Cessna Citation X simulation turbofan modelling: identification and identified model validation using simulated flight tests

Abstract

The aviation industry relies on accurate models. These models are used to predict an aircraft system’s outputs, and thus allow an understanding of the parameters involved, which could lead to system improvements. This study focuses on the engine modelling of an aircraft, and on its experimental validation using the Cessna Citation X Research Aircraft Simulator designed by CAE Inc., equipped with a level D Flight Dynamics toolbox. Level D is the highest rank attributed by the Federal Aviation Administration FAA certification authorities for flight dynamics. The proposed model aims to predict the thrust and the fuel consumption for various altitudes, Mach numbers and throttle lever angles (TLA). Different generic static models, which correspond to their steady state, from the literature, were used in this study; however, most of them were validated under restricted hypotheses. An optimisation algorithm was used in order to tune the static model parameters with the set of identification flight test data. Another set of data was then used in order to validate the identified model. Furthermore, a dynamic model corresponding to the transient operations was identified. TLA steps, impulses and ramp perturbations were performed in order to identify the system response, and to validate system dynamic model with other flight tests than the identification tests.

Improved Local Scale Generic Cycle Model for Aerothermodynamic Simulations of Gas Turbine Engines for Propulsion

“…The correlations defined in a typical compressor map are presented in Equations (3)- (5). These correlations are functions of the N corr and R-lines, the latter are auxiliary coordinates defined in a compressor map to uniquely define its operating point at a given power-setting.…”

“…Several multidisciplinary studies for aircraft and engine models have been pursued in the Laboratory of Applied Research in Active Control, Avionics and AeroServoElasticity (LARCASE) [1]. Regarding engine models, the LARCASE has explored techniques, such as system identification [2,3], empirical equations [4], component level modeling [5], neural networks [6,7], etc., to predict the F n and SFC for real engines, such as the Rolls-Royce AE3007C and the General Electric (GE) CF34-8C5B1.…”

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].…”