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

verfasst von: Juan Ma, Peng Yue, Wenyi Du, Changping Dai, Peter Wriggers
Abstract: 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.
Organisationseinheit(en): Institut für Kontinuumsmechanik
Externe Organisation(en): Xihua University
Xidian University
Typ: Artikel
Journal: Structural Engineering and Mechanics
Band: 83
Seiten: 293-304
Anzahl der Seiten: 12
ISSN: 1225-4568
Publikationsdatum: 10.08.2022
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Tief- und Ingenieurbau, Bauwesen, Werkstoffmechanik, Maschinenbau
Elektronische Version(en): https://doi.org/10.12989/sem.2022.83.3.293 (Zugang: Geschlossen)
: Details im Forschungsportal „Research@Leibniz University“

BibTeX

@article{4cb1180275c2460fa260ac610bd3bead,
title = "Reliability-based combined high and low cycle fatigue analysis of turbine blade using adaptive least squares support vector machines",
abstract = "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.",
keywords = "active learning strategy, combined high and low cycle fatigue, least squares support vector machines, reliability assessment, turbine blade",
author = "Juan Ma and Peng Yue and Wenyi Du and Changping Dai and Peter Wriggers",
note = "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. ",
year = "2022",
month = aug,
day = "10",
doi = "10.12989/sem.2022.83.3.293",
language = "English",
volume = "83",
pages = "293--304",
journal = "Structural Engineering and Mechanics",
issn = "1225-4568",
publisher = "Techno Press",
number = "3",
}