High-fidelity models are an increasingly indispensable tool for evaluating high-performance control and optimization algorithms of gas turbine (GT) engines. However, GT high-fidelity models (HFM’s) are often too complex for synthesizing model-based control and optimization algorithms, which are much more amenable to low-order linear dynamic models. Obtaining models suitable for control synthesis can be arduous and costly. White-box (first-principles) methods may produce models that lose fidelity in off-nominal conditions, while black-box (data-driven) methods yield model parameters without physical significance, preventing generalization of the model across product configurations. This paper presents a grey-box method for obtaining low-order linear dynamic models for a 5.5 MW GT engine that retain fidelity at off-nominal conditions and generalize to multiple product configurations. The approach exploits the structure of an available HFM by grouping its constituent component-level models according to their suitability to white-box or black-box modeling. Specifically, the rotor model is linearized, yielding a first-order linear model with adjustable physical parameters, e.g. inertia. A second-order open-loop linear model is obtained from remaining component-level models via system identification using closed-loop data generated by the HFM. The linear models are combined to form a third-order open-loop linear GT engine model, which retains fidelity with the HFM in transient validation experiments, and multiple rotor inertia values.

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