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This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modeling and nonlinear analysis is used to restrict the search space for nonlinear modeling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modeling can be effectively combined to enhance the modeling of an aircraft gas turbine.

In this paper a feedforward neural network is used to model the fuel flow to shaft speed relationship of a Spey gas turbine engine. The performance of the estimated model is validated against a range of small and large signal engine tests. It is shown that the performance of the estimated models is superior to that of the estimated linear models.

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The linear multivariable modelling of an aircraft gas turbine is presented. A frequency-domain identification method is employed to estimate a family of models from engine data at a range of operating points. It is found that the fuel feed to shaft speed dynamics can be represented by second-order models. This matches the results obtained for the reduced-order linearised thermodynamic models derived from the engine physics. A direct comparison can thus be made between the estimated and thermodynamic models, which shows significant discrepancies between them for the engine tested. This work illustrates how modern system identification techniques can be used to verify engine models. The multivariable framework employed means that additional inputs and outputs can be easily incorporated into the model.