• Structural Engineering and Mechanics
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• 제83권3호
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• pp.293-304
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• 2022

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.

• 대한기계학회논문집A
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• 제34권7호
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• pp.911-917
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• 2010

본 연구에서는 열적-기계적 주기하중을 받고 있는 엔진 배기매니폴드에 대해서 열응력 해석방법과 열피로수명 예측과정을 제시하였다. 즉, 파손현상이 복잡한 배기시스템의 효율적인 유한요소 모델링 방법과 온도 의존성 재료의 시험결과를 이용한 해석 데이터 구성, 그리고 열사이클 하중에 대한 열응력 및 파손 예측방법을 디젤엔진의 배기매니폴드에 대해서 나타내었다. 일반적으로 배기매니폴드의 파손 취약부에서는 고온영역에서 큰 압축소성변형이 발생하고 냉각시에는 인장의 잔류응력이 나타난다. 따라서 이같은 응력과 변형률의 이력곡선으로부터 소성변형의 진폭 또는 소성에너지의 크기를 얻을 수 있으며 이를 통해서 피로수명을 예측할 수 있다.