Home Uncertainty quantification of leakage characteristics of stepped labyrinth seals based on sparse PC-Kriging model
Article
Licensed
Unlicensed Requires Authentication

Uncertainty quantification of leakage characteristics of stepped labyrinth seals based on sparse PC-Kriging model

  • Decheng Xu ORCID logo , Xiang Zhang EMAIL logo , Zhongzhi Zhang , Jinxin Cheng and Fanzhen Meng
Published/Copyright: June 2, 2025
Become an author with De Gruyter Brill

Abstract

Based on the sparse PC-Kriging method, a high-fidelity surrogate model was constructed to map the geometry of a stepped labyrinth seal to its leakage characteristics. Based on this surrogate model, Monte Carlo sampling was employed to analyze the impact of geometric errors on the uncertainty of the seal’s leakage characteristics and conduct sensitivity analysis. The results indicate that within a 99.7 % confidence interval, the maximum discharge coefficient can reach 0.584; compared to the baseline value of 0.52118, the worst-case scenario could lead to an approximate 12.1 % increase in the discharge coefficient, significantly increasing leakage. Sensitivity analysis reveals that the tip clearance of the fifth tooth exhibits the highest sensitivity to the discharge coefficient, approaching 80 % and dominating the response, while the remaining parameters show lower sensitivity, with 47 of them having sensitivities close to zero.


Corresponding author: Xiang Zhang, School of Aero Engine, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China, E-mail:

Funding source: Henan Provincial Science and Technology Research Program

Award Identifier / Grant number: Grant No.242102220042

Funding source: Guangdong Basic and Applied Basic Research Foundation

Award Identifier / Grant number: Grant No.2022A1515110055

Funding source: Graduate Education Innovation Foundation of Zhengzhou University of Aeronautics

Award Identifier / Grant number: Grant No. 2025CX82

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This research was funded by the Henan Provincial Science and Technology Research Program (Grant No. 242102220042); Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515110055); and Graduate Education Innovation Foundation of Zhengzhou University of Aeronautics (Grant No. 2025CX82).

  7. Data availability: The data used for comparison in this study were obtained from previously published research articles, which are cited in the manuscript.

Nomenclature (SI UNITS)

b

tooth tip width (mm)

h

tooth height (mm)

H

step height (mm)

r

the radius of tooth tip fillet (mm)

R

the radius of tooth root fillet (mm)

s

tooth clearance (mm)

t

tooth pitch (mm)

α

tooth front angle (°)

β

tooth back angle (°)

Superscript

left

left side of tooth

right

right side of tooth

Subscripts

i

The i-th tooth/step/chamber

References

1. Sneck, HJ. Labyrinth seal literature survey. J Lubric Technol 1974;96:579–81. https://doi.org/10.1115/1.3452498.Search in Google Scholar

2. Li, N, Wan, S, Du, W, Zhang, S, Luo, L. Effects of the geometrical features of flow paths on the flow behaviour of a multi-stage labyrinth pressure reducing valve throttling components. Energy (Calg) 2024;296:130962. https://doi.org/10.1016/j.energy.2024.130962.Search in Google Scholar

3. Huang, M, Zhou, Z, Zhang, K, Li, Z, Li, J. Investigation on high-dimensional uncertainty quantification and reliability analysis of aero-engine. Aero Sci Technol 2023;142:108685. https://doi.org/10.1016/j.ast.2023.108685.Search in Google Scholar

4. Kim, TS, Cha, KS. Comparative analysis of the influence of labyrinth seal configuration on leakage behavior. J Mech Sci Technol 2009;23:2830–8. https://doi.org/10.1007/s12206-009-0733-5.Search in Google Scholar

5. Joachimiak, D, Krzyślak, P. Analysis of the impact of the labyrinth seal geometric parameters on the leakage. E3S Web of Conf EDP Sci 2021;323:00015. https://doi.org/10.1051/e3sconf/202132300015.Search in Google Scholar

6. Braun, E, Dullenkopf, K, Bauer, HJ. Optimization of labyrinth seal performance combining experimental, numerical and data mining methods[C]. In: Turbo expo: power for land, sea, and air. American Society of Mechanical Engineers; 2012, vol. 44700:1847–54 pp. https://doi.org/10.1115/gt2012-68077.Search in Google Scholar

7. Zhang, B, Jingjing, L, Li, W, Ji, H. Experimental investigation on sealing and heat transfer characteristics of different geometrical labyrinth seals with orthogonal method[J]. J Comput Theor Nanosci 2017;14:1528–34. https://doi.org/10.1166/jctn.2017.6473.Search in Google Scholar

8. Alizadeh, M, Nikkhahi, B, Farahani, AS, Fathi, A. Numerical study on the effect of geometrical parameters on the labyrinth–honeycomb seal performance. Proc Inst Mech Eng G J Aerosp Eng 2018;232:362–73. https://doi.org/10.1177/0954410017742227.Search in Google Scholar

9. Androsovich, I, Borovikov, D, Siluyanova, M. Analysis of the geometric parameters influence on the labyrinth seals performance. J Phys: Conf Series. IOP Publish 2021;1925:012075. https://doi.org/10.1088/1742-6596/1925/1/012075.[C]//Search in Google Scholar

10. Liu, H, Li, G, Kang, C, Ruan, Y, Wang, R, Lu, X. Optimization of smooth straight-through labyrinth seal based on XGBoost and improved genetic algorithm. J Eng Gas Turbines Power 2025;147. https://doi.org/10.1115/1.4066357.Search in Google Scholar

11. Zhang, ZZ, Zhang, X, Xu, DC, Ren, G, Zhang, H, et al.. Optimization of leakage characteristics of labyrinth seals with multiple structural parameters based on genetic algorithm. Lubric Seal 1–11. Available from: http://kns.cnki.net/kcms/detail/44.1260.TH.20250122.1047.002.html.Search in Google Scholar

12. Wang, J, Zheng, X. Review of geometric uncertainty quantification in gas turbines. J Eng Gas Turbines Power 2020;142:070801. https://doi.org/10.1115/1.4047179.Search in Google Scholar

13. Najm, HN. Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics. Annu Rev Fluid Mech 2009;41:35–52. https://doi.org/10.1146/annurev.fluid.010908.165248.Search in Google Scholar

14. Ji, T, Chu, W. Uncertainty quantification of blade geometric deviation on compressor stability. Aircraft Eng Aero Technol, 2024;96:257–64. https://doi.org/10.1108/aeat-06-2023-0163.Search in Google Scholar

15. Li, Y, Chu, WL, Ji, TY. Uncertainty study on the impact of blade installation angle deviation on rotor blade performance . J Xi’an Jiaot Univ 2023;57:49–59.Search in Google Scholar

16. Xu, BS, Yang, XM, Zou, AC, Zang, CP. Efficient metamodeling and uncertainty propagation for rotor systems by sparse polynomial chaos expansion. Probab Eng Mech 2025;79:103723. https://doi.org/10.1016/j.probengmech.2024.103723.Search in Google Scholar

17. Zhou, Y, Lu, Z. An enhanced Kriging surrogate modeling technique for high-dimensional problems. Mech Syst Signal Process 2020;140:106687. https://doi.org/10.1016/j.ymssp.2020.106687.Search in Google Scholar

18. Zhao, H, Gao, Z, Xu, F, Xia, L. Adaptive multi-fidelity sparse polynomial chaos-Kriging metamodeling for global approximation of aerodynamic data. Struct Multidiscip Optim, 2021;64:829–58. https://doi.org/10.1007/s00158-021-02895-2.Search in Google Scholar

19. Weinmeister, J, Gao, X, Roy, S. Analysis of a polynomial chaos-kriging metamodel for uncertainty quantification in aerodynamics[J]. AIAA J 2019;57: 2280–96, https://doi.org/10.2514/1.j057527.Search in Google Scholar

20. Zhao, H, Gao, ZH, Xia, L. Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model. Comput Fluid 2022;246:105643.https://doi.org/10.1016/j.compfluid.2022.105643.Search in Google Scholar

21. Xu, D, Zhang, X, Zhang, Z, Zhang, H, Ren, G. Optimization of labyrinth seal leakage with independently varied tooth parameters using efficient global optimization. Int J Turbo Jet Engines 2025. https://doi.org/10.1515/tjj-2025-0004.Search in Google Scholar

22. Schramm, V, Denecke, J, Kim, S, Wittig, S. Shape optimization of a labyrinth seal applying the simulated annealing method. Int J Rotating Mach 2004;10:365–71, https://doi.org/10.1155/s1023621x04000375.Search in Google Scholar

23. Wittig, S, Schelling, U, Kim, S. Numerical predictions and measurements of discharge coefficients in labyrinth seals. Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers 1987;79238:V001T01A064.10.1115/87-GT-188Search in Google Scholar

24. Wróblewski, W, Frączek, D, Marugi, K. Leakage reduction by optimisation of the straight–through labyrinth seal with a honeycomb and alternative land configurations. Int J Heat Mass Tran 2018;126:725–39. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.070.Search in Google Scholar

25. Lin, Z, Wang, X, Yuan, X, Shibukawa, N, Noguchi, T. Investigation and improvement of the staggered labyrinth seal. Chin J Mech Eng 2015;28:402–8. https://doi.org/10.3901/cjme.2015.0106.005.Search in Google Scholar

26. Blatman, G, Sudret, B. Adaptive sparse polynomial chaos expansion based on least angle regression[J]. J Comput Phys 2011;230:2345–67, https://doi.org/10.1016/j.jcp.2010.12.021.Search in Google Scholar

27. Soize, C, Ghanem, R. Physical systems with random uncertainties: chaos representations with arbitrary probability measure. SIAM J Sci Comput 2004;26:395–410, https://doi.org/10.1137/s1064827503424505.Search in Google Scholar

28. Efron, B, Hastie, T, Johnstone, I, Tibshirani, R. Least angle regression. Ann Statist 2004;32:409–99. https://doi.org/10.1214/009053604000000067.Search in Google Scholar

29. Cressie, N. The origins of kriging[J]. Math Geol 1990;22:239–52, https://doi.org/10.1007/bf00889887.Search in Google Scholar

30. Schöbi, R, Sudret, B. PC-Kriging: a new metamodelling method combining Polynomial Chaos Expansions and Kriging. In: Proc. 2nd int. Symposium on uncertainty quantification and stochastic modeling. France: Rouen; 2014.Search in Google Scholar

31. Meng, DJ, Shi, WB, Zhang, HG, et al.. Uncertainty analysis of the impact of manufacturing errors on the performance of a multistage compressor. J Aero Power 2025:1–13. https://doi.org/10.13224/j.cnki.jasp.20240553.Search in Google Scholar

32. Gao, LM, Yang, G, Wang, HH, et al.. Impact of leading-edge out-of-tolerance on the performance consistency of a high-subsonic cascade under random manufacturing deviations. Acta Aerodyn Sin 2024;42:109–18.Search in Google Scholar

33. Park, JS. Optimal Latin-hypercube designs for computer experiments. J Stat Plann Inference 1994;39:95–111, https://doi.org/10.1016/0378-3758(94)90115-5.Search in Google Scholar

34. He, DH. Sequential optimization method based on kriging surrogate model and its application. Shanghai: East China University of Science and Technology; 2015.Search in Google Scholar

35. Chen, L, Xu, Z, Huang, D, Chen, Z. An improved Sobol sensitivity analysis method[C]. J Phys: Conf Series. IOP Publish 2024;2747:012025. https://doi.org/10.1088/1742-6596/2747/1/012025.Search in Google Scholar

Received: 2025-04-10
Accepted: 2025-05-19
Published Online: 2025-06-02

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 30.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/tjj-2025-0033/html
Scroll to top button