Estimation of performance parameters of turbine engine components using experimental data in parametric uncertainty conditions

Abstract

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. It results from the fact that the number of measured parameters is significantly less than the number of components’ performance parameters needed to describe the real engine. Inthese conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, lossfactors etc. can appear out of the physical range. The paper presents new method for setting a priori information about the engine and its performance in view of fuzzy sets, forming objective functions and scalar convolutions synthesis of these functions to estimate gas-path components’ parameters. The comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. It reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters.