Hostname: page-component-669899f699-8p65j Total loading time: 0 Render date: 2025-04-27T12:21:47.245Z Has data issue: false hasContentIssue false
  • English
  • Français

Uncertainty quantification and sensitivity analysis of blade geometric deviation on compressor performance

Published online by Cambridge University Press:  01 October 2024

T.Y. Ji ,
W.L. Chu Open the ORCID record for W.L. Chu [Opens in a new window]  and
Z.T. Guo
T.Y. Ji
Affiliation:
School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, China
W.L. Chu*
Affiliation:
School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, China
Z.T. Guo
Affiliation:
School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, China
*
Corresponding author: W.L. Chu; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Carefully designing blade geometric parameters is necessary as they determine the aerodynamic performance of a rotor. However, manufacturing inaccuracies cause the blade geometric parameters to deviate randomly from the ideal design. Therefore, it is essential to quantify uncertainty and analyse the sensitivity of the blade geometric deviations on the compressor performance. This work considers a subsonic compressor rotor stage and examines samples with different geometry features using three-dimensional Reynolds-averaged Navier-Stokes simulations. A method to combine Halton sequence and non-intrusive polynomial chaos is adopted to perform the uncertainty quantitative (UQ) analysis. The Sobol’ index and Spearman correlation coefficient help analyse the sensitivity and correlation between the compressor performance and blade geometric deviations, respectively. The results show that the fluctuation amplitude of the compressor performance decreases for lower mass flow rates, and the sensitivity of the compressor performance to the blade geometrical parameters varies with the working conditions. The effects of various blade geometric deviations on the compressor performance are independent and linearly superimposed, and the combined effects of different geometric deviations on the compressor performance are small.

Keywords

machining deviationcompressor rotoruncertainty quantificationaerodynamic performancesensitivity analysis
Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1330 , December 2024 , pp. 2945 - 2963
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

An, G., Kang, J., Zou, Y., Zhang, L., Lang, J., Yuan, W. and Zhang, Q. Investigation of the unsteady flow in a transonic axial compressor adopted in the compressed air energy storage system, J. Energy Storage, 2023, 63, p 106918.CrossRefGoogle Scholar
An, G., Kang, J., Wang, L., Zhang, L., Lang, J. and Li, H. Decoupling and reconstruction analysis in a transonic axial compressor using the dynamic mode decomposition method, Phys. Fluids, 2023, 35, (8), p 084120.CrossRefGoogle Scholar
Zhang, L., Kang, J., Lang, J., An, G. and Zhang, Q. Stall evolution mechanism of a centrifugal compressor with a wide-long vaneless diffuser, J. Therm. Sci., 2024, 33, pp 899–913.CrossRefGoogle Scholar
Spieler, S., Staudacher, S., Fiola, R., Sahm, P. and Weißschuh, M. Probabilistic engine performance scatter and deterioration modeling, J. Eng. Gas Turb. Power, 2008, 130, (4), pp 042507.Google Scholar
Dow, E.A. and Wang, Q. The implications of tolerance optimization on compressor blade design, J. Turbomach., 2015, 137, (10), p 101008.CrossRefGoogle Scholar
Wang, J. and Zheng, X. Review of geometric uncertainty quantification in gas turbines, J. Eng. Gas Turb. Power, 2020, 142, (7), p 070801.Google Scholar
Bammert, K. and Sandstede, H. Influences of manufacturing tolerances and surface roughness of blades on the performance of turbines, J. Eng. Power, 1976, 98, (1), pp 29–36.CrossRefGoogle Scholar
Lange, A., Voigt, M., Vogeler, K., Schrapp, H., Johann, E. and Gümmer, V. Probabilistic CFD simulation of a high-pressure compressor stage taking manufacturing variability into account, American Society of Mechanical Engineers, ASME Turbo Expo 2010: Power for Land, Sea, and Air, 14–18 June 2010, Glasgow, UK. GT2010-22484.Google Scholar
Lange, A., Voigt, M., Vogeler, K., Schrapp, H., Johann, E. and Gümmer, V. Impact of manufacturing variability and nonaxisymmetry on high-pressure compressor stage performance, J. Eng. Gas Turb. Power, 2012, 134, (3), p 032504.Google Scholar
Lange, A., Voigt, M., Vogeler, K., Schrapp, H., Johann, E. and Gümmer, V. Impact of manufacturing variability on multistage high-pressure compressor performance, J. Eng. Gas Turb. Power, 2012, 134, (11), p 112601.CrossRefGoogle Scholar
Goodhand, M.N. and Miller, R.M. The impact of real geometries on three-dimensional separations in compressors, J. Turbomach., 2012, 134, (2), p 021007.CrossRefGoogle Scholar
Goodhand, M.N., Miller, R.M. and Lung, H.W. The impact of geometric variation on compressor two-dimensional incidence range, J. Turbomach., 2014, 137, (2), p 021007.CrossRefGoogle Scholar
Cheng, C., Wu, B., Zheng, H. and Gao, L. Effect of blade machining errors on compressor performance, Acta Aeronaut. Astronaut. Sin., 2020, 41, (2), pp 28–38. [in Chinese]Google Scholar
Dow, E.A. and Wang, Q. Simultaneous robust design and tolerancing of compressor blades, American Society of Mechanical Engineers, ASME Turbo Expo 2014: Turbine Technical Conference and Exposition, 16–20 June 2014, Düsseldorf, Germany. GT2014-25795.Google Scholar
Lamb, C.T. and David, L.D. Performance-based geometric tolerancing of compressor blades, American Society of Mechanical Engineers, ASME Turbo Expo 2004: Power for Land, Sea, and Air, 14–17 June 2004, Vienna, Austria. GT2004-53592.CrossRefGoogle Scholar
Garzon, V.E. and David, L.D. Impact of geometric variability on axial compressor performance, J. Turbomach., 2003, 125, (4), pp 692–703.CrossRefGoogle Scholar
Jonas, M., Gantner, S., Städing, J. and Friedrichs, J. A machine learning based approach of performance estimation for high-pressure compressor airfoils, American Society of Mechanical Engineers, ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, 11–15 June 2018, Oslo, Norway. GT2018-75372.Google Scholar
Marcello, A., Spinella, S. and Rinaudo, S. Stochastic response surface method and tolerance analysis in microelectronics, COMPEL, 2003, 22, (2), pp 314–327.CrossRefGoogle Scholar
Brahmachary, S., Fujio, C., Aksay, M. and Ogawa, H. Design optimization and off-design performance analysis of axisymmetric scramjet intakes for ascent flight, Phys. Fluids, 2022, 34, (3), p 036109.CrossRefGoogle Scholar
Maria, M.C., Ahlfeld, R., Rosic, B. and Montomoli, F. Uncertainty quantification of leakages in a multistage simulation and comparison with experiments, J. Fluid. Eng., 2018, 140, (2), p 021110.CrossRefGoogle Scholar
Shi, W., Chen, P., Li, X., Jing, R. and Jiang, H. Uncertainty quantification of the effects of squealer tip geometry deviation on aerothermal performance, P. I. Mech. Eng. A-J Pow., 2020, 234, (7), pp 1026–1038.Google Scholar
Guo, Z. and Chu, W. Stochastic aerodynamic analysis for compressor blades with manufacturing variability based on a mathematical dimensionality reduction method, P. I. Mech. Eng. C-J Mec., 2022, 236, (10), pp 5719–5735.Google Scholar
Guo, Z., Chu, W. and Zhang, H. Uncertainty analysis of global and local performance impact of inflow and geometric uncertainties using sparse grid-based non-intrusive polynomial chaos, P. I. Mech. Eng. A-J Pow., 2022, 236, (7), pp 1239–1256.Google Scholar
Chu, W., Ji, T., Chen, X. and Luo, B. Mechanism analysis and uncertainty quantification of blade thickness deviation on rotor performance, P. I. Mech. Eng. A-J Pow., 2023, 237, (6), pp 1188–1202.Google Scholar
Ji, T., Chu, W., Liang, C. and Meng, D. Uncertainty quantification on the influence of blade thickness deviation at different rotational speeds based on flow dissipation analysis, Phys. Fluids, 2023, 35, (6), p 066126.Google Scholar
Wang, W., Chu, W., Zhang, H. and Kuang, H. Experimental and numerical study of tip injection in a subsonic axial flow compressor, Chinese J. Aeronaut., 2017, 30, pp 907–917.CrossRefGoogle Scholar
Chi, Z., Chu, W., Zhang, Z. and Zhang, H. Research on the stability enhancement mechanism of multi-parameter interaction of casing treatment in an axial compressor rotor, P. I. Mech. Eng. G-J Aer., 2022, 236, (12), pp 2405–2419.Google Scholar
Chi, Z., Chu, W., Zhang, H., and Zhang, Z. Stall margin evaluation and data mining based multiobjective optimization design of casing treatment for an axial compressor rotor, Phys. Fluids, 2023, 35, (8), p 086117.CrossRefGoogle Scholar
Zhang, H., Liu, W., Wang, E., Wu, Y. and Yao, W. Mechanism investigation of enhancing the stability of an axial flow rotor by blade angle slots, P. I. Mech. Eng. G-J Aer., 2019, 233, (13), pp 4750–4764.Google Scholar
Denton, J.D. Some limitations of turbomachinery CFD, American Society of Mechanical Engineers, ASME Turbo Expo 2010: Power for Land, Sea, and Air, 14–18 June 2010, Glasgow, UK. GT2010-44021.Google Scholar
Luo, J. and Liu, F. Statistical evaluation of performance impact of manufacturing variability by an adjoint method, Aerosp. Sci. Technol., 2018, 77, (7), pp 471–484.CrossRefGoogle Scholar
Debusschere, B.J., Najm, H.N., Matta, A., Knio, O.M., Ghanem, R.G. and Le Maı;tre, O.P. Protein labeling reactions in electrochemical microchannel flow: Numerical simulation and uncertainty propagation, Phys. Fluids, 2003, 15, (8), pp 2238–2250.CrossRefGoogle Scholar
Chen, H., Wang, Q., Hu, R. and Constantine, P. Conditional sampling and experiment design for quantifying manufacturing error of transonic airfoil, American Institute of Aeronautics and Astronautics, 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 4–7 January 2011, Orlando, Florida. AIAA 2011-658.Google Scholar
Babacan, S.D., Molina, R. and Katsaggelos, A.K. Bayesian compressive sensing using laplace priors, IEEE T. Image Process., 2010, 19, (1), pp 53–63.CrossRefGoogle ScholarPubMed
Blatman, G. and Sudret, B. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis, Probabilist. Eng. Mech., 2010, 25, (2), pp 183–197.CrossRefGoogle Scholar
Sobol, I.M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulat., 2001, 55, (1), pp 271–280.CrossRefGoogle Scholar
Kang, Y.E., Yang, S. and Yee, K. Physics-aware reduced-order modeling of transonic flow via β-variational autoencoder, Phys. Fluids, 2022, 34, (7), pp 076103.CrossRefGoogle Scholar
Guo, Z., Chu, W. and Zhang, H. A data-driven non-intrusive polynomial chaos for performance impact of high subsonic compressor cascades with stagger angle and profile errors, Aerosp. Sci. Technol., 2022, 129, p 107802.CrossRefGoogle Scholar
Sarabia Aldana, C.A., Medina-Torres, L., Calderas, F., Ramírez-Torres, L.A., Núñez-Ramírez, D.M., Herrera-Valencia, E.E., Bernad-Bernad, M.J. & Manero, O. Hemorheological and biochemical study in patients with liver cirrhosis, Phys. Fluids, 2022, 34, (4), p 041907.CrossRefGoogle Scholar