朱顺鹏，雷强，黄洪钟，杨亮
（电子科技大学机械电子工程学院，成都 611731）
摘 要：在机械构件或材料的疲劳寿命预测中，概率方法已被广泛用于各种不确定性分析，Bayes方法提供了一个表征各种不确定性的理论基础，依据历史知识、经验或数据，给出模型参数的先验分布，并能依据现有新的知识进行信息更新，以便准确地进行寿命预估、可靠性分析和维修决策。相比确定性寿命预测方法，本文依据Bayes推理及信息更新技术，综合考虑模型参数、模型输入变量、材料属性和模型等不确定性，构建了概率故障物理寿命预测理论框架，且提出了混合不确定性量化方法-White-Box方法，并探讨了其在概率寿命预测的应用。最后，通过对涡轮盘材料GH4133进行概率寿命预测，对比分析了基于延性耗竭模型（VBM）、广义能量损伤参数（GDP）、SWT模型和塑性应变能密度（PSED）法的寿命预测结果，其预测结果与实测结果均吻合较好，且基于VBM模型的寿命预测值的不确定性范围明显窄于SWT、GDP和PSED模型对应的不确定性范围。
关键词：高温低周疲劳；不确定性；概率寿命预测；涡轮盘；Bayes推理
Probabilistic Life Prediction for Aircraft Turbine Disc Alloy Based on Hybrid-uncertainty Quantification
Shun-Peng Zhu, Qiang Lei, Hong-Zhong Huang, Liang Yang
School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
Abstract: Probabilistic methods have been widely used to account for uncertainty from various sources to predict fatigue life for components/materials. The Bayesian approach can potentially give more accurate estimates through combining test data with technical knowledge available from theoretical analyses and/or previous experimental results. The aim of the present paper is to develop a probabilistic methodology for high temperature low cycle fatigue life prediction using physics based models and to demonstrate the use of an efficient probabilistic method. Accordingly, a white-box approach is developed to quantify the hybrid-uncertainty for four life prediction methods （the viscosity based model （VBM）， generalized damage parameter （GDP）， SWT and plastic strain energy density （PSED）） using measured differences between experimental data and model predictions. The proposed method was verified using experimental data for turbine disc alloy GH4133 under different temperatures from literature. The results show that the uncertainty bounds using the VBM for life prediction are tighter than that of GDP, SWT and PSED methods, which leads to better decision making based on the same available knowledge.
Keywords: High temperature low cycle fatigue, uncertainty, probabilistic life prediction, turbine disc, Bayesian inference
联系方式
