TY - JOUR

T1 - Reliability-based combined high and low cycle fatigue analysis of turbine blade using adaptive least squares support vector machines

AU - Ma, Juan

AU - Yue, Peng

AU - Du, Wenyi

AU - Dai, Changping

AU - Wriggers, Peter

N1 - Funding Information: The work was supported by Natural Science Foundation of China (Grant No. 11572233) and Pre-Research Foundation (Grant No. 61400020106) as well as the Fundamental Research Funds for the Central Universities.

PY - 2022/8/10

Y1 - 2022/8/10

N2 - In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

AB - In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

KW - active learning strategy

KW - combined high and low cycle fatigue

KW - least squares support vector machines

KW - reliability assessment

KW - turbine blade

UR - http://www.scopus.com/inward/record.url?scp=85140022298&partnerID=8YFLogxK

U2 - 10.12989/sem.2022.83.3.293

DO - 10.12989/sem.2022.83.3.293

M3 - Article

AN - SCOPUS:85140022298

VL - 83

SP - 293

EP - 304

JO - Structural Engineering and Mechanics

JF - Structural Engineering and Mechanics

SN - 1225-4568

IS - 3

ER -