Open Access
Issue
JNWPU
Volume 43, Number 3, June 2025
Page(s) 447 - 456
DOI https://doi.org/10.1051/jnwpu/20254330447
Published online 11 August 2025
  1. GARZON V E, DARMOFAL D L. Impact of geometric variability on axial compressor performance[R]. ASME GT-2003- 38130, 2003 [Google Scholar]
  2. GARZON V E. Probabilistic aerothermal design of compressor airfoils[D]. Massachusetts: Massachusetts Institute of Technology, 2003: 27–44 [Google Scholar]
  3. LAMB C T. Performance-based geometric tolerancing of compressor blades[R]. ASME GT-2004-53592, 2004 [Google Scholar]
  4. LANGE A, VOGELER K, GUMMER V, et al. Introduction of a parameter based compressor blade model for considering measured geometry uncertainties in numerical simulation[R]. ASME GT-2009-59937, 2004 [Google Scholar]
  5. GIEBMANNS A, BACKHAUS J, FREY C, et al. Compressor leading-edge sensitivities and analysis with an adjoint flow solver[R]. ASME GT-2013-94427, 2013 [Google Scholar]
  6. WUNSCH D, HIRSCH C, NIGRO R, et al. Quantification of combined operational and geometrical uncertainties in turbomachinery design[R]. ASME GT-2015-43399, 2015 [Google Scholar]
  7. WANG J, WANG B, YANG H, et al. Compressor geometric uncertainty quantification under conditions from near choke to near stall[J]. Chinese Journal of Aeronautics, 2023, 36(3): 16–29 [Google Scholar]
  8. MA C, GAO L, WANG H, et al. Influence of leading-edge with real manufacturing error on aerodynamic performance of high subsonic compressor cascades[J]. Chinese Journal of Aeronautics, 2021, 34(6): 220–232 [Google Scholar]
  9. JI Tianyuan, CHU Wuli, LI Qinghan, et al. Research blade geometric deviation affecting the compressor performance[C]//The Seventh Joint Conference on Air and Space Power and the 43rd Technical Exchange Meeting of the Third Specialized Information Network of China Aerospace, 2023 (in Chinese) [Google Scholar]
  10. JI Tianyuan, CHU Wuli, ZHANG Haoguang, et al. Uncertainty quantification of real stagger angle deviation affecting compressor performance[J]. Journal of Aerospace Power, 2024, 39: 20220858 (in Chinese) [Google Scholar]
  11. MA C, GAO L, CAI Y, et al. Robust optimization design of compressor blade considering machining error[R]. ASME GT-2017-63157, 2017 [Google Scholar]
  12. LI Zhihui. A study on robust optimization of highly loaded compressor blade-end considering fine-scale geometry deformations[D]. Beijing: Beijing Institute of Technology, 2018 (in Chinese) [Google Scholar]
  13. LANGE A, VOIGT M, VOGELER K, et al. Principal component analysis on 3D scanned compressor blades for probabilistic CFD simulation[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2012 [Google Scholar]
  14. 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 [Google Scholar]
  15. LUO Jiaqi, ZHU Yalu, LIU Feng. Aerodynamic sensitivity analysis for manufacturing variations of a turbine blade by an adjoint method[J]. Journal of Engineering Thermophysics, 2017, 38(3): 498–503 (in Chinese) [Google Scholar]
  16. LUO J, FENG L. Statistical evaluation of performance impact of manufacturing variability by an adjoint method[J]. Aerospace Science and Technology, 2018, 77: 471–484 [Google Scholar]
  17. CAI Yutong, GAO Limin, MA Chi, et al. Uncertainty quantification on compressor blade considering manufacturing error based on NIPC method[J]. Journal of Engineering Thermophysics, 2017, 38(3): 490–497 (in Chinese) [Google Scholar]
  18. WANG Xiaojing, ZOU Zhengping. Uncertainty analysis of impact of profile geometric manufacture variations on turbine blade performance in stage environment[J]. Journal of Propulsion Technology, 2022, 43(3): 112–119 (in Chinese) [Google Scholar]
  19. JU Y, LIU Y, JIANG W, et al. Aerodynamic analysis and design optimization of a centrifugal compressor impeller considering realistic manufacturing uncertainties[J]. Aerospace Science and Technology, 2021, 115: 106787 [Google Scholar]
  20. HE X, ZHENG X. Performance improvement of transonic centrifugal compressors by optimization of complex three-dimensional features[J]. Journal of Aerospace Engineering, 2017, 231(14): 2723–2738 [Google Scholar]
  21. LIU B, LIU J, YU X, et al. A novel decomposition method for manufacture variations and the sensitivity analysis on compressor blades[J]. Aerospace, 2022, 9: 542 [Google Scholar]
  22. CHEN H, WANG Q, HU R, et al. Conditional sampling and experiment design for quantifying manufacturing error of transonic airfoil[R]. AIAA-2011-658, 2011 [Google Scholar]
  23. GAO Limin, CAI Yutong, XU Haoliang, et al. Uncertainty analysis of machining error influence of compressor blade[J]. Journal of Aerospace Power, 2017, 32(9): 2253–2259 (in Chinese) [Google Scholar]
  24. Aviation Industry Corporation of China. HB5647 Blade profile marking, tolerance and blade surface roughness [S]. HB5647-1998, 1999 [Google Scholar]
  25. DOW E A, WANG Q. Simultaneous robust design and tolerancing of compressor blades[R]. ASME GT-2014-25795, 2014 [Google Scholar]
  26. GUO Zhengtao, CHU Wuli, YAN Song, et al. Data mining on effects of manufacturing error on aerodynamic performance and stability of a compressor cascade[J]. Journal of Propulsion Technology, 2022, 43(3): 141–153 (in Chinse) [Google Scholar]
  27. LU Qing. Qualitative influence of machining deviation on critical attack angle of cascade[D]. Dalian: Dalian Maritime University, 2024 (in Chinese) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.