Three-Dimensional Optimization of Blade Lean and Sweep for a Transonic Axial Compressor and Investigation of the On-Design and Off-Design Engine Performance

Recently optimization methods have been considered by researchers to improve the performance of turbo-machines. This is made by coupling numerical methods with optimization algorithms. In this article, a genetic algorithm and artificial neural network, coupled with computational fluid dynamics, are being used for the three-dimensional optimization of an axial compressor. For this purpose, the numerical study of the aerodynamic flow of the NASA Rotor-67 axial compressor has been investigated. The geometry variation was done by using Lean and Sweep. A spline algorithm has been used to vary the Stacking line from the hub to the shroud. The results of this optimization are improvements in three objective functions at design speed. According to the best-optimized case results, the mass flow rate, pressure ratio, and isentropic efficiency of the design point have been increased by about 2.020% +, 1.297%, and 0.174%, respectively. Increasing the surge margin of the performance curve of the optimized geometry compared to the reference rotor is another result obtained for the optimal compressor. Improving the convergence of surface streamlines in delaying the shock on the blade is another factor in improving the optimal geometry's performance compared to the base one. Then, the effect of the best-optimized rotor is studied at the on-design and off-design steady-state performance of a turbojet engine. The results show that Thrust has improved at design and off-design speeds.