeds substructure simulation into a kriging surrogate model (KSM). A distributed collaborative SKM (DCSKM) approach is, then, proposed based on the combination of a distributed collaborative strategy (DC) with SKM. Low-cycle fatigue (LCF) life and high-cycle fatigue (HCF) life of turbine blades are predicted with DCSKM. Based on the simulation data, the combined high and low cycle fatigue (CCF) life and damage assessment are performed with respect to the linear cumulative damage by DCSKM. Further, the relationships between the number of applied cycles and CCF reliability R with survival probabilities P = 0.5 , 0.9 and 0.95, for a confidence level of 0.95, are fitted. Finally, the DCSKM is compared with the Monte Carlo method (MCM) and response surface method (RSM). It is found that (1) the CCF reliability of turbine blades decreases with increasing survival probability for the same applied cycle and decreases with increasing applied cycles under the same survival probability; (2) LCF holds a significant influence on the CCF damage of gas turbine blades; (3) the proposed DCSKM is found to be an available probabilistic analysis approach for the CCF assessment of turbine blades. |
| Databáze: | OpenAIRE |
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