ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Maintenance-oriented approach for HPT blade life digital twin modeling
Received date: 2023-07-29
Revised date: 2023-08-31
Accepted date: 2023-10-09
Online published: 2023-11-07
Supported by
National Natural Science Foundation of China(52072176)
High-Pressure Turbine （HPT） blade is a typical hot-end component with high temperature， high load and complex structure. Its in-service life depends not only on the level of design， manufacture and process， but also on the actual operating environment， operating conditions and maintenance of the engine. Therefore， how to fuse multi-modal operation and maintenance data to improve the prediction accuracy of HPT blade in-service life and reduce the prediction uncertainty is an important issue. Based on the Usage-Based Life （UBL） method and digital twin technology， this paper proposes a Life Digital Twin （LDT） modeling method for in-service HPT blades driven by data and model fusion. Based on multi-modal operation and maintenance data， the life consumption of HPT blades under actual service conditions can be characterized and tracked， and the in-service lifetime can be predicted under specific operating condition. In addition， the approach of uncertainty quantification and management involved in LDT is also proposed. The approach proposed has been verified on an HPT blade. The results show that the LDT and uncertainty quantification and management approach proposed can effectively fuse multi-modal operation and maintenance data， reduce the prediction uncertainty of HPT blade’s in-service load and life， as well as improve the fidelity of HPT blade life digital twin model； therefore， provides a basic approach support for single engine life management and intelligent operation and maintenance based on digital twin.
Chunhua LI , Jianzhong SUN , Jilong LU . Maintenance-oriented approach for HPT blade life digital twin modeling[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(21) : 629385 -629385 . DOI: 10.7527/S1000-6893.2023.29385
| 1 | 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］∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2014： V07BT30A002. |
| 2 | GIESECKE D， WEHKING M， FRIEDRICHS J， et al. A method for forecasting the condition of several HPT parts by using Bayesian belief networks［C］?∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2015： V07AT29A003. |
| 3 | WADE R A. A need-focused approach to air force engine health management research［C］∥ 2005 IEEE Aerospace Conference. Piscataway： IEEE Press， 2005： 1-13. |
| 4 | XIA T B， DONG Y F， XIAO L， et al. Recent advances in prognostics and health management for advanced manufacturing paradigms［J］. Reliability Engineering & System Safety， 2018， 178： 255-268. |
| 5 | 郑新前， 王钧莹， 黄维娜，等. 航空发动机不确定性设计体系探讨［J］. 航空学报， 2023， 44（7）： 027099. |
| ZHENG X Q， WANG J Y， HUANG W N， et al. Uncertainty-based design system for aeroengines［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（7）： 027099 （in Chinese）. | |
| 6 | GRELOTTI R， GLANOVSKY J. Usage-based life prediction and fleet management for gas turbine engines［C］?∥ 51st AIAA/ASME/ASCE/AHS/ASC Structures， Structural Dynamics， and Materials Conference. Reston： AIAA， 2010： 2972. |
| 7 | ARAHCHIGE B， PERINPANAYAGAM S. Uncertainty quantification in aircraft gas turbine engines［J］. Proceedings of the Institution of Mechanical Engineers， Part G： Journal of Aerospace Engineering， 2018， 232（9）： 1628-1638. |
| 8 | WANG Y C， TAO F， ZHANG M， et al. Digital twin enhanced fault prediction for the autoclave with insufficient data［J］. Journal of Manufacturing Systems， 2021， 60： 350-359. |
| 9 | KRAFT J， KUNTZAGK S. Engine fleet-management： The use of digital twins from an MRO perspective［C］∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2017： V001T01A007. |
| 10 | ZACCARIA V， STENFELT M， ASLANIDOU I， et al. Fleet monitoring and diagnostics framework based on digital twin of aeroengines［C］∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2018：V006T05A021. |
| 11 | DAWES B， HUNT M， MEAH N， et al. Physics-based simulation in support of a through-life gas turbine service business model［C］∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2019： V001T01A015. |
| 12 | KRISHNABABU S， VALERO O， WELLS R. AI assisted high fidelity multi-physics digital twin of industrial gas turbines［C］?∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2021： V02DT36A007. |
| 13 | SUN J Z， YAN Z C， HAN Y， et al. Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective［J］. Reliability Engineering & System Safety， 2023， 238： 109404. |
| 14 | ABU A O， ESHATI S， LASKARIDIS P， et al. Aero-engine turbine blade life assessment using the Neu/Sehitoglu damage model［J］. International Journal of Fatigue， 2014， 61： 160-169. |
| 15 | 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. |
| 16 | CAI L， HOU Y， LI F， et al. Impacts of startup， shutdown and load variation on transient temperature and thermal stress fields within blades of gas turbines［J］. Journal of Thermal Science， 2022， 31（3）： 727-740. |
| 17 | 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. Piscataway： IEEE Press， 2015： 133-138. |
| 18 | VAN ENKHUIZEN M J， DRESBACH C， REH S， et al. Efficient lifetime prediction of high-pressure turbine blades in real life conditions［C］?∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2017： V07AT31A002. |
| 19 | SONG L K， BAI G C， FEI C W， et al. Reliability-based fatigue life prediction for complex structure with time-varying surrogate modeling［J］. Advances in Materials Science and Engineering， 2018， 2018： 1-16. |
| 20 | SPODNIAK M， SEMRáD K， DRAGANOVá K. Turbine blade temperature field prediction using the numerical methods［J］. Applied Sciences， 2021， 11（6）： 2870. |
| 21 | CARTER J A， GOSWAMI T. Probabilistic risk assessment for life extension of turbine engine rotors［J］. Metals， 2022， 12（8）： 1269. |
| 22 | RíOS J， STAUDTER G， WEBER M， et al. Uncertainty of data and the digital twin： A review［J］. International Journal of Product Lifecycle Management， 2020， 12（4）： 329-358. |
| 23 | SCHNELL R， LENGYEL-KAMPMANN T， NICKE E. On the impact of geometric variability on fan aerodynamic performance， unsteady blade row interaction， and its mechanical characteristics［J］. Journal of Turbomachinery， 2014， 136（9）： 091005. |
| 24 | LAMB C T， DARMOFAL D L. Performance-based geometric tolerancing of compressor blades［C］?∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2004： 203-210. |
| 25 | SHI W， LI W， SHI B， et al. Uncertainty quantification of conjugate heat transfer of a cooled turbine vane： Roughness effect［C］?∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2017： V05AT10A004. |
| 26 | LIU X J， TANG H L， ZHANG X， et al. Gaussian process model-based performance uncertainty quantification of a typical turboshaft engine［J］. Applied Sciences， 2021， 11（18）： 8333. |
| 27 | LIU H， SUN J Z， LEI S Y， et al. In-service aircraft engines turbine blades life prediction based on multi-modal operation and maintenance data［J］. Propulsion and Power Research， 2021， 10（4）： 360-373. |
| 28 | 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（4）： 2463. |
| 29 | HUANG Y F， TAO J， SUN G， et al. A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis［J］. Energy， 2023， 270： 126894. |
| 30 | ORLANDE H R B. Inverse problems in heat transfer： New trends on solution methodologies and applications［J］. Journal of Heat Transfer， 2012， 134（3）： 031011. |
| 31 | CHAO M A， LILLEY D S， MATHé P， et al. Calibration and uncertainty quantification of gas turbine performance models［C］∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2015： V07AT29A001. |
| 32 | WHITE A， MAHADEVAN S， GREY Z， et al. Efficient calibration of a turbine disc heat transfer model under uncertainty［J］. Journal of Thermophysics and Heat Transfer， 2020， 35（2）： 234-244. |
| 33 | NANNAPANENI S， MAHADEVAN S. Reliability analysis under epistemic uncertainty［J］. Reliability Engineering & System Safety， 2016， 155： 9-20. |
| 34 | ABSI G N， MAHADEVAN S. Multi-fidelity approach to dynamics model calibration［J］. Mechanical Systems and Signal Processing， 2016， 68： 189-206. |
| 35 | O’HAGAN A. Bayesian analysis of computer code outputs： A tutorial［J］. Reliability Engineering & System Safety， 2006， 91（10-11）： 1290-1300. |
| 36 | KENNEDY M C， O’HAGAN A. Bayesian calibration of computer models［J］. Journal of the Royal Statistical Society： Series B （Statistical Methodology）， 2001， 63（3）： 425-464. |
| 37 | LIU H， SUN J Z， LEI S Y. In-service reliability assessment of turbine blade thermal barrier coatings based on a novel cumulative damage index model［J］. Journal of Engineering for Gas Turbines and Power， 2021， 143（11）： 111005. |
| 38 | 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. |
| LEI S Y， SUN J Z， LIU H. Cumulative damage index model and service reliability evaluation of turbine blade［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（3）： 225064 （in Chinese）. | |
| 39 | 《中国航空材料手册》编辑委员会. 中国航空材料手册［M］. 北京： 中国标准出版社， 2001： 812-818. |
| Editorial Committee of《China Aeronautical Materials Manual》. China aeronautical materials manual［M］. Beijing： Standards Press of China， 2001： 812-818 （in Chinese）. | |
| 40 | MONTOMOLI F， CARNEVALE M， D’AMMARO A， et al. Uncertainty quantification in computational fluid dynamics and aircraft engines［M］. Cham： Springer International Publishing， 2015. |
| 41 | 何磊， 黄启鹤， 赵连会， 等. 重型燃气轮机透平叶片表面粗糙度对换热的影响［J］. 动力工程学报， 2019， 39（9）： 711-716， 746. |
| HE L， HUANG Q H， ZHAO L H， et al. Effect of surface roughness on heat transfer characteristics of heavy duty gas turbine airfoils［J］. Journal of Chinese Society of Power Engineering， 2019， 39（9）： 711-716， 746 （in Chinese）. | |
| 42 | 董平， 郭兆元， 王强， 等. 进口温度不均匀对气冷涡轮叶片传热的影响［J］. 工程热物理学报， 2010， 31（7）： 1109-1112. |
| DONG P， GUO Z Y， WANG Q， et al. Effects of non-uniform inlet temperature distribution on air cooled gas turbine vane［J］. Journal of Engineering Thermophysics， 2010， 31（7）： 1109-1112 （in Chinese）. | |
| 43 | GLASENAPP T， PUETZ F， SCHULZ A， et al. Assessment of real turbine blade roughness parameters for the design of a film cooling test rig［C］?∥Turbo Expo： Power for Land， Sea， and Air. New York： ASME， 2017： V05AT13A001. |
| 44 | WANG J Y， ZHENG X Q. Review of geometric uncertainty quantification in gas turbines［J］. Journal of Engineering for Gas Turbines and Power， 2020， 142（7）： 070801. |
| 45 | HUANG M， LI Z G， LI J， et al. Efficient uncertainty quantification and sensitivity analysis on the aerothermal performance of turbine blade squealer tip［J］. Journal of Turbomachinery， 2022， 144（5）： 051014. |
| 46 | SAHA S K， MATSAGAR V， CHAKRABORTY S. Uncertainty quantification and seismic fragility of base-isolated liquid storage tanks using response surface models［J］. Probabilistic Engineering Mechanics， 2016， 43： 20-35. |
| 47 | VAN RAVENZWAAIJ D， CASSEY P， BROWN S D. A simple introduction to Markov chain Monte–Carlo sampling［J］. Psychonomic Bulletin & Review， 2018， 25（1）： 143-154. |
| 48 | JOHNSON A A， JONES G L， NEATH R C. Component-wise Markov chain Monte Carlo： Uniform and geometric ergodicity under mixing and composition［J］. Statistical Science， 2013， 28（3）： 360-375. |
| 49 | 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. |
| 50 | 雷世英. 服役条件下多模态数据驱动的典型关重件寿命预测模型研究［D］. 南京： 南京航空航天大学， 2021： 57-61. |
| LEI S Y. Research on life prediction model of typical key and important components driven by multimodal data under service conditions［D］. Nanjing： Nanjing University of Aeronautics and Astronautics， 2021： 57-61 （in Chinese）. | |
| 51 | NEU R W， SEHITOGLU H. Thermomechanical fatigue， oxidation， and creep： Part II. Life prediction［J］. Metallurgical Transactions A， 1989， 20（9）： 1769-1783. |
/
| 〈 |
|
〉 |
CJA