Abstract
During recent decades, the equilibrium manifold expansion (EME) model has been considered as a powerful identification tool for complex industrial systems with the aim of system control and simulation. Based on a two-step “dynamic and static” identification method, an approximate nonlinear state-space model is built by using multiple polynomials. However, the existing identification method is only suitable for single-input (SI) systems, but not for multi-input (MI) systems, where EME models cannot guarantee global calculation stability. For solving such a problem, this paper proposes a corrected equilibrium manifold expansion (CEME) model based on gas turbine prior knowledge. The equilibrium manifold is extended in dimension by introducing similarity equations instead of the high dimensional polynomial fitting. The dynamic similarity criterion of similarity theory guarantees the global stability of the CEME model. Finally, the comparative test between the CEME model and the existing MI-EME model is carried out through case studies involving data that are generated by a general turbofan engine simulation. Simulations show superior precision and calculation stability of the proposed model in capturing nonlinear behaviors of the gas turbine engine.
Highlights
Gas turbine (GT) engines provide power for airplanes, ships, and industrial equipment, and reliable and efficient operation is crucial to their safety and performance
For solving the above problems, this paper proposes a corrected equilibrium manifold expansion (CEME) model by integrating the prior knowledge of gas turbines into the existing identification method
The multiple-input EME model built by the existing identification method has problems of calculation multiple-input EME model built by the existing identification method has problems of calculation instability and difficulties in engineering applications
Summary
Gas turbine (GT) engines provide power for airplanes, ships, and industrial equipment, and reliable and efficient operation is crucial to their safety and performance. Unexpected faults and improper control lead to unplanned maintenance of equipment [1]. Since the cost of unplanned service interruption is usually significantly higher than the cost of performing preventative maintenance and returning [2], sensitive fault detection, and isolation systems and robust control systems are essential, in both of which an accurate model describing engine behaviors is very important [3]. The demand for enhanced and reliable performance of models is ever increasing while an urgent demand for shorting design cycles, minimizing inspection and reducing costs is required. With significant interest to expand the performance of models, there is the need for designing smaller and more flexible nonlinear models. The need to use simple and nonlinear structural models is increasing in engineering applications
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