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This paper presents a model-based, off-line method for analyzing the performance of individual components in an operating gas turbine. This integrated model combines sub-models of the combustor efficiency, the combustor pressure loss, the hot-end heat trans-fer, the turbine inlet temperature, and the turbine performance. As part of this, new physics-based models are proposed for both the combustor efficiency and the turbine. These new models accommodate operating points that feature the flame extending beyond the combustor and combustion occurring in the turbine. Systematic model reduction is undertaken using experimental data from a prototype, microgas turbine rig built by the group. This so called gas turbine air compressor (GTAC) prototype utilizes a single com-pressor to provide cycle air and a supply of compressed air as its sole output. The most general model results in sensible estimates of all system parameters, including those obtained from the new models that describe variations in both the combustor and turbine performance. As with other microgas turbines, heat losses are also found to be significant. [DOI: 10.1115/1.4007731

Compressed air and steam are perhaps the most significant industrial utilities after electricity, gas and water, and are responsible for a significant proportion of global energy consumption. Microturbine technology, in the form of a Gas Turbine Air Compressor (GTAC), offers a promising alternative to traditional, electrically driven air compressors providing low vibration, a compact size, reduced electrical consumption and potentially reduced greenhouse gas emissions. With high exhaust temperatures, gas turbines are well suited to the cogeneration of steam. The compressed air performance can be further increased by injecting some of that cogenerated steam or by conventional recuperation.This paper presents a thermodynamic analysis of various forms of the GTAC cycle incorporating steam cogeneration, steam injection (STIGTAC) and recuperation. The addition of cogeneration leads to improved energy utilisation, while steam injection leads to a significant boost in both the compressed air delivery and efficiency. As expected, for a low pressure ratio device, recuperating the GTAC leads to a significant increase in efficiency. The combination of steam injection and recuperation forms a recuperated STIGTAC with increased compressed air performance over the unrecuperated STIGTAC at the expense of reduced steam production. Finally, an analysis using a simpli- * Address all correspondence to this author fied model of the STIGTAC demonstrates a significant reduction in CO 2 emissions, when compared to an equivalent air compressor driven by primarily coal-based electricity and a natural gas fired boiler. NOMENCLATUREa, c, d, e Number of mol CMF Corrected mass flow rate EGT Evaporative gas turbine GTAC Gas turbine air compressor H Enthalpy h Specific enthalpy HAT Humid Air Turbine HRSG Heat recovery steam generator ∆h f o Enthalpy of formation M Mach numbeṙ m Mass flow rate Mo Molar mass NIST National Institute of Standards and Technology q Heat release/transfer r p Pressure ratio S Steam ratio STIG Steam injected gas turbine STIGTAC Steam injected gas turbine air compressor 1

This paper presents and validates a physics-based, dynamic model of a gas turbine. The model is an extension of that proposed by Badmus et al. [1], such that representation of a complete gas turbine is achieved. It includes new models of several gas turbine components, in particular the turbine and compressor, and also applies a well known method for prescribing boundary conditions [10] to the gas path. This model first uses data from a previously published, static model of the same gas turbine to determine this dynamic model’s many so-called ‘forcing terms’. A least-squares optimisation is then undertaken to estimate the shaft inertia and the thermal inertia of system components using transient test data. Importantly, these optimised results are all close to physically reasonable estimates. Further, they show that the shaft dynamics are only significant for a short period at the start of most transients, after which the dynamic effects of thermal storage are dominant. The complete gas turbine model is then validated against transient test data. Whilst the simulated traces demonstrate some steady-state error arising from the static model [12], the overall system dynamics appear to be captured well. Since steady-state error can be integrated out in a control system, this suggests that the proposed dynamic model is appropriate for use in a model-based, gas turbine controller.

This paper is the second part of a two part study that develops, validates and integrates a one-dimensional, physics-based, dynamic boiler model. Part 1 of this study [1] extended and validated a particular modelling framework to boilers. This paper uses this framework to first present a higher order model of a gas turbine based cogeneration plant. The significant dynamics of the cogeneration system are then identified, corresponding to states in the gas path, the steam path, the gas turbine shaft, gas turbine wall temperatures and boiler wall temperatures. A model reduction process based on time scale separation and singular perturbation theory is then demonstrated. Three candidate reduced order models are identified using this model reduction process, and the simplest, acceptable dynamic model of this integrated plant is found to require retention of both the gas turbine and boiler wall temperature dynamics. Subsequent analysis of computation times for the original physics-based one-dimensional model and the candidate, reduced order models demonstrates that significantly faster than real time simulation is possible in all cases. Furthermore, with systematic replacement of the algebraic states with feedforward maps in the reduced order models, further computational savings of up to one order of magnitude can be achieved. This combination of model fidelity and computational tractability suggest suggests that the resulting reduced order models may be suitable for use in model based control of cogeneration plants.