Future compressors require the use of novel technologies to improve their efficiency, off-design performance, and thermal management. A frequently discussed option to achieve these goals is the use of active cooling methods in compressors. Maintaining a low temperature during the compression process improves the overall performance of the compressor such as its efficiency or mass flow capacity. It is thus essential to consider the effect of cooling during the early design stages. However, current preliminary design methods rely heavily on empirical data and experience and are, therefore, only partially applicable for the design of cooled compressors. In this work, we demonstrate how modern data-driven methods may be used to obtain a surrogate model for the fast and accurate prediction of performance parameters for cooled compressors. To do so, we train a feed-forward neural network to predict the total pressure and temperature ratio, as well as the mass flow rate of a 4.-stage axial compressor test case with arbitrarily cooled stator vanes. The performance predictions of our machine learning model extend over the full (numerical) operating range of the test case and deviate by less than 1% from computational fluid dynamics (CFD) simulations on the test data set. We further demonstrate and discuss the accuracy and generalisation capabilities of this approach by predicting entire performance maps for different cooling configurations.

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Seasonal variations of the ambient conditions affect the performance of turbomachines. Compressors are particularly sensitive to these variations, which must be considered in order to obtain representative performance predictions. This paper investigates how variations in the inlet total temperature and inlet air-humidity due to seasonal changes propagate and how this affects compressor performance predictions. Sensitivities of the performance parameters to variations in the ambient conditions for different operating points are derived on the basis of this study. The results show that an increase in the inlet total enthalpy by different mechanisms, i.e. by a variation of the inlet total temperature and air-humidity, has different effects on the performance prediction of compressors and leads to dissimilar sensitivities. An increase in the inlet total temperature reduces the corrected mass-flow rate, the total-temperature ratio, the total-pressure ratio, and the isentropic efficiency. An increase in the air-humidity leads to higher corrected mass-flow rates and isentropic efficiencies. The total-pressure ratio and total-temperature ratio are reduced by a higher inlet air-humidity. This paper shows that the propagation of the inlet total temperature and inlet air-humidity variations depends on the operating point of the compressor.

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NOx emission characteristics of Micro-mix (MMX) burner for hydrogen aircraft under high pressure and temperature are investigated experimentally and numerically. Three types of experiment are conducted to investigate the effect of burner inlet temperature, inlet pressure, NOx emission and air fuel ratio (AFR) on NOx emission under high pressure and temperature are conducted. And steady RANS simulations with detailed chemistry and CHT analysis are performed and compared with the experimental data. The experimental results showed linear relationship between inlet temperature and Emission Index of NOx (EINOx). And EINOx increases in proportional to the 0.7 powers of inlet pressure of which index is higher than the generally known index of 0.5. The numerical simulation results suggested that the combustion process of MMX is composed of regimes starting with premixed combustion and ending with non-premixed jet combustion.

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