A multi-variable multi-objective methodology for experimental data and thermodynamic analysis validation: An application to micro gas turbines

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. In this study, a methodology aimed at performing a validation of micro gas turbine experimental data congruence and the related thermodynamic analysis was investigated through two studied cases. This methodology was based on setting up of a thermodynamic analysis and verifying experimental data reliability through a genetic algorithm that optimizes the input thermodynamic parameters. The following conclusions can be made: • The methodology verifies the experimental data congruence and highlights the less reliable parameters. For example, the combustion chamber pressure for the Turbec T100 has a variance that is one order of magnitude greater than that of other parameters. The Capstone C30 has the overall efficiency as the least reliable parameter, but the other ones have similar variances. • The methodology can define an optimal set of thermodynamic input data, using the Euclidean norm to evaluate the preferred solution: this useful mathematical tool ranks the obtained designs by the minimum variance of the overall objectives, determining the preferred design. Consequently, the preferred solution for each case has congruent thermodynamic input parameters and the related results are very close to the objectives set. • As stated above, the methodology, when coupled with experimental tests, could decrease the needed measurement campaigns. • Because of its generic nature, the methodology applied here on two typical gas turbine plants could also be used on other plants, for treatment of both steady flow (e.g., a steam plant or an organic Rankine cycle plant) and unsteady flow (e.g., an internal combustion engine). The methodology effectiveness was proved by plotting the preferred experimental design onto turbomachinery performance maps, examining the congruent matching of the design operating conditions.