Abstract

Turbofan engine technology has evolved over several decades resulting in highly efficient and reliable propulsion systems for commercial airliners. Maintenance costs have decreased, but still represent a major part of the overall aircraft operating costs. To further minimize these, engine maintenance needs to be planned timely and strategically. Advanced diagnostics and health monitoring methods are being developed at KLM Engine Services (ES) to improve engine maintenance. For health monitoring, gas path analysis is used which requires accurate engine performance models. The modelling task is complicated further by the reduced number of measured gas path parameters with modern turbofan engines. This paper presents a systematic approach to overcome these complications. An engine model usually comprises of a cycle reference point, representing a design point such as maximum take-off thrust. As a first step, a genetic algorithm is used to determine the set of unknown cycle reference point parameters and component efficiencies best matching a set of known engine measurement data. Additionally, physical relations were used as constraints to compensate for the missing data. Off-design performance is calculated by solving a set of non-linear algebraic equations which depend on the unknown component performance maps. The customary method of deriving the performance maps by scaling similar maps at the cycle reference point only, often suffers from large deviations at off-design conditions. Consequently, these ‘baseline’ maps require corrections across the entire operating envelope. In the second step of the method, genetic algorithms determine the best off-design performance estimations at multiple measured operating points by finding the optimal coefficients of polynomial scaling functions for map parameters such as efficiency, corrected pressure ratio and mass flow. The modelling method has been verified by developing CF6-80C2 and GEnx-1B turbofan engine models using test cell data. The GEnx-1B engine model has subsequently been validated using on-wing operating data. The largest validation error was attained at cruise flight conditions and was found to be equal to 3.9%. The resulting method provides a systematic way to deal with missing data and can be used for developing accurate engine models for better gas path analysis reliability, resulting in more effective engine maintenance.

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