涡轮冷却叶片寿命可靠性分析参数化仿真平台

doi:10.7527/S1000-6893.2020.24747

固体力学与飞行器总体设计

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

涡轮冷却叶片寿命可靠性分析参数化仿真平台

贾贝熙, 吕震宙, 雷婧宇

西北工业大学航空学院, 西安 710072

收稿日期:2020-09-11 修回日期:2020-10-14 发布日期:2020-11-06
通讯作者: 吕震宙 E-mail:zhenzhoulu@nwpu.edu.cn
基金资助:
国家自然科学基金（52075442）；国家科技重大专项（2017-IV-0009-0046）

Parameterized simulation platform of turbine cooling film blade life reliability analysis

JIA Beixi, LYU Zhenzhou, LEI Jingyu

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received:2020-09-11 Revised:2020-10-14 Published:2020-11-06
Supported by:
National Natural Science Foundation of China (52075442); Major National Science and Technology Projects(2017-IV-0009-0046)

摘要/Abstract

摘要： 目前，涡轮冷却叶片等复杂结构的多模式寿命可靠性分析中存在各功能模块的集成与管理不成体系、参数化联合调用技术不完善的工程应用问题。针对这些问题，完善了随机不确定性下涡轮冷却叶片多模式寿命可靠性分析的工程化方法，搭建了多模式寿命可靠性分析的参数化、多软件联合仿真平台，为某型号叶片寿命可靠性分析提供合理的工程化方法及高效便捷的自动化实现工具。主要工作包括：一建立了不确定性环境下含孔、肋及空腔复杂结构网格划分和结构有限元仿真的参数化方法，实现了随机变量不同取值下仿真的自动执行，解决了可靠性理论方法应用至复杂工程结构的瓶颈问题；二在经回归处理的概率寿命曲线中考虑温度插值及多失效模式串联系统，拓展了概率寿命曲线的应用范围，使得所建涡轮叶片寿命可靠性模型更符合实际；三提出了可靠性分析数字模拟过程中嵌入包括有限元结构分析和疲劳寿命极限状态面两方面的双层自适应代理模型方法，该自适应策略可在保证寿命可靠性分析精度的基础上提高效率。通过所建平台在某型号叶片上的算例分析及与蒙特卡洛法参考解的对比，验证了所提多模式系统寿命可靠性分析工程化方法的高效和准确性及仿真平台的实用性。

关键词: 涡轮冷却叶片, 参数化联合仿真平台, 多模式疲劳寿命概率模型, 可靠性分析, 代理模型

Abstract: There are management and parameterization problems in the multiple mode life reliability analysis of complicated structure as turbine cooling film blade. To solve these problems, engineering technologies and simulation platform of multiple mode life reliability analysis in the presence of random uncertainty are created, which contains parameterization of mesh and finite element analysis, multiple mode probability life prediction, as well as the life reliability analysis methods. It is an efficient and automatic tool for the multiple mode life reliability analysis of turbine cooling film blades. The platform is able to do the parameterization of mesh and finite element simulation, so that automatic computation can be done with different inputs. On the basis of current turbine cooling film blade material life test data linear heteroscedasticity regression, homovariance polynomial regression and nonlinear damage accumulation criterion, a probability life prediction model containing temperature term of the low cycle fatigue, high cycle fatigue, creep and coupled multi-modes is proposed. Furthermore, adaptive Kriging surrogate model is implied in the platform to make a global approximation of the finite element simulation and a local approximation of the life failure boundary, the multimode life random distribution characteristics as well as the failure probability of the series system are obtained. As a result, and the platform greatly improves the efficiency of complicated structure multiple mode life reliability analysis. Finally, a test case of turbine cooling film blade life reliability analysis is run in the platform, it is proved that the proposed multiple mode life reliability analysis methods and the platform are well behaved in the application of complicated structure life reliability analysis.

Key words: turbine cooling film blades, parameterized co-simulation platform, multiple probability life prediction model, reliability analysis, surrogate model

中图分类号:

V228

贾贝熙, 吕震宙, 雷婧宇. 涡轮冷却叶片寿命可靠性分析参数化仿真平台[J]. 航空学报, 2021, 42(12): 224747-224747.

JIA Beixi, LYU Zhenzhou, LEI Jingyu. Parameterized simulation platform of turbine cooling film blade life reliability analysis[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(12): 224747-224747.

参考文献

[1] LEWIS B L, BECKWITH L R. A unified approach to turbine blade life prediction[C]//Aerospace Congress and Exposition. Warrendale:SAE International, 1982:821439.
[2] 高阳, 白广忱. 轮盘低循环疲劳寿命可靠性分析方法[J]. 机械设计与制造, 2009(6):60-62. GAO Y, BAI G C. Reliability analysis method for the low cycle fatigue life of a disk[J]. Machinery Design & Manufacture, 2009(6):60-62(in Chinese).
[3] 江龙平, 徐可君, 隋育松. 叶片振动的灰色可靠性研究[J]. 汽轮机技术, 2002, 44(5):285-286, 309. JIANG L P, XU K J, SUI Y S. Gray reliability research on vibration of blades[J]. Turbine Technology, 2002, 44(5):285-286, 309(in Chinese).
[4] 王延荣, 宋兆泓, 侯贵仓. 涡轮叶片高温低循环疲劳/蠕变寿命试验评定[J]. 航空动力学报, 2002, 17(4):407-411. WANG Y R, SONG Z H, HOU G C. Experimental evaluation of high temperature low cycle fatigue/creep life of turbine blade[J]. Journal of Aerospace Power, 2002, 17(4):407-411(in Chinese).
[5] ZHU S P, HUANG H Z, PENG W W, et al. Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty[J]. Reliability Engineering & System Safety, 2016, 146:1-12.
[6] ZHU S P, LIU Q, PENG W W, et al. Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks[J]. International Journal of Mechanical Sciences, 2018, 142-143:502-517.
[7] ZHU S P, LIU Q, ZHOU J, et al. Fatigue reliability assessment of turbine discs under multi-source uncertainties[J]. Fatigue & Fracture of Engineering Materials & Structures, 2018, 41(6):1291-1305.
[8] NIU X P, WANG R Z, LIAO D, et al. Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks[J]. International Journal of Fatigue, 2021, 142:105912.
[9] 龚勋. 涡轮冷却叶片结构网格参数化方法研究[D]. 南京:南京航空航天大学, 2016. GONG X. Research on parametric block-decomposition methods of turbine cooling blades' mesh-generation[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2016(in Chinese).
[10] 李磊, 杨子龙, 王佩艳. 燃气轮机涡轮冷却叶片设计及优化[M]. 北京:科学出版社, 2018. LI L, YANG Z L, WANG P Y. Cooling blade design and optimization of gas turbine[M]. Beijing:Science Press, 2018(in Chinese).
[11] 苏清友. 航空涡喷、涡扇发动机主要零部件定寿指南[M]. 北京:航空工业出版社, 2004. SU Q Y. Life determination guide for main components of areo turbojet and turbofan engines[M]. Beijing:Aviation Industry Press, 2004(in Chinese).
[12] 《中国航空材料手册》编委会. 中国航空材料手册(第2卷:变形高温合金、铸造高温合金)[M]. 2版. 北京:中国标准出版社, 2001. Editorial Committee of China Aviation Materials Manual. Handbook of aeronautical materials of China (Volume II:Wrought and cast superalloys)[M]. 2nd edition. Beijing:China Standard Press, 2001(in Chinese).
[13] 岳鹏. 发动机涡轮叶片高低周复合疲劳寿命预测与可靠性分析[D]. 成都:电子科技大学, 2017. YUE P. Combined cycle fatigue life prediction and reliability analysis of turbine blades[D]. Chengdu:University of Electronic Science and Technology of China, 2017(in Chinese).
[14] MORROW J. Cyclic plastic strain energy and fatigue of metals[M]//Internal friction, damping, and cyclic plasticity. West Conshohocken:ASTM International, 1965:45-87.
[15] 殷之平, 谢传. 结构疲劳与断裂[M]. 西安:西北工业大学出版社, 2012. YIN Z P, XIE C. Structural fatigue and fracture M]. Xi'an:Northwestern Polytechnical University Press, 2012(in Chinese).
[16] MANSON S S, SUCCOP G. Stress-rupture properties of inconel 700 and correlation on the basis of several time-temperature parameters[C]//Symposium on Metallic Materials for Service at Temperatures Above 1600 F. West Conshohocken:ASTM International, 1956:40-40-7.
[17] 傅惠民, 高镇同, 梁美训. P-S-N曲线拟合法[J]. 航空学报, 1988, 9(7):338-341. FU H M, GAO Z T, LIANG M X. A method for fitting P-S-N curve[J]. Acta Aeronautica et Astronautica Sinica, 1988, 9(7):338-341(in Chinese).
[18] PETERSON R E. Stress concentration factors:Charts and relations useful in making strength calculations for machine parts and structural elements[M]. New York:John Wiley & Sons, 1974.
[19] GOODMAN J. Mechanics applied to engineering[M]. London:Longmans, Green and Co., Ltd., 1914.
[20] JEAN L. A course on damage mechanics[M]. Berlin:Springer, 1996.
[21] MINER M A.Cumulative damage in fatigue[J]. Journal of Applied Mechanics, 1945, 12(3):A159-A164.
[22] ECHARD B, GAYTON N, LEMAIRE M. AK-MCS:An active learning reliability method combining Kriging and Monte Carlo Simulation[J]. Structural Safety, 2011, 33(2):145-154.
[23] YUN W Y, LU Z Z, ZHOU Y C, et al. AK-SYSi:An improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function[J]. Structural and Multidisciplinary Optimization, 2019, 59(1):263-278.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

涡轮冷却叶片寿命可靠性分析参数化仿真平台

Parameterized simulation platform of turbine cooling film blade life reliability analysis

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	崔壮壮, 原昕, 赵国庆, 井思梦, 招启军. 共轴刚性旋翼高速直升机前飞性能操纵策略影响[J]. 航空学报, 2024, 45(9): 529256-529256.
[2]	柳家齐, 陈荣钱, 楼锦华, 韩旭, 吴昊, 尤延铖. 基于深度学习的高速直升机旋翼翼型气动优化设计[J]. 航空学报, 2024, 45(9): 529828-529828.
[3]	龚煜廉, 张建国, 吴志刚, 褚光远, 范晓铎, 黄赢. 主动学习基自适应PC⁃Kriging模型的复合材料结构可靠度算法[J]. 航空学报, 2024, 45(8): 228982-228982.
[4]	张帆, 程伯晗, 王鹏, 董磊. 二元共载系统退化相关的两阶段退化模型及可靠性分析[J]. 航空学报, 2024, 45(7): 229046-229046.
[5]	陈树生, 冯聪, 张兆康, 赵轲, 张新洋, 高正红. 基于全局/梯度优化方法的宽速域乘波-机翼布局气动设计[J]. 航空学报, 2024, 45(6): 629596-629596.
[6]	李玉涵, 杨宝玉, 吴亦农, 张强, 唐晓. 卫星光机载荷热模型参数高效修正方法研究进展[J]. 航空学报, 2024, 45(6): 628814-628814.
[7]	赵欢, 高正红, 夏露. 基于新型多可信度代理模型的多目标优化方法[J]. 航空学报, 2023, 44(6): 126962-126962.
[8]	赵欢, 高正红, 夏露. 基于新型高维代理模型的气动外形设计方法[J]. 航空学报, 2023, 44(5): 126924-126924.
[9]	张帆, 孙紫荆, 肖国松, 刘嘉琛, 王鹏. 基于直觉模糊贝叶斯网络的HUD系统多阶段任务可靠性分析[J]. 航空学报, 2023, 44(4): 226853-226853.
[10]	郑志阳, 张阳, 张钊, 吴宝海, 张莹. 基于GA⁃SVR的薄壁叶片辅助支撑布局优化方法[J]. 航空学报, 2023, 44(4): 426805-426805.
[11]	于汀, 李璐祎, 刘昱杉, 常泽明. 观测不确定性下的高效贝叶斯更新方法及其在机翼结构中的应用[J]. 航空学报, 2023, 44(24): 228592-228592.
[12]	杨乐昌, 汪晨星. 基于多源异构信息的航空发动机转子参数校准与可靠性分析[J]. 航空学报, 2023, 44(23): 228575-228575.
[13]	姜峰, 李华聪, 符江锋, 洪林雄. 基于RBF和主动学习的非概率可靠度求解方法[J]. 航空学报, 2023, 44(2): 226667-226667.
[14]	冯蕴雯, 林心怡, 薛小锋, 杨祥, 刘佳奇. 高可靠单向爆破的民机防爆结构设计[J]. 航空学报, 2023, 44(18): 228297-228297.
[15]	员婉莹, 吕震宙. 参数可靠性全局灵敏度高效分析的代理模型法[J]. 航空学报, 2023, 44(12): 227670-227670.