An Efficient Uncertainty Quantification Method Based on Inter-Blade Decoupling for Compressors

Abstract

Abstract A compressor usually contains multiple blade rows, and its uncertainty dimensionality grows proportionally with the number of blade rows, leading to a rapid increase in required sample size for uncertainty quantification analysis. This paper proposes a new dimensionality reduction method based on inter-blade decoupling, by analyzing uncertainty propagation in compressors. Traditionally, a surrogate model with uncertainty variables of all blade rows as input is directly established and the dimensionality is high. To solve this problem, this study decomposes the compressor domain into sub-domains, each containing one blade row. For each sub-domain, a sub-model introduces aerodynamic uncertainties at the interfaces connecting different sub-domains. The dimensionality of a sub-model is roughly equal to the uncertainty factors in a single row, significantly reducing the required sample size. The uncertainties in the rotor and stator blade rows of a one-stage compressor are investigated to verify this method. Using principal component analysis and machine learning, the projection amplitudes of the interface aerodynamic flow field onto the principal modes are extracted, and sub-models are established. Results show that the original 25-dimensional model can be decoupled into a 13-dimensional sub-model for the rotor and a 16-dimensional sub-model for the stator, reducing the required sample size from 600 to 90 with similar accuracy. This dimensionality reduction method greatly reduces the cost of predicting compressor performance with uncertainty, laying a foundation for comprehensive analysis and effective control of uncertainty factors in engineering applications.