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.
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
.
DOI: 10.7527/S1000-6893.2020.24747
[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.
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