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A recent development in the design of control system for a jet engine is to use a suitable, fast and accurate model running on board. Development of linear models is particularly important as most engine control designs are based on linear control theory. Engine control performance can be significantly improved by increasing the accuracy of the developed model. Current state-of-the-art is to use piecewise linear models at selected equilibrium conditions for the development of set point controllers, followed by scheduling of resulting controller gains as a function of one or more of the system states. However, arriving at an effective gain scheduler that can accommodate fast transients covering a wide range of operating points can become quite complex and involved, thus resulting in a sacrifice on controller performance for its simplicity. :This thesis presents a methodology for developing a control oriented analytical linear model of a jet engine at both equilibrium and off-equilibrium conditions. This scheme requires a nonlinear engine model to run onboard in real time. The off-equilibrium analytical linear model provides improved accuracy and flexibility over the commonly used piecewise linear models developed using numerical perturbations. Linear coefficients are obtained by evaluating, at current conditions, analytical expressions which result from differentiation of simplified nonlinear expressions. Residualization of the fast dynamics states are utilized since the fast dynamics are typically outside of the primary control bandwidth. Analytical expressions based on the physics of the aerothermodynamic processes of a gas turbine engine facilitate a systematic approach to the analysis and synthesis of model based controllers. In addition, the use of analytical expressions reduces the computational effort, enabling linearization in real time at both equilibrium and off-equilibrium conditions for a more accurate capture of system dynamics during aggressive transient maneuvers. The methodology is formulated and applied to a separate flow twin-spool turbofan engine model in the Numerical Propulsion System Simulation (NPSS) platform. The fidelity of linear model is examined by validating against a detailed nonlinear engine model using time domain response, the normalized additive uncertainty and the nu-gap metric. The effects of each simplifying assumptions, which are crucial to the linear model development, on the fidelity of the linear model are analyzed in detail. A case study is performed to investigate the case when the current state (including both slow and fast states) of the system is not readily available from the nonlinear simulation model. Also, a simple model based control is used to illustrate benefits of using the proposed modeling approach.
A real time marine gas turbine simulation would offer an essential basis for advanced marine propulsion control designs. Such designs may be realized as model reference controllers and/or health monitoring controllers. This paper presents an approach to real time turbine simulation using a method of sequential state space linearizations. The linearizations are shown to be simple enough to be computed in real time. Comparisons between simulations and experiments are presented and discussed. The approach is shown to have very good accuracy for both transient and steady state predictions.
Previous work has shown that it is possible to model the dynamics of marine gas turbines with very simple equations using a method called sequential linearization, thus offering the possibility of real time simulation for monitoring and controller design purposes. This paper presents further simplifications on the method of sequential linearization for marine gas turbines and begins to deal with the important model issues of robustness and accuracy. The method is applied to data from a small marine gas turbine emulator installation which is located at the Naval Postgraduate School. The step-by-step method is discussed and an excellent agreement between the simulation results and the installation data is shown for cases of steady states and dynamic transients.
Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consi
Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book:Outlines important criteria to consi
This SAE Aerospace Information Report (AIR) provides a review of real-time modeling methodologies for gas turbine engine performance. The application of real-time models and modeling methodologies are discussed. The modeling methodologies addressed in this AIR concentrate on the aerothermal portion of the gas turbine propulsion system. Characteristics of the models, the various algorithms used in them, and system integration issues are also reviewed. In addition, example cases of digital models in source code are provided for several methodologies. This document has been updated to include an advancement in aerothermodynamic real time model transient simulation methodology. In recent years, there has been an emphasis on using a single model for both non-real-time and real-time simulation, without any loss in accuracy and without the extra software maintenance efforts associated with having a separate real-time model. Discussion of a method to optimize the equation structure of the non-real-time aerothermal model for real-time execution is included.