Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Critical Sample Size Analysis on Uncertainty Aerodynamic Evaluation for Compressor Blade With Stagger Angle Errors

Version 1 : Received: 9 November 2023 / Approved: 9 November 2023 / Online: 9 November 2023 (11:23:10 CET)

A peer-reviewed article of this Preprint also exists.

Wang, H.; Gao, L.; Wu, B. Critical Sample-Size Analysis for Uncertainty Aerodynamic Evaluation of Compressor Blades with Stagger-Angle Errors. Aerospace 2023, 10, 990. Wang, H.; Gao, L.; Wu, B. Critical Sample-Size Analysis for Uncertainty Aerodynamic Evaluation of Compressor Blades with Stagger-Angle Errors. Aerospace 2023, 10, 990.

Abstract

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 data collected in compressor blades of aero-engine are mostly limited due to 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 sampled data. The calculation accuracy of PDDPC method is closely related to the sample size of collected data. Therefore, the influence of sample size on PDDPC method is investigated using a nonlinear test function. Subsequently, we consider the real manufacturing errors of stagger angle for compressor blades. Under three different operating conditions, 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 of aerodynamic performance obtained by 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 make a valuable reference for the fast and cheap UQ analysis of compressor blades in practical engineering.

Keywords

compressor blade; manufacturing uncertainty; stagger angle; uncertainty quantification; limited samples; polynomial chaos

Subject

Engineering, Aerospace Engineering

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