Ali Madadi

Nowadays, optimization methods have been considered as a practical tool to improve the performance of turbo-machines. For this purpose, the numerical study of the aerodynamic flow of the NASA Rotor-67 axial compressor has been investigated, and the results of this three-dimensional simulation show good agreement with experimental data. Then, the blade stacking line is changed using lean and sweep for Rotor-67 to improve the compressor performance. The third-order polynomial is selected to generate the lean and sweep changes from the hub to the shroud. The compressor flow field is solved by a Reynolds averaged Navier-Stokes solver. The genetic algorithm, coupled with the artificial neural networks, is implemented to find the optimum values for blade lean and sweep. Considering the three objective functions of pressure ratio, mass flow rate, and isentropic efficiency, the optimized rotor is obtained using the optimization algorithm. Two geometries are obtained using the optimization algorithm. The results of the optimized compressor include improving the isentropic efficiency, pressure ratio, and mass flow equal to 0.57%, 0.93%, and 1.8%, respectively. After compressor optimization, the effect of the changes in the compressor performance parameters is studied on a single spool turbojet engine. The engine is modeled by analyzing the Brayton thermodynamic cycle of the assumed turbojet engine under design point operating conditions. Results show that for the best test case, the engine with the optimized rotor, the thrust, and SFC are improved by 1.86% and 0.21%, respectively.

An intake has to provide air for the engine uniformly with minimum total pressure loss. Nowadays, regarding the usage of S-shaped intakes, optimization of these ducts has been considered. Uniform distribution of flow at the compressor inlet directly influences the engine performance, and non-uniformity of flow increases surge occurrence possibility. Flow separation along the duct causes a reduction of pressure recovery and engine thrust force. This research has optimized an S-shaped intake to reduce the total pressure loss and flow distortion. The genetic algorithm and artificial neural networks have been combined to decrease the computational cost. Two optimizations, using different conditions, have been studied. In the first case, by modifying centerline coordinates and area ratio of sections, new geometries have been produced, which has caused an improvement of 32.5 % in pressure recovery coefficient and a decrease of 35.8% in flow distortion. In the second optimization, the shape of each section has also been changed. Superellipse, egg-shaped and circular profiles are considered as cross-sections of the duct. The second optimization has improved the pressure recovery coefficient by 35.5 % and decreased flow distortion by 39.2 %.

Recently, an inverse design method called the ball spine algorithm (BSA) is introduced to design S-duct diffusers with elliptic cross-sections. The technique is developed for the 3-D design of S-shaped ducts with special cross-sectional profiles in the present work. The upper and lower lines of the S-duct symmetric section are modified under the BSA. Two types of special cross-section profiles are used to improve the aerodynamic properties of the duct. At the S-duct inlet, the super-ellipse profile is used to minimize the duct inlet height. At the aft part of the duct, the egg-shaped profile is predefined to decrease the pressure difference between the upper and lower lines and to diminish the adverse pressure gradient along the upper line. Finally, the flow through the designed S-duct is numerically analyzed using a viscous flow solver with the SST turbulence model to validate the designed S-duct performance.