A novel metamodeling approach for probabilistic LCF estimation of turbine disk

To improve the computing efficiency and accuracy of probabilistic low cycle fatigue (LCF) estimation for turbine disk, a distributed-coordinated neural network metamodel (DCNNM) is developed. By integrating the proposed neural network metamodel and distributed-coordinated strategy, the mathematical model of DCNNM is studied. The probabilistic LCF estimation theory is introduced in respect of the presented DCNNM. Moreover, the probabilistic LCF estimation for turbine disk is regarded as one case to evaluate the proposed method with respect to various randomness such as material variability, load variation and model randomness. We obtain the distributional traits, reliability degree and sensitivity degree of LCF failure cycle, which provides an effective guidance for the turbine disk life control. By comparing the direct Monte Carlo simulation, support vector regression (SVR), neural network metamodel (NNM), distributed coordinated SVR (DCSVR) and DCNNM, we observe that the proposed DCNNM approach possesses high efficiency and accuracy for probabilistic LCF estimation of turbine disk. The present effort offers a useful insight for estimating LCF failure from a probabilistic perspective.

Automated summary

A distributed-coordinated neural network metamodel (DCNNM) is developed to enhance the computational efficiency and accuracy of probabilistic low cycle fatigue (LCF) estimation for turbine disk. By analyzing the probabilistic LCF estimation theory and evaluating the method under various randomness factors, the study provides useful insight for estimating LCF failure from a probabilistic perspective. The proposed DCNNM approach shows high efficiency and accuracy compared to other methods like direct Monte Carlo simulation and support vector regression.