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 authors to enhance the turbo-machines' performance. in this article, the genetic algorithm (ga) and artificial neural network (ann) with computational fluid dynamics (cfd) are being coupled, and the optimization of nasa rotor-67, an axial compressor, has been simulated. the compressor flow field is simulated with cfd, and the results proved the excellent validation with experimental data. the rotor leaned and swept parametrization was modeled, and the results are improvements in design objective functions: pressure ratio, isentropic efficiency, and mass flow rate. 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. improving the convergence of surface streamlines in delaying the shock on the blade is another factor in improving the optimal rotor'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 matching code has been worked out by solving compatibility equations using the characteristic maps. the results show that thrust has improved at design and off-design speeds.