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An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks

机译：一种综合可靠性方法，具有改进的涡轮机磁盘低周疲劳损伤预测的重要性取样

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摘要

The objective of this paper is to develop an effective reliability approach to improve the numerical accuracy and computational efficiency of the low-cycle fatigue (LCF) damage prediction for turbine disks. The approach is called as distributed collaborative (DC) probabilistic substructure (PS)-based moving improved importance-sampling least-squares (MIIL), DC-PS-MIIL. In this approach, the MIIL model is firstly developed by combining an improved importance sampling (IIS) with moving least squares (MLS), to reduce the computation burden and improve the precision of surrogate model; then, the PS-MIIL comes up by integrating probabilistic substructure (PS) method into MIIL, to address the interface forces of turbine disks; finally, DC-PS-MIIL is proposed by incorporating distributed collaborative strategy with PS-MIIL. The comprehensive analysis procedure with DC-PS-MIIL is shown to simplify the probabilistic assessment of complex structures for improving numerical accuracy and simulation efficiency. Taking the LCF damage prediction of turbine disks as an example, DC-PS-MIIL is demonstrated to be an effective reliability approach. Also, numerical results show that the confidence levels, applied cycles and coefficients of variation (CVs) of random input variables have important impact on the LCF damage reliability for turbine disks.
机译：本文的目的是开发一种有效的可靠性方法，以提高涡轮机磁盘的低循环疲劳（LCF）损坏预测的数值和计算效率。该方法被称为分布式协作（DC）概率子结构（PS），用于移动改进的重要性 - 采样最小二乘（MIIL），DC-PS-MIIL。在这种方法中，首先通过将改进的重要性采样（IIS）与移动最小二乘（MLS）组合来实现MIIL模型，以降低计算负担并提高代理模型的精度;然后，通过将概率的子结构（PS）方法集成到MIIL中来解决PS-MIIL，以解决涡轮机盘的界面力;最后，通过将分布式协作策略掺入与PS-MIIL的分布式协同策略提出。显示具有DC-PS-MIIL的综合分析程序，简化了复杂结构的概率评估，以提高数值精度和仿真效率。以涡轮机盘的LCF损伤预测为例，证明DC-PS-MIIL是一种有效的可靠性方法。此外，数值结果表明，随机输入变量的变化（CVS）的置信水平，应用周期和系数对涡轮机磁盘的LCF损伤可靠性具有重要影响。

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