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

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 model decomposition 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 subdomains, each containing one blade row. For each subdomain, a submodel introduces aerodynamic uncertainties at the interfaces connecting different subdomains. The dimensionality of a submodel 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 submodels are established. Results show that the original 25-dimensional model can be decoupled into a 13-dimensional submodel for the rotor and a 16-dimensional submodel for the stator, reducing the required sample size from 600 to 90 with similar accuracy. This model decomposition 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.