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首页> 外文期刊>Aerospace science and technology >Probabilistic-based combined high and low cycle fatigue assessment for turbine blades using a substructure-based kriging surrogate model
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Probabilistic-based combined high and low cycle fatigue assessment for turbine blades using a substructure-based kriging surrogate model

机译：基于子结构的Kriging代理模型的涡轮叶片基于概率的高低循环疲劳评估

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摘要

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. (C) 2020 Elsevier Masson SAS. All rights reserved.
机译：燃气涡轮机叶片的疲劳评估很高，低疲劳环状载荷非常困难。在本文中，我们提出了一种基于基于子结构的Kriging代理模型（SKM）的概率方法，该模型将子结构模拟嵌入到Kriging代理模型（KSM）中。然后，基于具有SKM的分布式协作策略（DC）的组合，提出了一种分布式协作SKM（DCSKM）方法。用DCSKM预测涡轮叶片的低循环疲劳（LCF）寿命和高循环疲劳（HCF）寿命。基于仿真数据，相对于DCSKM的线性累积损坏来执行组合的高和低循环疲劳（CCF）寿命和损伤评估。此外，安装了施加循环和CCF可靠性R之间的关系，适用于生存概率P = 0.5,0.9和0.95，置位水平为0.95。最后，将DCSKM与蒙特卡罗方法（MCM）和响应面法（RSM）进行比较。发现（1）（1）涡轮机叶片的CCF可靠性随着同一施加循环的增加概率而降低，并随着在相同的存活概率下增加施加循环而降低; （2）LCF对燃气轮机叶片的CCF损坏具有显着影响; （3）拟议的DCSKM被发现是涡轮机叶片的CCF评估的可用概率分析方法。 （c）2020 Elsevier Masson SAS。版权所有。

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