The heat transfer remains a huge problem for industrialists and engineers because many production processes required considerable amount of heat to finish the process successfully. Although, conventional fluids have large scale industrial applications but unable to provide huge amount of heat transfer. Therefore, the study is organized to propose a new ternary heat transfer model using different physical constraints. The key applications area of nanofluid heat transfer are chemical, applied thermal and food processing engineering. and Methodology: The key purpose of this research is introduce a new ternary nanofluid model using the impressive effects of thermal radiations, surface convection and saddle/nodal points. The results simulated via RKF-45 and discussed in detail. The strength of Al₂O₃ nanoparticles form 1%–7% (keeping fixed CuO and Cu as 4% and 6%) and s₁ = −0.2,-0.4,-0.6,-0.8 controlled the fluid movement while s₁ = 0.2,0.4,0.6,0.8 boosted the velocity. Increasing the convection process B_i = 0.1,0.2,0.3,0.4 increased the temperature significantly. Further, shear drag is maximum for ternary nanofluid and thermal radiations R_d = 0.1,0.2,0.3,0.4 enhances the heat transfer rate.

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.

Horizontal axis wind turbines are used for energy generation at domestic as well as industrial levels. In the wind turbines, a reduction in drag force and an increase in lift force are desired to increase the energy efficiency. In this research work, computational fluid dynamics (CFD) analysis has been performed on a turbine blade’s frontal section with an NACA S814 profile. The drag force has been reduced by introducing an array of dimpled structures at the blade surface. The dimpled structures generate a turbulent boundary layer flow on its surface that reduces the drag force and modifies the lift force because it has greater momentum than the laminar flow. The simulation results are verified by the experimental results performed in a wind tunnel and are in close harmony with the simulated results. For accurate results, CFD is performed on the blade’s frontal section at the angle of attack (AOA) with a domain of 0° to 80° and at multiple Reynolds numbers. The local attributes, lift force, drag force and pressure coefficient are numerically computed by using the three models on Ansys fluent: the Spalart-Allmaras, the k-epsilon (RNG) and the k-omega shear stress transport (SST).

The unfavorable effects of non-uniform temperature inlet flow on gas turbine engine operations have always been a hindrance on the performance of turbo-fan engines. The propulsive efficiency is a function of the overall efficiency of turbofan engine which itself is dependent on other ambient parameters. Variation of inlet compressor temperature due to increase or decrease of aircraft altitude, air density, relative humidity, and geographical climate conditions affects the compressor performance. This research focuses on the turbofan transonic compressor performance due to ambient temperature distortion. A novel predictive approach based on neural network model has been implemented to predict the compressor performance and behavior at different ambient temperature conditions. The model produces substantially accurate results when compared to the results of CFD analysis. Computational results from CFD analysis show that engine thrust decreases at higher altitude, lower density and lower pressure regions.