固体力学与飞行器总体设计

涡轮叶片累积损伤指数模型及服役可靠性评估

  • 雷世英 ,
  • 孙见忠 ,
  • 刘赫
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  • 南京航空航天大学 民航学院, 南京 211106

收稿日期: 2020-12-07

  修回日期: 2020-12-28

  网络出版日期: 2021-01-21

基金资助

国家自然科学基金(91860139,52072176)

Cumulative damage index model and service reliability evaluation of turbine blade

  • LEI Shiying ,
  • SUN Jianzhong ,
  • LIU He
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  • College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

Received date: 2020-12-07

  Revised date: 2020-12-28

  Online published: 2021-01-21

Supported by

National Natural Science Foundation of China (91860139, 52072176)

摘要

高压涡轮(HPT)叶片是民用航空发动机的关键结构件之一,直接关系到发动机的性能、可靠性与使用寿命。提出了一种HPT叶片服役可靠性评估方法,基于服役条件下的历史工况参数,结合发动机性能模型、叶片关键点应力、温度计算模型、蠕变损伤评估模型对叶片蠕变损伤进行计算,之后考虑服役条件下的多模态数据,针对蠕变失效建立了累积损伤指数模型,融合历史协变量信息对叶片进行服役可靠性评估。仿真结果表明:采用文中定义的蠕变累积损伤指数,可充分利用发动机服役条件下的历史使用信息、状态参数及截尾失效数据,实现特定使用条件下的涡轮叶片服役可靠性评估及剩余寿命预测。相较于传统的可靠性分析方法,累积损伤指数预测模型能够基于单机服役条件提供更加可靠的评估结果,可为航空发动机运行风险评估与视情维修决策提供更好的支持。

本文引用格式

雷世英 , 孙见忠 , 刘赫 . 涡轮叶片累积损伤指数模型及服役可靠性评估[J]. 航空学报, 2022 , 43(3) : 225064 -225064 . DOI: 10.7527/S1000-6893.2021.25064

Abstract

High Pressure Turbine (HPT) blades are one of the key structural parts of civil aviation engines, directly related to the performance, reliability and service life of the engine.This paper proposes a service reliability assessment method for HPT blades.The blade creep damage is evaluated based on historical flight condition data under service conditions, combined with the engine performance model, the stress and temperature calculation model of the key point, and the creep damage assessment model.After calculation, and in consideration of the multi-modal data under service conditions, a cumulative damage index model is established for creep failure, and the service reliability of the blade is evaluated by fusing the historical covariate information.The simulation results show that the creep cumulative damage index defined in the article can make full use of the historical usage information, state parameters and truncated failure data of the engine under service conditions to realize the service reliability assessment and remaining life prediction of the turbine blades under specific service conditions.Compared with traditional reliability analysis methods, the cumulative damage index prediction model can provide more reliable evaluation results based on the service conditions of a single aircraft and better support for aero engine operation risk assessment and condition-based maintenance decision-making.

参考文献

[1] 孙见忠, 左洪福, 梁坤.基于民航发动机状态数据的涡轮叶片剩余寿命评估[J].机械工程学报, 2015, 51(23):53-59. SUN J Z, ZUO H F, LIANG K.Remaining useful life estimation method for the turbine blade of a civil aircraft engine based on the QAR and field failure data[J].Journal of Mechanical Engineering, 2015, 51(23):53-59(in Chinese).
[2] WADE R A.A need-focused approach to air force engine health management research[C]//2005 IEEE Aerospace Conference, 2005:1-13.
[3] NAEEM M.Implications of day temperature variation for an aero-engine's HP turbine-blade's creep life-consumption[J].Aerospace Science & Technology, 2009, 13(1):27-35.
[4] 孙见忠, 左洪福.使用条件对民航发动机涡轮叶片蠕变寿命的影响分析[J].中国机械工程, 2014, 25(11):1511-1516. SUN J Z, ZUO H F.Impacts of operating and health conditions on civil aircraft engine turbine blade creep life[J].China Mechanical Engineering, 2014, 25(11):1511-1516(in Chinese).
[5] AN D, CHOI J H, KIM N H, et al.Fatigue life prediction based on Bayesian approach to incorporate field data into probability model[J].Structural Engineering & Mechanics, 2011, 37(4):427-442.
[6] ZARETSKY E V, LITT J S, HENDRICKS R C, et al.Determination of turbine blade life from engine field data[J].Journal of Propulsion and Power, 2012, 28(6):1156-1167.
[7] LI H, HUANG H Z, LI Y F, et al.Physics of failure-based reliability prediction of turbine blades using multi-source information fusion[J].Applied Soft Computing, 2018, 72:624-635.
[8] STAROSELSKY A, MARTIN T J, CASSENTI B.Transient thermal analysis and viscoplastic damage model for life prediction of turbine components[J].Journal of Engineering for Gas Turbines and Power, 2015, 137(4):042501.
[9] HULS R A, LAMMEN W, MAAS R, et al.A quick prediction model for prognostic health management of an engine turbine blade[C]//International Scientific Conference Modern Safety Technologies in Transportation, 2015.
[10] VAN ENKHUIZEN M J, DRESBACH C, REH S, et al.Efficient lifetime prediction of high pressure turbine blades in real life conditions[C]//ASME Turbo Expo:Power for Land, Sea, and Air, 2017:V07AT31A002.
[11] ZHU S P, YUE P, YU Z Y, et al.A combined high and low cycle fatigue model for life prediction of turbine blades[J].Materials, 2017, 10(7):698.
[12] BRANDÃO P, INFANTE V, DEUS A M.Thermo-mechanical modeling of a high pressure turbine blade of an airplane gas turbine engine[J].Procedia Structural Integrity, 2016, 1:189-196.
[13] PILLAI P, KAUSHIK A, BHAVIKATTI S, et al.A hybrid approach for fusing physics and data for failure prediction[J].International Journal of Prognostics and Health Management, 2016, 7(25):1-12.
[14] GIESECKE D, FRIEDRICHS J, KENULL T, et al.A method for forecasting the condition of HPT NGVs by using Bayesian belief networks and a statistical approach[C]//ASME Turbo Expo:Power for Land, Sea, and Air, 2014:V07BT30A002.
[15] GIESECKE D, WEHKING M, FRIEDRICHS J, et al.A method for forecasting the condition of several HPT parts by using Bayesian belief networks[C]//ASME Turbo Expo:Power for Land, Sea, and Air, 2015:V07AT29-A003.
[16] FU C, CHEN Y, HE S, et al.ICME Framework for damage assessment and remaining creep life prediction of in-service turbine blades manufactured with Ni-based superalloys[J].Integrating Materials and Manufacturing Innovation, 2019, 8(4):509-520.
[17] 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 aero turbojet and turbofan engines[M].Beijing:Aviation Industry Press, 2004(in Chinese).
[18] 刘葆华, 黄金泉.基于高压涡轮叶片寿命损耗的航空发动机功率控制[J].航空动力学报, 2013, 28(12):2836-2841. LIU B H, HUANG J Q.Aero-engine power control based on life consumption of high pressure turbine blade[J].Journal of Aerospace Power, 2013, 28(12):2836-2841(in Chinese).
[19] HARRISON G F, HOMEWOOD T.The application of the Graham and Walles creep equation to aeroengine superalloys[J].The Journal of Strain Analysis for Engineering Design, 1994, 29(3):177-184.
[20] 《工程材料实用手册》编委会.工程材料实用手册.第2卷, 变形高温合金铸造高温合金-第2版[M].北京:中国标准出版社, 2002. Editorial board of "Engineering materials practical handbook".Engineering materials practical handbook, Volume 2, Wrought superalloys casting superalloys-2nd edition[M].Beijing:Standards Press of China, 2002(in Chinese).
[21] 《航空发动机设计用材料数据手册》编委会.航空发动机设计用材料数据手册[M].北京:航空工业出版社, 2010. Editorial board of "Material data manual for aeroengine design".Material data book for aeroengine design[M].Beijing:Aviation industry press, 2010(in Chinese).
[22] REED R C.The superalloys:fundamentals and applications[M].Cambridge:Cambridge University Press, 2008.
[23] STAMATIS A, MATHIOUDAKIS K, SMITH M, et al.Gas turbine component fault identification by means of adaptive performance modeling[C]//ASME Turbo Expo:Power for Land, Sea, and Air, 1990:V005T15A015.
[24] LAMBIRIS B, MATHIOUDAKIS K, STAMATIS A, et al.Adaptive modeling of jet engine performance with application to condition monitoring[J].Journal of Propulsion and Power, 1994, 10(6):890-896.
[25] STAMATIS A, MATHIOUDAKIS K, RUIZ J, et al.Real time engine model implementation for adaptive control and performance monitoring of large civil turbofans[C]//ASME Turbo Expo:Power for Land, Sea, and Air, 2001:V001T01A002.
[26] OGIRIKI E A, LI Y G, NIKOLAIDIS T.Prediction and analysis of impact of thermal barrier coating oxidation on gas turbine creep life[J].Journal of Engineering for Gas Turbines and Power, 2016, 138(12):121501.
[27] WALSH P P, FLETCHER P.Gas turbine performance[M].New York:John Wiley & Sons, 2008.
[28] HAN J C, DUTTA S, EKKAD S.Gas turbine heat transfer and cooling technology[M].2012.
[29] GHAFIR A, BIN M F.Performance based creep life estimation for gas turbines application[D].Cranfield:Cranfield University, 2011.
[30] SARAVANAMUTTOO H I H, ROGERS G F C, COHEN H.Gas turbine theory[M].2009.
[31] NELSON W B.Accelerated testing:statistical models, test plans, and data analysis[M].New York:John Wiley & Sons, 2009.
[32] 孙有朝, 张永进, 李龙彪.可靠性原理与方法[M].北京:科学出版社, 2016. SUN Y C, ZHANG Y J, LI L B.Reliability principle and method[M].Beijing:Science Press, 2016(in Chinese).
[33] HONG Y, MEEKER W Q.Field-failure predictions based on failure-time data with dynamic covariate information[J].Technometrics, 2013, 55(2):135-149.
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