No abstract

The boundary layer ingestion (BLI) concept has emerged as a novel technology for reducing aircraft fuel consumption. Several studies designed BLI-fans for aircraft. BLI-propellers, although, have still received little attention, and the choice of open-rotors or ducted propellers is still an open question regarding the best performance. The blade design is also challenging because the BLI-propulsors ingest a non-uniform flow. These aspects emphasize further investigation of unducted and ducted BLI-propulsors and the use of optimization frameworks, coupled with CFD simulations, to design the propeller to adapt to the incoming flow. This paper uses a multi-objective NSGA-II optimization framework, coupled with 3D RANS simulations and Radial Basis Function (RBF) meta-modelling, used for the design and optimization of three propeller configurations at cruise conditions: (a) conventional propeller operating in the free stream, (b) unducted BLI-propeller and (c) ducted BLI-propeller, both ingesting the airframe boundary layer. The optimization results showed a significant increase in chord and a decrease in the blade angles in the BLI configurations, emphasizing that these geometric parameters optimization highly affects the BLI-blade design. The unducted BLI-propeller needs approximately 40% less shaft power than the conventional propeller to generate the same amount of propeller force. The ducted BLI-propeller needs even less power, 47%. However, the unducted and ducted BLI-configurations presented a higher backward force, 26% and 46%, respectively, compared to the conventional propeller, which can be detrimental and narrow the use of these configurations.

No abstract

Film cooling is an important technique to ensure safe operation and performance fulfillment of turbines. Its ultimate goal is to protect the axial turbine blades from high gas temperatures. An appropriate study is necessary in order to obtain a reliable representation of the flow characteristics involved in such phenomena. Because of the high computational cost of high-fidelity simulations, the low-fidelity simulation method Reynolds Averaged Navier Stokes (RANS) is commonly used in practical configurations. However, the majority of the current turbulent heat flux models fail to accurately predict heat transfer in film cooling flows. Recent work suggests the use of machine learning models to improve turbulent closure in these flows. In the present work, a machine learning model for spatially varying turbulent Prandtl number previously described in the literature is applied to a transverse film cooling flow consisting of a jet square channel. The results obtained in the present work were compared to adiabatic effectiveness experimental data available in the literature to assess the performance of the machine learning model. The results shown that for low blowing ratios (BR = 0.2 and BR = 0.4) the proposed machine learning model has poor performance. However, for the case with the highest blowing ratio (BR = 0.8), the proposed model presented better results. These results are then explained in terms of the resulting turbulent Prandtl number field and suggest that the training set is not appropriate for capturing the turbulent heat flux in fully attached jets in crossflow.