Surrogate models for the prediction of the aerodynamic performance of exhaust systems

The aerodynamic performance of the exhaust system is becoming more important in the design of engines for civil aircraft applications. To increase propulsive efficiency and reduce specific fuel consumption, it is expected that future engines will operate with higher bypass ratios, lower fan pressure ratios and lower specific thrust. At these operating conditions, the net thrust and the specific fuel consumption are more sensitive to losses in the exhaust. Thus the performance of the exhaust needs to be accurately assessed as early as possible during the design process. This research investigates low-order models for the prediction of the performance of separate-jet exhaust systems, as a function of the free-stream Mach number, the fan nozzle pressure ratio and the extraction ratio (fan to core pressure ratio). In the current practice the two nozzles are typically considered in isolation and the performance is modelled as a function of their pressure ratio. It is shown that the additional degrees of freedom have a substantial impact on the metrics describing the performance of the exhaust system. These models can be employed at a preliminary design stage coupled with engine performance models, which require as input the characteristics of the exhaust system. Two engines, which are representative of current and future large turbofan architectures are studied. The low-order models investigated, generalized Kriging and radial basis functions, are constructed based on data obtained with computational fluid dynamics simulations. The data represents the characteristics of the exhaust of each engine, and they are provided for the first time for a wide operational envelope. The influence on accuracy of the type of surragate model and its settings have been quantified. Furthermore, the trade-off between the accuracy of the model and the number of samples has been identified. It is found that the exhaust performance metrics can be modelled using a low-order model with sufficient accuracy. Recommendations on the best settings of the model are also provided.