[pt] Os mapas de características do compressor axial representam o desempenho deste por toda sua faixa de operação. Estes mapas podem ser utilizados para determinar o ponto de operação na simulação off-design. O presente trabalho propõe uma metodologia de manipulação de mapas de características de compressores axiais, baseada nas metodologias já existentes. A rotina desenvolvida, denominada PCOMP, é capaz de obter uma nova linha de rotação, entre duas linhas adjacentes conhecidas. A distância entre duas linhas adjacentes conhecidas e a nova linha irá determinar a influência que cada uma terá nas características desta nova linha. Uma vez determinada a linha de rotação, os parâmetros são interpolados a fim de determinar a vazão mássica corrigida e eficiência isentrópica para a razão de pressão correspondente, dada na entrada da rotina. O método de ponderação para a determinação de novas linhas de rotação apresentou desvios menores que 3% para a vazão mássica corrigida e eficiência isentrópica. Comparando as saídas da rotina com dados de operação de uma usina real, foram encontrados desvios menores que 1% para a eficiência isentrópica. A rotina desenvolvida foi implementada no módulo de simulação do compressor da ferramenta de simulação de turbinas a gás denominada NGGT (Natural Gas & Gas Turbine), apresentando resultados satisfatórios. / [en] Axial compressor maps are able to represent the performance of all its operating range. These maps are used for determining the operating point in the off-design simulation. This thesis proposes a methodology for handling maps of axial compressors, based on existing methodologies. The model, called PCOMP, is able to obtain a new rotational speed line between two adjacent lines known. The distance between the known and the new line will determine the influence that each one will have in the characteristics of the new line. Once determined the speed line, one can find the operating point interpolating the known parameters to determine the corrected mass flow and isentropic efficiency for the corresponding pressure ratio given in the entry data of the routine. The weighting method using to determinate the new rotation speed line presented deviations smaller than 3% for the corrected mass flow and isentropic efficiency. By comparing the outputs of the developed code with the operating data of a real power plant, were found deviations smaller than 1% for the isentropic efficiency. The routine was successfully implemented in a gas turbine performance computer program, called NGGT (Natural Gas & Gas Turbine) model, which presented accurate and efficient simulations.

Gas turbine performance models are usually created in an object oriented manner, where different standard components are connected to form the complete model. The characteristics of these components are often represented by component maps and empirical correlations. However, engine specific component characteristics are seldom available to anyone outside of the manufacturers. It is therefore very common for researchers to use publicly accessible or generic component maps instead. But in order to reduce prediction errors the maps have to be modified to fit any specific engine. This thesis work investigates the process of adapting a parametric turbofan engine model to a limited amount of test-data using the propulsion program EVA. Steady state test-data was generated using an initial reference model with SLS operating conditions. Another engine model with different fan, compressor and turbine maps was then used in the adaptation. An initial on-design model was adapted to the highest power test-data point. This model is based on aerothermodynamic equations and is used as a reference to scale the generic component maps to. A sensitivity analysis was done at this point in order to find dependencies between unknown component parameters and test data. These were then included in the cycle solver which employs a version of the Newton-Raphson method. After the fan and compressor maps had been scaled to the design point they were adapted to test-data by adjusting the mass flow parameters in a direct search optimizer. Finally, speed lines in the fan and compressor maps were relabeled to reduce rotor speed errors. The adapted performance model was then validated against the reference model at a few flying conditions. The performance model results demonstrate that it is possible to greatly reduce prediction errors by only adjusting the corrected mass flow in fan and compressor maps. Additionally, rotor speed errors could successfully be corrected as a final step in the adaptation by relabeling speed lines in the component maps. When validated, the adapted model had a maximum parameter error of 1.5%.