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In the paper, an adaptive sparse arbitrary polynomial chaos expansion (PCE) is first proposed to quantify the performance impact of realistic multi-dimensional manufacturing uncertainties. The Stieltjes algorithm is employed to generate the PCE basis functions concerning geometric variations with arbitrary distributions. The basis-adaptive Bayesian compressive sensing algorithm is introduced to retain a small number of significant PCE basis functions, requiring fewer model training samples while preserving fitting accuracy. Secondly, several benchmark tests are used to verify the computational efficiency and accuracy of the proposed method. Eventually, the coexistence effects of six typical machining deviations on the aerodynamic performance and flow fields of a controlled diffusion compressor cascade are investigated. The probability distributions of the machining deviations are approximated by limited measurement data using kernel density estimation. By uncertainty quantification, it can be learned that the mean performance seriously deteriorates with increasing incidence, while the performance at negative incidences is more dispersed. By global sensitivity analysis, the leading-edge profile error should be given high priority when working at negative incidences, and the inlet metal angle error would be carefully inspected first when the cascade works at high positive incidences. Furthermore, controlling the manufacturing accuracy of suction surface profile error can play a certain role in improving the robustness of aerodynamic performance in off-design conditions. Through flow field analysis, it further proves that actual leading-edge errors are the most important to aerodynamics, and reveals how the effects of leading-edge errors propagate in the cascade passage, thus affecting the aerodynamic loss.
In the paper, an adaptive sparse arbitrary polynomial chaos expansion (PCE) is first proposed to quantify the performance impact of realistic multi-dimensional manufacturing uncertainties. The Stieltjes algorithm is employed to generate the PCE basis functions concerning geometric variations with arbitrary distributions. The basis-adaptive Bayesian compressive sensing algorithm is introduced to retain a small number of significant PCE basis functions, requiring fewer model training samples while preserving fitting accuracy. Secondly, several benchmark tests are used to verify the computational efficiency and accuracy of the proposed method. Eventually, the coexistence effects of six typical machining deviations on the aerodynamic performance and flow fields of a controlled diffusion compressor cascade are investigated. The probability distributions of the machining deviations are approximated by limited measurement data using kernel density estimation. By uncertainty quantification, it can be learned that the mean performance seriously deteriorates with increasing incidence, while the performance at negative incidences is more dispersed. By global sensitivity analysis, the leading-edge profile error should be given high priority when working at negative incidences, and the inlet metal angle error would be carefully inspected first when the cascade works at high positive incidences. Furthermore, controlling the manufacturing accuracy of suction surface profile error can play a certain role in improving the robustness of aerodynamic performance in off-design conditions. Through flow field analysis, it further proves that actual leading-edge errors are the most important to aerodynamics, and reveals how the effects of leading-edge errors propagate in the cascade passage, thus affecting the aerodynamic loss.
Compressed air energy storage systems must promptly adapt to power network demand fluctuations, necessitating a high surge margin in the compression system to ensure safety. It is challenging to completely eliminate blade geometric variations caused by limited machining precision, the important effects of which should be considered during aerodynamic shape design and production inspection. The present paper explores the uncertainty impact of geometric deviations on the stability margin of a multi-stage axial compressor at a low rotational speed. Initially, an adaptive polynomial chaos expansion-based universal Kriging model is introduced, and its superior response performance in addressing high-dimensional uncertainty quantification problems is validated through rigorous analytical and engineering tests. Then, this model is used to statistically evaluate the stability margin improvement (SMI) of the compressor due to the Gaussian and realistic geometric variabilities separately. The results show that the mean and standard deviation of SMI are −0.11% and 0.5% under the Gaussian geometric variability, while those are 0.33% and 0.39% under the realistic variability. For both the geometric variabilities, the stagger angle and maximum thickness deviations of the first-stage rotor are the most influential parameters controlling the uncertainty variations in the stability margin. Finally, the underlying impact mechanism of the influential geometric deviations is investigated. The variation in the stability margin caused by the geometric deviations primarily results from the alteration of inlet incidences, affecting the size of the tip leakage vortex blockage and boundary-layer separation regions near the blade tip of the first-stage rotor.
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