关键词: 航空发动机, 燃油离心泵, KL变换, 耦合双层参数更新的改进PCE模型, 性能不确定性分析, 流场不确定性分析

Abstract: Blade manufacturing and extreme operating conditions in aviation engines introduce uncertainty factors that significantly impact the performance and flow field variability of fuel centrifugal pumps. This paper proposes a synchronized analysis method based on CFD mechanistic models and surrogate models. Firstly, employing Karhuben-Loève (KL) transform theory, uncertainty modeling of three-dimensional blade profile error in the centrifugal pump was conducted. Secondly, nesting Least Angle Regression (LAR) algorithm, a double-layer parameter update was applied to the Polynomial Chaos Expansion (PCE) model, constructing a high-precision surrogate model. Finally, employing a specific type of fuel centrifugal pump as the research subject, uncertainty analysis of the centrifugal pump was accomplished based on CFD simulations, experimental verification, and the PCE surrogate model. The research demonstrates that the KL transform efficiently describes uncertainty in three-dimensional blade profile error using only 9 input parameters. The improved PCE model with coupled double-layer parameter updating exhibits an average increase of 27.6% in accuracy metrics across multiple centrifugal pump conditions compared to the unimproved model. Controlling blade profile error within -0.3mm to 0.3mm at the hub significantly reduces uncertainty in centrifugal pump performance. Blade profile error at the hub have a greater impact on flow field uncertainty compared to those at the shroud and midsection, while speed is a crucial operational parameter affecting uncertainty in the fuel pump flow field.

Key words: aviation, fuel centrifugal pump, KL transform, improved PCE model with coupled double-layer parameter updating, performance uncertainty, flow field uncertainty