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.
Highlights
Mathematical models of turbine engines, which are based on working thermo-gas-dynamic process description, are widely used in the design of the engine and its automatic control and diagnostic systems
This paper presents the regularization of the matching task using a priori information about the engine, its mathematical model and expected performance, and about the measuring system and the measuring procedure
The new method is proposed for stable estimation of the engine performance parameters using a priori information about the engine, its mathematical model and expected performance, and about the measuring system and measuring procedure
Summary
Mathematical models of turbine engines, which are based on working thermo-gas-dynamic process description, are widely used in the design of the engine and its automatic control and diagnostic systems. The models are based on the performance of various engine components (compressor, turbine, main combustion chamber, afterburner, intake, exhaust system, transition channels, secondary air system, etc.) These component performances are known at some degree of confidence and have their own individual deviations because of differences in manufacturing and degradation in maintenance. The task of the engine mathematical model matching with experimental data is characterized by a presence of multiple parameters, which can be used for the model correction. A quantity of measured parameters is strongly limited These reasons decrease the stability of the correction procedure, which is based on the LSM, and force researchers to check for methods to improve the stability. This paper considers the solution based on the genetic algorithm [17], which is adapted to specifics of the engine model matching
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