Application of Sub-Model Technology in Reliability Analysis of Aero-Engine Turbine Disks
DOI: https://doi.org/10.62517/jiem.202603309
Author(s)
Qiao Wenkang
Affiliation(s)
Jilin Business and Technology College, Jilin, China
Abstract
Aero-engine turbine disks are subjected to high-speed centrifugal loads, non-uniform temperature fields, and start-stop cycles. High stress gradients and localized plasticity easily form in the tenon, hole edges, and hub transition areas. Full-disk fine-scale finite element analysis (FEM) is costly due to contact nonlinearity and probabilistic sampling. Finite element sub-models, on the other hand, achieve scale separation through a process of "global solution-local truncation-boundary field interpolation-fine-scale solution." This paper reviews the basic principles, local modeling objects of turbine disks, reliability coupling process, typical failure modes, and methodological differences. Research shows that sub-models can reduce the cost of local analysis, but their reliability depends on the cut boundaries, global model accuracy, field variable transfer, and local nonlinearity handling. Future development should focus on multi-field bidirectional coupling, adaptive error control, fusion of manufacturing and measured data, and intelligent physical information models oriented towards multiple failure modes.
Keywords
Aero-Engine; Turbine Disk; Finite Element Sub-Model; Fatigue Reliability; Uncertainty Quantification
References
[1] CORMIER NG, SMALLWOOD BS, SINCLAIR GB, MEDA G. Aggressive submodelling of stress concentrations[J]. International Journal for Numerical Methods in Engineering, 1999, 46(6): 889-909.
[2] KARDAK AA, SINCLAIR G B. Verification of submodeling for the finite element analysis of stress concentrations[J]. Journal of Verification, Validation and Uncertainty Quantification, 2019, 4(3): 031003.
[3] PAPANIKOS P, MEGUID SA, STJEPANOVIC Z. Three-dimensional nonlinear finite element analysis of dovetail joints in aeroengine discs[J]. Finite Elements in Analysis and Design, 1998, 29(3): 173-186.
[4] MEGUID SA, KANTH PS, CZEKANSKI A. Finite element analysis of fir-tree region in turbine discs[J]. Finite Elements in Analysis and Design, 2000, 35(4): 305-317.
[5] SINCLAIR GB, CORMIER NG, GRIFFIN JH, MEDA G. Contact stresses in dovetail attachments: Finite element modeling[J]. Journal of Engineering for Gas Turbines and Power, 2002, 124(1): 182-189.
[6] BEISHEIM JR, SINCLAIR G B. On the three-dimensional finite element analysis of dovetail attachments[J]. Journal of Turbomachinery, 2003, 125(2): 372-379.
[7] BEISHEIM JR, SINCLAIR G B. Three-dimensional finite element analysis of dovetail attachments with and without crowning[J]. Journal of Turbomachinery, 2008, 130(2): 021012.
[8] YAN C, ZHU JF, SHEN XL, FAN J, JIA ZG, CHEN T F. Structural design and optimization for vent holes of an industrial turbine sealing disk[J]. Chinese Journal of Aeronautics, 2021, 34(5): 265-277.
[9] MELIS ME, ZARETSKY EV, AUGUST R. Probabilistic analysis of aircraft gas turbine disk life and reliability[J]. Journal of Propulsion and Power, 1999, 15(5): 658-666.
[10] ZHU SP, HUANG HZ, SMITH R, ONTIVEROS V, HE LP, MODARRES M. Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys[J]. Probabilistic Engineering Mechanics, 2013, 34: 114-122.
[11] ZHU SP, HUANG HZ, PENG W, WANG HK, MAHADEVAN S. 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.
[12] NIU XP, WANG RZ, LIAO D, ZHU SP, ZHANG XC, KESHTEGAR B. Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks[J]. International Journal of Fatigue, 2021, 142: 105912.
[13] WITEK L. Failure analysis of turbine disc of an aero engine[J]. Engineering Failure Analysis, 2006, 13(1): 9-17.
[14] TOMEVENYA KM, LIU S J. Probabilistic fatigue-creep life reliability assessment of aircraft turbine disk[J]. Journal of Mechanical Science and Technology, 2018, 32: 5127-5132.
[16] Huang Xiaoyu, Wang Pan, Li Haihe, Zhang Zheng. Fatigue reliability and sensitivity analysis of turbine disk with fuzzy failure state [J]. Journal of Northwestern Polytechnical University, 2021, 39(6): 1312-1319.
[17] SONG LK, BAI GC, FEI C W. Probabilistic LCF life assessment for turbine discs with DC strategy-based wavelet neural network regression[J]. International Journal of Fatigue, 2019, 119: 204-219.
[18] YAO Q, ZHANG MC, LIU YS, GUO Q. Life reliability assessment of twin-web disk using the active learning Kriging model[J]. Structural and Multidisciplinary Optimization, 2020, 61: 1229-1251.
[19] LI XQ, SONG LK, CHOY YS, BAI G C. Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023, 381(2260): 20220384.
[20] SONG LK, CHOY YS, ZHANG S, WANG B L. Multi-XGB: A multi-objective reliability evaluation approach for aeroengine turbine discs[J]. Digital Engineering, 2024, 2: 100006.