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Featured Application: Development of a transonic compressor instability prediction tool under distorted inlet flow conditions using extensive CFD runs for a supervised learning dataset. ANN with optimal algorithm and different regression learning has been selected to produce all-inclusive transonic compressor rotor performance and behavior at different inlet conditions. 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. [ABSTRACT FROM AUTHOR]

Replaces tangential blowing with alternating tangential blowing/suction in boundary layer control. Implication of boundary-layer separation; Numerical simulations based on Navier-Stokes equations; Variation of the aerodynamic forces.