Neuro-Fuzzy Approach for Performance Optimization of Variable Geometry Turbofan Engine
by
Tarek R. Nada
National Authority for Remote Sensing and Space Sciences, Egypt
Coauthors: Ali A. Hashim

An algorithm employing adaptive Neuro-fuzzy online identification and sequential quadratic programming optimization techniques is developed to enhance aircraft engine performance. This algorithm is implemented and tested using digital simulation for two spool, mixed exhaust, variable geometry turbofan engine. Parametric study is conducted to select the proper measurable parameter that can represent the actual thrust during online optimization. Subtractive clustering technique is applied to generate the minimum number of fuzzy rules that can model the engine performance at various input parameters and flight conditions. The resulting Neuro-fuzzy system is then optimized through training algorithm to accurately represent the engine. This system can address engine variations by relearning the network using online measurements from the actual engine. Generating the optimum schedules and comparing them with those obtained from the complete nonlinear engine model verify the algorithm. Benefits from this algorithm include fuel consumption savings, reductions in turbine inlet temperature, raising the maximum allowable thrust at take off and preventing limit exceeding.

Date received: March 28, 2004

Copyright © 2004 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # canw-21.