Effect of Manufacturing Tolerance in Flow Past a Compressor Blade

Suriyanarayanan, Venkatesh; Rendu, Quentin; Vahdati, Mehdi; Salles, Loïc

doi:10.1115/1.4052600

Cited by 19 publications

(4 citation statements)

References 17 publications

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“…Gao et al 23 conducted both numerical and experimental investigations on a compressor cascade and found that the stagger angle error was the dominant factor affecting the aerodynamic performance compared with the chord length, profile and trailing edge errors. Venkatesh et al 24 investigated the influence of stagger angle errors on the NASA rotor 67, and the results showed that when the stagger angle changed with ±1.5°, the mass flowrate changed by ± 2%. However, these studies mainly considered the influence of some specific stagger angle errors based on the deterministic approaches, which cannot reflect the essential stochastic characteristic of stagger angle errors.…”

Section: Introductionmentioning

confidence: 99%

A data-driven polynomial chaos method for uncertainty quantification of a subsonic compressor cascade with stagger angle errors

Wang,

Gao,

Yang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part A:

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The probability-based uncertainty quantification (UQ) methods require a large amount of sampled data to construct the probability distribution of uncertain input parameters. However, it is a common situation that only limited and scarce sampled data are available in engineering applications due to expensive tests. In the present paper, the Data-Driven Polynomial Chaos (DDPC) method is adopted, which can propagate input uncertainty in the case of scarce sampled data. The calculation accuracy and convergence of the self-developed DDPC method are validated by a nonlinear test function. Subsequently, the DDPC method is applied to investigate the uncertain impact of stagger angle errors on the aerodynamic performance of a subsonic compressor cascade. A family of manufacturing error data of stagger angles was obtained from the real compressor blades. Based on the limited measurement data, the DDPC method combined with Computational Fluid Dynamics (CFD) simulation is employed to quantify the performance impact of the compressor cascade. The results show that the performance dispersion under off-design conditions is more prominent than that under design conditions. The actual aerodynamic performance deviating from the nominal performance is not a small probability event, and the probability of deviating from the nominal loss coefficient and exit flow angle by more than 1% can reach up to 47.6% and 36.8% under high incidence i = 7°. Detailed analysis shows that stagger angle errors have a significant effect on the flow state near the leading edge, resulting in variations in separation bubble size and boundary layer thickness.

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Section: Introductionmentioning

confidence: 99%

A data-driven polynomial chaos method for uncertainty quantification of a subsonic compressor cascade with stagger angle errors

Wang,

Gao,

Yang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part A:

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“…They found that thickness deviations in the leading edge and the front half of the suction surface of the blades have a significant impact on cascade performance. Additionally, studies conducted in Garzon and Darmofal (2003), Suriyanarayanan et al (2022) have investigated the impact of various types of blade geometric deviations on compressor performance, leading to several engineering-relevant conclusions with valuable guidance.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification of blade geometric deviation on compressor stability

Ji,

Chu

2023

AEAT

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Purpose The geometric parameters of the compressor blade have a noteworthy influence on compressor stability, which should be meticulously designed. However, machining inaccuracies cause the blade geometric parameters to deviate from the ideal design, and the geometric deviation exhibits high randomness. Therefore, the purpose of this study is to quantify the uncertainty and analyze the sensitivity of the impact of blade geometric deviation on compressor stability. Design/methodology/approach In this work, the influence of blade geometric deviation is analyzed based on a subsonic compressor rotor stage, and three-dimensional numerical simulations are used to compute samples with different geometric features. A method of combining Halton sequence and non-intrusive polynomial chaos is adopted to carry out uncertainty quantitative analysis. Sobol’ index and Spearman correlation coefficient are used to analysis the sensitivity and correlation between compressor stability and blade geometric deviation, respectively. Findings The results show that the compressor stability is most sensitive to the tip clearance deviation, whereas deviations in the leading edge radius, trailing edge radius and chord length have minimal impact on the compressor stability. And, the effects of various blade geometric deviations on the compressor stability are basically independent and linearly superimposed. Originality/value This work provided a new approach for uncertainty quantification in compressor stability analysis. The conclusions obtained in this work provide some reference value for the manufacturing and maintenance of rotor blades.

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“…Lu et al [30] found that the mis-staggering can be amplified by aerodynamic loading and the designed peak efficiency cannot be achieved for a transonic fan blade. Suriyanarayanan et al [31] studied the influence of stagger-angle errors on NASA rotor 67. Their results showed that the sinusoidal arrangement of stagger-angle variations throughout the annulus is optimal compared to the linear and random types.…”

Section: Introductionmentioning

confidence: 99%

Critical Sample-Size Analysis for Uncertainty Aerodynamic Evaluation of Compressor Blades with Stagger-Angle Errors

Wang,

Gao,

2023

Aerospace

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Many probability-based uncertainty quantification (UQ) schemes require a large amount of sampled data to build credible probability density function (PDF) models for uncertain parameters. Unfortunately, the amounts of data collected as to compressor blades of aero-engines are mostly limited due to the expensive and time-consuming tests. In this paper, we develop a preconditioner-based data-driven polynomial chaos (PDDPC) method that can efficiently deal with uncertainty propagation of limited amounts of sampled data. The calculation accuracy of a PDDPC method is closely related to the sample size of collected data. Therefore, the influence of sample size on this PDDPC method is investigated using a nonlinear test function. Subsequently, we consider the real manufacturing errors in stagger angles for compressor blades. Under three different operating conditions, the PDDPC method is applied to investigate the effect of stagger-angle error on UQ results of multiple aerodynamic parameters of a two-dimensional compressor blade. The results show that as the sample-size of measured data increases, UQ results regarding aerodynamic performance obtained by the PDDPC method gradually converge. There exists a critical sample size that ensures accurate UQ analysis of compressor blades. The probability information contained in the machining error data is analyzed through Kullback–Leibler divergence, and the critical sample size is determined. The research results can serve as a valuable reference for the fast and cheap UQ analysis of compressor blades in practical engineering.

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Effect of Manufacturing Tolerance in Flow Past a Compressor Blade

Cited by 19 publications

References 17 publications

A data-driven polynomial chaos method for uncertainty quantification of a subsonic compressor cascade with stagger angle errors

A data-driven polynomial chaos method for uncertainty quantification of a subsonic compressor cascade with stagger angle errors

Uncertainty quantification of blade geometric deviation on compressor stability

Critical Sample-Size Analysis for Uncertainty Aerodynamic Evaluation of Compressor Blades with Stagger-Angle Errors

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