Advertisement

Structural and Multidisciplinary Optimization

, Volume 59, Issue 2, pp 659–673 | Cite as

Reliability based multidisciplinary design optimization of cooling turbine blade considering uncertainty data statistics

  • Lei LiEmail author
  • Huan Wan
  • Wenjing Gao
  • Fujuan Tong
  • Honglin Li
Industrial Application
  • 208 Downloads

Abstract

Considering the coupling among aerodynamic, heat transfer and strength, a reliability based multidisciplinary design optimization method for cooling turbine blade is introduced. Multidisciplinary analysis of cooling turbine blade is carried out by sequential conjugated heat transfer analysis and strength analysis with temperature and pressure interpolation. Uncertainty data including the blade wall, rib thickness, elasticity Modulus and rotation speed is collected. Data statistics display the probability models of uncertainty data follow three-parameter Weibull distribution. The thickness of blade wall, thickness and height of ribs are chosen as design variables. Kriging surrogate model is introduced to reduce time-consuming multidisciplinary reliability analysis in RBMDO loop. The reliability based multidisciplinary design optimization of a cooling turbine blade is carried out. Optimization results shows that the RBMDO method proposed in this work improves the performance of cooling turbine blade availably.

Keywords

Cooling turbine blade Reliability based multidisciplinary design optimization Kriging surrogate model Uncertainty data statistics 

Notes

Acknowledgments

National Natural Science Foundation of China (Grant No. 51575444), Aerospace Science and Technology Foundation (Grant No. 2017-HT-XGD), Aviation Power Foundation (Grant No. 6141B090319) support this work.

References

  1. Ahn J, Kwon JH (2006) An efficient strategy for reliability-based multidisciplinary design optimization using BLISS. Struct Multidiscip Optim 31(5):363–372CrossRefGoogle Scholar
  2. Bejan A (2013) Convection heat transfer. Wiley, HobokenCrossRefzbMATHGoogle Scholar
  3. Chen QF, Ma XB (2012) Research of aircraft SHP method based on changed loading spectrum. Prognostics and system health management (PHM), 2012 IEEE conference on. IEEE 2012: 1–5Google Scholar
  4. Chen ZZ, Qiu HB, Gao L, Li XK, Li PG (2014) A local adaptive sampling method for reliability-based design optimization using kriging model. Struct Multidiscip Optim 49(3):401–416MathSciNetCrossRefGoogle Scholar
  5. Choi SM, Park JS, Chung H, Park S, Cho HH (2017) Upstream wake effect on flow and heat transfer characteristics at an endwall of first-stage blade of a gas turbine. Exp Thermal Fluid Sci 86:23–36CrossRefGoogle Scholar
  6. Deng S, Suresh K (2017a) Stress constrained thermo-elastic topology optimization with varying temperature fields via augmented topological sensitivity based level-set. Struct Multidiscip Optim 56(6):1413–1427MathSciNetCrossRefGoogle Scholar
  7. Deng S, Suresh K (2017b) Topology optimization under thermo-elastic buckling. Struct Multidiscip Optim 55(5):1759–1772MathSciNetCrossRefGoogle Scholar
  8. Du X, Guo J, Beeram H (2008) Sequential optimization and reliability assessment for multidisciplinary systems design. Struct Multidiscip Optim 35(2):117–130MathSciNetCrossRefzbMATHGoogle Scholar
  9. Fei C, Bai G (2012) Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation. Propulsion and Power Research 1(1):58–63CrossRefGoogle Scholar
  10. Gao HF, Fei CW, Bai GC, Ding L (2016) Reliability-based low-cycle fatigue damage analysis for turbine blade with thermo-structural interaction. Aerospace Science and Technology 49: 289-300Google Scholar
  11. Garg VK, Ameri AA (2001) Two-equation turbulence models for prediction of heat transfer on a transonic turbine blade. Int J Heat Fluid Flow 22(6):593–602CrossRefGoogle Scholar
  12. Gupta AK, Haider MR (2014) Creep Life Estimation of Low Pressure Reaction Turbine Blade. International Journal of Technological Exploration and Learning (IJTEL) 3(2):402-404Google Scholar
  13. Huang HZ, Huang CG, Peng Z, Li YF, Yin H (2017) Fatigue life prediction of fan blade using nominal stress method and cumulative fatigue damage theory. International Journal of Turbo & Jet-Engines.  https://doi.org/10.1515/tjj-2017-0015
  14. Huang ZL, Zhou YS, Jiang C, Zheng L, Han X (2018) Reliability-based multidisciplinary design optimization using incremental shifting vector strategy and its application in electronic product design. Acta Mech Sinica 34(2):285–302Google Scholar
  15. Hui F, Weiji L (2008) An efficient method for reliability-based multidisciplinary design optimization. Chin J Aeronaut 21(4):335–340CrossRefGoogle Scholar
  16. Li XK, Qiu HB, Chen ZZ, Gao L, Shao XY (2016) A local kriging approximation method using MPP for reliability-based design optimization. Comput Struct 162:102–115CrossRefGoogle Scholar
  17. Liu CJ, Peng JQ (2015) Four hot corrosion resistant materials for IGT blades. Procedia Engineering 130:662–667CrossRefGoogle Scholar
  18. Meng D, Li YF, Huang HZ, Zhang X, Liu Y (2015) Reliability-based multidisciplinary design optimization using subset simulation analysis and its application in the hydraulic transmission mechanism design. J Mech Des 137(5):051402CrossRefGoogle Scholar
  19. Menter FR (1994) Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 32(8):1598–1605CrossRefGoogle Scholar
  20. Nikbay M, Kuru MN (2013) Reliability based multidisciplinary optimization of aeroelastic systems with structural and aerodynamic uncertainties. J Aircr 50(3):708–715CrossRefGoogle Scholar
  21. Park HW, Kim MS, Choi DH, Mavris DN (2002) Optimizing the Parallel Process Flow for the Individual Discipline Feasible Method. 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization: 5411Google Scholar
  22. Perez R, Liu H, Behdinan K (2004) Evaluation of multidisciplinary optimization approaches for aircraft conceptual design. 10th AIAA/ISSMO multidisciplinary analysis and optimization conference: 4537Google Scholar
  23. Roshanian J, Ebrahimi M (2013) Latin hypercube sampling applied to reliability-based multidisciplinary design optimization of a launch vehicle. Aerosp Sci Technol 28(1):297–304CrossRefGoogle Scholar
  24. Saad L, Aissani A, Chateauneuf A, Raphael W (2016) Reliability-based optimization of direct and indirect LCC of RC bridge elements under coupled fatigue-corrosion deterioration processes. Eng Fail Anal 59:570–587CrossRefGoogle Scholar
  25. Sellar R, Batill S, Renaud J (1996) Response surface based, concurrent subspace optimization for multidisciplinary system design. 34th Aerospace Sciences Meeting and Exhibit: 714Google Scholar
  26. Song LK, Fei CW, Wen J, Bai GC (2017a) Multi-objective reliability-based design optimization approach of complex structure with multi-failure modes. Aerosp Sci Technol 64:52–62CrossRefGoogle Scholar
  27. Song L, Zhu P, Li J, Feng Z (2017b) Effect of purge flow on endwall flow and heat transfer characteristics of a gas turbine blade. Appl Therm Eng 110:504–520CrossRefGoogle Scholar
  28. Stocki R (2005) A method to improve design reliability using optimal Latin hypercube sampling. Comput Assist Mech Eng Sci 12(4):393Google Scholar
  29. Wang L, Wang S, Wen F, Zhou X, Wang Z (2018) Effects of continuous wavy ribs on heat transfer and cooling air flow in a square single-pass channel of turbine blade. Int J Heat Mass Transf 121:514–533CrossRefGoogle Scholar
  30. Wang P, Li Y, Zou Z, Wang L, Song S (2014) Influence of turbulence model parameter settings on conjugate heat transfer simulation. Heat Mass Transf 50(4):521–532CrossRefGoogle Scholar
  31. Wang XH, Li RJ, Xia RW (2013) Comparison of MDO methods for an Earth observation satellite. Procedia Engineering 67:166–177CrossRefGoogle Scholar
  32. Wong CN, Huang HZ, Li N (2013) Fourier series based reliability analysis of aeroengine turbine blade under linear fuzzy safety state. Eng Fail Anal 31:268–280CrossRefGoogle Scholar
  33. Yang F, Yue Z (2014) Kernel density estimation of three-parameter Weibull distribution with neural network and genetic algorithm. Appl Math Comput 247:803–814MathSciNetzbMATHGoogle Scholar
  34. Yao W, Chen X, Luo W, Tooren MV, Guo J (2011) Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Prog Aerosp Sci 47(6):450–479CrossRefGoogle Scholar
  35. Zhang MC, Gou WX, Li L, Wang XM, Yue ZF (2016) Multidisciplinary design and optimization of the twin-web turbine disk. Struct Multidiscip Optim 53(5):1129–1141CrossRefGoogle Scholar
  36. Zhou H, Jiang P, Shao X, Yi Y (2014) An improved bi-level integrated system collaborative optimization method for multidisciplinary design optimization. Modelling, Identification & Control (ICMIC), 2014 Proceedings of the 6th International Conference on IEEE: 365–370Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Lei Li
    • 1
    Email author
  • Huan Wan
    • 1
  • Wenjing Gao
    • 1
  • Fujuan Tong
    • 1
  • Honglin Li
    • 1
  1. 1.Department of Engineering MechanicsNorthwestern Polytechnincal UniversityXi’anChina

Personalised recommendations