Performance Model Identification of the General Electric CF34-8C5B1 Turbofan Using Neural Networks

Abstract

This paper presents a methodology developed at the Laboratory of Applied Research in Active Controls, Avionics, and Aeroservoelasticity to identify a performance model of the CF34-8C5B1 turbofan engine powering the CRJ-700 regional jet aircraft from simulated flight data using artificial neural networks (ANNs). For this purpose, a qualified virtual research simulator was used to conduct different types of flight tests and to collect engine data under a wide range of operating conditions. The collected data were then used to create a comprehensive database for the training of the ANN model. This process was performed using the Bayesian regularization algorithm available in the MATLAB Neural Networks Toolbox, followed by a study to identify the optimal network architecture, namely, the number of layers and the number of neurons. The validation of the methodology was accomplished by comparing the model predictions with a set of flight data collected with the flight simulator for different flight conditions and flight regimes including takeoff, climb, cruise, and descent. The results showed that the model was able to predict the engine performance in terms of fan speed, core speed, inlet turbine temperature, net thrust, and fuel flow with less than 5% relative error.

An Engine Deterioration Model for Predicting Fuel Consumption Impact in a Regional Aircraft

“…Aircraft and engine models for the Cessna Citation X and the CRJ-700 have been developed at the LARCASE [33]. Examples of their work on engine models include system identification [34][35][36], adaptive algorithms [37], and neural networks [38][39][40]. Different cycle model analyses have also been performed at the LARCASE over the past few years [31,[41][42][43].…”