An energy-based representation of an axial-flow compressor system

Abstract

Large-scale industrial systems play a great role in our modern industry. One example of such a system is the Brayton cycle. The Brayton cycle consists of a compressor, combustor and turbine. Each interconnecting component can be modelled independently based on either the state variables or the energy of the system. Modelling of industrial systems for Control, Condition Monitoring and Fault Detection and Diagnosis (FDD) is general in engineering. The modelling of the nonlinearities pose difficulties in the analysis of such a system when multi-domain effects are considered in the model. Over the last decade, energy based-techniques delivered promising results in terms of the optimisation of nonlinear systems in the field of Control, condition monitoring and FDD. Recent research has focused on viewing components in systems as energy-shaping devices. A compressor system demonstrates nonlinearities, commonly found in practical systems, so such a system can be considered as an ideal system for an energy-based representation. The most common axial-flow compressor model found in the literature is Moore and Greitzer's dimensional model derived in 1986. The dimensional model from Greitzer was used to obtain the state space model of the compressor system for this study. A set of equations were included in the model to simulate the dynamics of a leak in the plenum volume. The model can introduce one of three fault conditions. The first is a leak in the plenum volume. The secon is rotating stall and thirdly surge, both types of unstable operations. The axial-flow compressor model's response is validated by comparing it to the response of a Flownex® model. Flownex® is a computational CFD software package that is capable of modelling complex thermohydraulic systems. All of the components found in Flownex® are validated with experimental data. The energy analysis is done from the state variables of the system by deriving an energy-balance for each component in the system. The energy-balance for each component is derived from the first law of thermodynamics to verify the energy analysis. A visual energy representation is given for both stable and unstable conditions for each component. A sensitivity analysis based on the energy of each component is done that relates the energy parameter to a change of an input parameter. A distinct result is that the steady state energy characteristic can be mapped for each component. The power-energy plane for a fault condition can be superimposed on the power-energy plane of the normal operation when the operating point is modified. The energy and power response can be used to implement an FDD system. The sensitivity analysis indicates that the energy and power could be used for Controller design, Condition monitoring and FDD