Skip to main content

Advertisement

SpringerLink
Log in

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

An Erratum to this article was published on 08 August 2016

Abstract

To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine blade-tip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Gole, N., Kumar, A., Narasimhan, V.: Health risk assessment and prognosis of gas turbine blades by simulation and statistical methods [C]. In: Canadian Conference on Electrical and Computer Engineering, pp. 1087–1092. Niagara Falls, Canada (2008)

  2. Bai, G.C., Fei, C.W.: Distributed collaborative response surface method for mechanical dynamic assembly reliability design. Chin. J. Mech. Eng. 26, 1160–1168 (2013)

    Article  Google Scholar 

  3. Lattime, S.B., Steinetz, B.M.: Turbine engine clearance control systems: current practices and future directions. J. Propuls. Power 20, 302–311 (2004)

    Article  Google Scholar 

  4. Lattime, S.B., Steinetz, B.M., Robbie, M.G.: Test rig for evaluating active turbine blade tip clearance control concepts. J Propuls. Power 21, 552–563 (2005)

    Article  Google Scholar 

  5. NSSA Glenn Research Center: HTP Clearance Control, NASA/CR-2005-213970 (2005)

  6. Jia, B.H., Zhang, X.D.: Study on effect of rotor vibration on tip clearance variation and fast active control of tip clearance. Adv. Mater. Res. 139–141, 2469–2472 (2010)

    Article  Google Scholar 

  7. Annette, E.N., Christoph, W.M., Stehan, S.: Modeling and validation of the thermal effects on gas turbine transients. J. Eng. Gas Turbines Power 127, 564–572 (2005)

    Article  Google Scholar 

  8. Forssell, L.S.: Flight clearance analysis using global nonlinear optimisation-based search algorithms. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, pp. 1–8. Austin, Texas (2003)

  9. Lattime, S.B., Steinetz, B.M.: Turbine engine clearance control systems: current practices and future directions. NASA/TM-2002-211794 (2002)

  10. Kypuros, J.A., Melcher, K.J.: A reduced model for prediction of thermal and rotational effects on turbine tip clearance. NASA/TM–2003–212226 (2003)

  11. Annette, E.N., Christoph, W.M., Stephan, S.: Modelling and validation of the thermal effects on gas turbine transients. J. Eng. Gas Turbines Power 127, 564–572 (2005)

    Article  Google Scholar 

  12. Hu, D.Y., Wang, R.Q., Tao, Z.: Probabilistic design for turbine disk at high temperature. Aircr. Eng. Aerosp. Technol. 83, 199–207 (2011)

    Article  Google Scholar 

  13. Reed, D.A.: Reliability of multi-component assemblages. Reliab. Eng. Syst. Saf. 27(2), 167–178 (1990)

    Article  Google Scholar 

  14. Li, D.M., Zhang, X.M., Guan, Y.S.: Multi-objective topology optimization of thermo-mechanical compliant mechanisms. Chin. J. Mech. Eng. 24, 1123–1129 (2011)

    Article  Google Scholar 

  15. Lv, Q., Low, B.K.: Probabilistic analysis of underground rock excavations using response surface method and SORM. Comput. Geotech. 38, 1008–1021 (2011)

    Article  Google Scholar 

  16. Murat, E.K., Hasan, B.B., Aledar, B.: Probabilistic nonlinear analysis of CFR dams by MCS using response surface method. Appl. Math. Model. 35, 2752–2770 (2011)

    Article  Google Scholar 

  17. Fitzpatrick, C.K., Baldwin, M.A., Rullkoetter, P.J., et al.: Combined probabilistic and principal component analysis approach for multivariate sensitivity evaluation and application to implanted patellofemoral mechanics. J. Biomech. 44, 13–21 (2011)

    Article  Google Scholar 

  18. Alessandro, Z., Michele, B., Andrea, D.A., et al.: Probabilistic analysis for design assessment of continuous steel concrete composite girders. J. Constr. Steel Res. 66, 897–905 (2010)

    Article  Google Scholar 

  19. Toshiya, N., Kenji, F.: Pobabilistic transient thermal analysis of an atmospheric reentry vehicle structure. Aerosp. Sci. Technol. 10, 346–354 (2006)

    Article  Google Scholar 

  20. Sung, E.C.: Probabilistic stability analyses of slopes using the ANN-based response surface. Comput. Geotechn. 36, 787–797 (2009)

    Article  Google Scholar 

  21. Eom, Y.S., Yoo, K.S., Park, J.Y.: Reliability-based topology optimization using a standard response surface method for three- dimensional structures. Struct. Multidiscipl. Optim. 43, 287–295 (2011)

    Article  MATH  Google Scholar 

  22. Zhang, C.Y., Bai, G.C.: Extremum response surface method of reliability analysis on two-link flexible robot manipulator. J. Cent. South Univ. 19, 101–107 (2012)

    Article  Google Scholar 

  23. Pellissetti, M.F., Schueller, G.I., Pradlwarter, H.J., et al.: Reliability analysis of spacecraft structures under static and dynamic loading. Comput. Struct. 84(21), 1313–1325 (2006)

    Article  Google Scholar 

  24. Huang, Z.J., Wang, C.G., Chen, J.: Optimal design of aeroengine turbine disc based on Kriging surrogate models. Comput. Struct. 89, 27–37 (2011)

    Article  Google Scholar 

  25. Tan, X.H., Bi, W.H., Hou, X.L., et al.: Reliability analysis using radial basis function networks and support vector machines. Comput. Geotech. 38, 178–186 (2011)

    Article  Google Scholar 

  26. Guo, Z.W., Bai, G.C.: Application of least squares support vector machine for regression to reliability analysis. Chin. J. Aeronaut. 22, 160–166 (2009)

    Article  Google Scholar 

  27. Fei, C.W., Bai, G.C.: Nonlinear dynamic probabilistic analysis for turbine casing radial deformation using extremum response surface method based on support vector machine. J. Comput. Nonlinear Dyn. 8, 041004 (2013)

    Article  Google Scholar 

  28. Fei, C.W., Bai, G.C.: Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression. Mech. Syst. Signal Process. 49, 196–208 (2014)

    Article  Google Scholar 

  29. Basudhar, A., Missoum, S.: Adaptive explicit decision functions for probabilistic design and optimization using support vector machines. Comput. Struct. 86, 1904–1917 (2008)

  30. Tian, J., Gu, H.: Anomaly detection combining one-class SVMs and particle swarm optimization algorithms. Nonlinear Dyn. 61, 303–310 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  31. Tian, J., Gu, H., Gao, C.Y.: Local density one-class support vector machines for anomaly detection. Nonlinear Dyn. 64, 127–130 (2011)

    Article  Google Scholar 

  32. Guo, Z.K., Guan, X.P.: Nonlinear generalized predictive control based on online least squares support vector machines. Nonlinear Dyn. 79, 1163–1168 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  33. Fei, C.W., Bai, G.C.: Distributed collaborative extremum response surface method for mechanical dynamic assembly reliability analysis. J. Cent. South Univ. 20, 2414–2422 (2013)

    Article  Google Scholar 

  34. Fei, C.W., Bai, G.C.: Wavelet correlation feature scale entropy and fuzzy support vector machine approach for aeroengine whole-body vibration fault diagnosis. Shock Vib. 20, 341–349 (2013)

    Article  Google Scholar 

  35. Fei, C.W., Bai, G.C.: Study on extremum selection method of random variable for nonlinear dynamic reliability analysis. Propuls. Power Res. 1, 58–63 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

The paper is co-supported by the National Natural Science Foundation of China (Grant No. 51275024), General Research Grant from Hong Kong SAR Government (Grant No. 514013(B-Q39B)), the Foundation of Hong Kong Scholars Program (Grant Nos. G-YZ290 and XJ2015002) and the Funding of Hong Kong Scholars Program (XJ2015002). The authors would like to thank them.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China

    Cheng-Wei Fei & Yat-Sze Choy

  2. School of Energy and Power Engineering, Beijing University of Aeronautics and Astronautics, Beijing, 100191, People’s Republic of China

    Cheng-Wei Fei, Dian-Yin Hu & Guang-Chen Bai

  3. School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, 100191, People’s Republic of China

    Wen-Zhong Tang

Authors
  1. Cheng-Wei Fei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yat-Sze Choy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dian-Yin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guang-Chen Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wen-Zhong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng-Wei Fei.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s11071-016-2950-7.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fei, CW., Choy, YS., Hu, DY. et al. Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance. Nonlinear Dyn 86, 205–223 (2016). https://doi.org/10.1007/s11071-016-2883-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-016-2883-1

Keywords

Search

Navigation