Numerical and experimental study on the critical geometric variation based on sensitivity analysis on a compressor rotor

Yan Wang; Mingmin Zhu; Songan Zhang; Xiaoqing Qiang; Biaojie Zheng; Jinfang Teng

doi:10.1515/tjj-2023-0097

Article

Numerical and experimental study on the critical geometric variation based on sensitivity analysis on a compressor rotor

Yan Wang , Mingmin Zhu , Songan Zhang , Xiaoqing Qiang , Biaojie Zheng and Jinfang Teng

Published/Copyright: June 24, 2024

Published by

Become an author with De Gruyter Brill

Author Information

From the journal International Journal of Turbo & Jet-Engines Volume 42 Issue 1

Abstract

Different types of geometric variations often appear coupledly in manufactured blades. It is desired to identify the ones that have the strongest impact on the performance. In this paper, the influence of multiple geometric variations on the compressor rotor blade at the design point is studied. Two hundred and nine varied blades are constructed by adding variation data to the design-intent blade and assessed using steady-state Reynolds-Averaged Navier–Stokes (RANS) simulations. It is shown that the region near the lower and upper end of the blade is more sensitive to geometric variations. Spearman’s rank correlation coefficient is used to measure the sensitivity of rotor performance to geometric variations. The results show that the stagger angle variation is the most influencing variation. Local sensitivity analysis at different spans also shows that the stagger angle variation is an important geometric variation that needs more attention during blade manufacturing. Also, the influence of geometric variations can reach further than nearby regions. Consequently, experiments on linear cascades at different spans of the rotor are carried out to study the effect of stagger angle on flow characteristics. Results show that the stagger angle variation could lead to different changes in performance depending on the specific cascade profile.

Keywords: cascade; compressor; geometric variation; stagger angle; uncertainty quantification

Corresponding author: Mingmin Zhu, School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Rd. Dongchuan, Minhang District, Shanghai, China, E-mail: jackzhushen@sjtu.edu.cn

Funding source: Natural Science Foundation of China

Award Identifier / Grant number: (No. 52376027)

Funding source: Fundamental Research Funds for the Central Universities

Funding source: Shanghai Municipal Education Commission

Award Identifier / Grant number: (No. 2023-02-7)

Funding source: the Natural Science Foundation of Shanghai

Award Identifier / Grant number: (23ZR1435400)

Funding source: Aeronautical Science Foundation of China

Award Identifier / Grant number: (2019ZB057006)

Acknowledgments

The authors gratefully acknowledge the support of the Natural Science Foundation of China (No. 52376027), the Fundamental Research Funds for the Central Universities, the Shanghai Municipal Education Commission (No. 2023-02-7), the Natural Science Foundation of Shanghai (23ZR1435400), Aeronautical Science Foundation of China (2019ZB057006), and the United Innovation Center (UIC) of Aerothermal Technologies for Turbomachinery.

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. Detailed information about the authors’ contributions are as follows: Yan Wang: Investigation (lead); Methodology (lead); Writing – original draft (lead); Writing – review & editing (lead). Mingmin Zhu: Project administration (lead); Experimental (lead); Supervision (lead); Writing – review & editing (supporting). Xiaoqing Qiang: Validation (lead); Experimental (lead); Supervision (supporting). Biaojie Zheng: Validation (supporting); Writing – review & editing (supporting). Jinfang Teng: Supervision (supporting).
Competing interests: The authors state no conflict of interest.
Research funding: Natural Science Foundation of China (No. 52376027); Fundamental Research Funds for the Central Universities; Shanghai Municipal Education Commission (No. 2023-02-7); the Natural Science Foundation of Shanghai (23ZR1435400); Aeronautical Science Foundation of China (2019ZB057006).
Data availability: The raw data can be obtained on request from the corresponding author.

Nomenclature

English symbols

c _p: Pressure coefficient
i: Incidence angle (°)
m ˙: Mass flow rate (kg/s)
ΔP: Profile tolerance (mm)
std: Standard deviation
ΔY: Circumferential position tolerance (mm)
ΔZ: Axial position tolerance (mm)

Greek symbols

β: Stagger angle (°)
γ: Stagger angle (°)
μ: Mean value
π: Total pressure ratio
ρ: Spearman’s rank correlation coefficient
ω ‾: Total pressure loss coefficient
σ: Total pressure recovery coefficient

Subscripts

1: Inlet
2: Outlet
*: Total parameter
b: Bottom
m: Mid
rel: Relative
t: Top

References

1. Roberts, WB, Armin, A, Kassaseya, G, Suder, KL, Thorp, SA, Strazisar, AJ. The effect of variable chord length on transonic axial rotor performance. J Turbomach 2002;124:351–7. https://doi.org/10.1115/1.1459734.Search in Google Scholar

2. Goodhand, MN, Miller, RJ. Compressor leading edge spikes: a new performance criterion. J Turbomach 2011;133:021006.10.1115/1.4000567Search in Google Scholar

3. Goodhand, MN, Miller, RJ, Lung, HW. The impact of geometric variation on compressor two-dimensional incidence range. J Turbomach 2015;137:021007. https://doi.org/10.1115/1.4028355.Search in Google Scholar

4. Garzon, VE. Probabilistic aerothermal design of compressor airfoils. Massachusetts: Massachusetts Institute of Technology; 2003.Search in Google Scholar

5. Lange, A, Vogeler, K, Gümmer, V, Schrapp, H, Clemen, C. Introduction of a parameter based compressor blade model for considering measured geometry uncertainties in numerical simulation. Turbo Expo Power Land Sea Air 2009;6:1113–23. https://doi.org/10.1115/gt2009-59937.Search in Google Scholar

6. Lange, A, Voigt, M, Vogeler, K, Schrapp, H, Johann, E, Gümmer, V. Probabilistic CFD simulation of a high-pressure compressor stage taking manufacturing variability into account. Turbo Expo Power Land Sea Air 2010;6:617–28. https://doi.org/10.1115/gt2010-22484.Search in Google Scholar

7. Heinze, K, Friedl, WH, Vogeler, K, Voigt, M. Probabilistic HCF-investigation of compressor blades. Turbo Expo Power Land Sea Air 2009;6:1103–12. https://doi.org/10.1115/gt2009-59899.Search in Google Scholar

8. Lange, A, Voigt, M, Vogeler, K, Schrapp, H, Johann, E, Gümmer, V. Impact of manufacturing variability and nonaxisymmetry on high-pressure compressor stage performance. J Eng Gas Turbines Power 2012;134:032504. https://doi.org/10.1115/1.4004404.Search in Google Scholar

9. Parussini, L, Pediroda, V, Poloni, C. Prediction of geometric uncertainty effects on fluid dynamics by polynomial chaos and fictitious domain method. Comput Fluids 2010;39:137–51. https://doi.org/10.1016/j.compfluid.2009.07.008.Search in Google Scholar

10. Luo, J, Liu, F. Statistical evaluation of performance impact of manufacturing variability by an adjoint method. Aero Sci Technol 2018;77:471–84. https://doi.org/10.1016/j.ast.2018.03.030.Search in Google Scholar

11. Tang, X, Luo, J, Liu, F. Adjoint aerodynamic optimization of a transonic fan rotor blade with a localized two-level mesh deformation method. Aero Sci Technol 2018;72:267–77. https://doi.org/10.1016/j.ast.2017.11.015.Search in Google Scholar

12. Schmidt, R, Voigt, M, Vogeler, K, Meyer, M. Comparison of two methods for sensitivity analysis of compressor blades. J Turbomach 2017;139:111006. https://doi.org/10.1115/1.4037127.Search in Google Scholar

13. Lejon, M, Andersson, N, Ellbrant, L, Mårtensson, H. The impact of manufacturing variations on performance of a transonic axial compressor rotor. J Turbomach 2020;142:081009. https://doi.org/10.1115/1.4046617.Search in Google Scholar

14. Liu, B, Liu, J, Yu, X, An, G. A novel decomposition method for manufacture variations and the sensitivity analysis on compressor blades. Aerospace 2022;9:542. https://doi.org/10.3390/aerospace9100542.Search in Google Scholar

15. Wang, J, Wang, B, Yang, H, Sun, Z, Zhou, K, Zheng, X. Compressor geometric uncertainty quantification under conditions from near choke to near stall. Chin J Aeronaut 2023;36:16–29. https://doi.org/10.1016/j.cja.2022.10.012.Search in Google Scholar

16. Reitz, G, Schlange, S, Friedrichs, J. Design of experiments and numerical simulation of deteriorated high pressure compressor airfoils. Turbo Expo Power Land Sea Air 2016;2A:V02AT37A002. https://doi.org/10.1115/gt2016-56024.Search in Google Scholar

17. Zheng, S. Sensitivity analysis of manufacturing variability on high-pressure compressor performance. J Mech Eng 2018;54:216. https://doi.org/10.3901/jme.2018.02.216.Search in Google Scholar

18. Wang, X, Du, P, Yao, L, Zou, Z, Zeng, F. Uncertainty analysis of measured geometric variations in turbine blades and impact on aerodynamic performance. Chin J Aeronaut 2023;36:140–60. https://doi.org/10.1016/j.cja.2023.03.041.Search in Google Scholar

19. Ma, Y, Teng, J, Zhu, M, Qiang, X. Effective strategy of non-axisymmetric endwall contouring in a linear compressor cascade. Proc Inst Mech Eng Part G 2022;236:1473–87. https://doi.org/10.1177/09544100211037518.Search in Google Scholar

Received: 2023-11-08

Accepted: 2024-05-24

Published Online: 2024-06-24

Published in Print: 2025-03-26

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/tjj-2023-0097

Keywords for this article

cascade; compressor; geometric variation; stagger angle; uncertainty quantification