LAUSR

Abstract A methodology aimed at defining thermodynamic model parameters and validating experimental data has been proposed. The methodology consists of a thermodynamic model of a micro gas turbine coupled with a multi-variable multi-objective genetic optimization algorithm, in which decision variables and objectives are set depending on available experimental data. To validate both the thermodynamic model and the collected experimental data, the methodology has been applied to two micro gas turbine plants: the Capstone C30 and the Turbec T100. Validations of the thermodynamic model and the collected experimental data for the two plants have been performed by evaluating the match between input and output physical parameters. The optimal results of the optimization algorithm have plausible thermodynamic parameters and constitute the Pareto front; between these results, the one with the minimum difference between experimental data and calculated values is chosen as preferred. The two studied cases highlight the effect of measurement chain errors on experimental data reliability: the greater is the overall variance of the objectives, the lower is the accuracy of the experimental data. The effectiveness of proposed methodology has been verified for the Capstone C30 through the congruence of the design operating conditions on both the compressor and turbine maps.