In this article, the heat transfer augmentation on a flat surface with jet impingement on axisymmetric detached ribs is numerically investigated. Both single and multiple jet impingement with and without crossflow interaction is investigated. Numerical simulations are done using Reynold-averaged Navier–Stokes (RANS) equations with a shear stress transport (SST) model with the commercial CFD code ANSYS-CFX. The influence of jet Reynolds number (7000 ≤ Re _j ≤ 78,000), blowing ratio (5.8 ≤ M ≤ 11.5), and jet-outlet-to-target wall distance (2 ≤ H/D ≤ 6) are examined. Results show that the heat transfer is enhanced on the target wall with detached ribs in single and multiple jet impingement at moderate crossflow speeds.

Non-uniform inlet flows frequently occur in aircrafts and result in chronological distortions of total temperature and total pressure at the engine inlet. Distorted inlet flow operation of the axial compressor deteriorates aerodynamic performance, which reduces the stall margin and increases blade stress levels, which in turn causes compressor failure. Deep learning is an efficient approach to predict catastrophic compressor failure, and its stability for better performance at minimum computational cost and time. The current research focuses on the development of a transonic compressor instability prediction tool for the comprehensive modeling of axial compressor dynamics. A novel predictive approach founded by an extensive CFD-based dataset for supervised learning has been implemented to predict compressor performance and behavior at different ambient temperatures and flow conditions. Artificial Neural Network-based results accurately predict compressor performance parameters by minimizing the Root Mean Square Error (RMSE) loss function. Computational results show that, as compared to the tip radial pressure distortion, hub radial pressure distortion has improved the stability range of the compressor. Furthermore, the combined effect of pressure distortion with the bulk flow has a qualitative and deteriorator effect on the compressor.

The flame surface density (FSD) based reaction rate closure is one of the most important methodologies of turbulent premixed flame modeling in the context of Large Eddy Simulations (LES). The transport equation for the Favre-filtered reaction progress variable needs closure of the filtered reaction rate and the subgrid scalar flux (SGSF). The SGSF in premixed turbulent flames has both gradient and countergradient components, where the former is typically modeled using eddy diffusivity and the latter can be modeled either on its own or in combination with the filtered reaction rate term using an appropriate wrinkling factor. The scope of the present work is to identify an explicit SGSF closure for the optimum performance in combination with an already established LES FSD model. The performance of different SGSF models for premixed turbulent combustion has been assessed recently by the authors using a Direct Numerical Simulation (DNS) database of freely propagating turbulent premixed flames with a range of different values of turbulent Reynolds number. The two most promising models have been implemented in the LES code. The modeling methodology identified based on a priori DNS analysis is assessed further a posteriori by comparing the LES simulation results of turbulent methane Bunsen flames with the well-documented experimental data. A significant change of the overall flame speed is not observed for different SGSF models. However, the flame shape and thickness respond to the modeling of SGSF. Considering the fact that the SGSF models have very different characteristics, the overall effect on the LES results in this work is smaller than expected. An extension of a previous a priori DNS analysis provides detailed explanations for the observed behavior.