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The purpose of this paper is to find the optimum design of a typical gas turbine exhaust diffuser. In order to access the maximum overall static pressure recovery at the condition of swirling flow, an evolutionary algorithm is used. The optimization process is studied in three independent cases. Firstly, the optimization is done for a single profile of strut cover from hub to shroud. Secondly, two profiles are selected for the strut covers, one in the hub section and the other in the shroud section. Finally, the optimization process is done for the strut cover and diffuser channel geometries simultaneously. In order to produce the strut cover profiles the PARSEC parameterization method is used. The turbulent 3D flow is solved using computational fluid dynamic (CFD). The optimization process starts with the initial sampling of solution domain and subsequently the genetic algorithm (GA) is used to find the global optimum. The swirling flow at the turbine exit with the Reynolds number of 1.7 ×105 based on the hydraulic diameter of the diffuser inlet is optimized. All steps of GA and corresponding processes of model creation, mesh generation by TurboGrid, flow simulation by ANSYS CFX and goal function calculation for all members of each generation are coded in the MATLAB platform. As a result of the optimization, the pressure recovery coefficients increased 1.94%, 3.1% and 7.42% in the first, second and third cases of the optimization process respectively.

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Gravitational search algorithm (for the first time) has been used for two-objective optimization of airfoil shape, in this article. 2D compressible Navier-Stokes equations with Spalart-Allmaras model has been used to simulate viscous and turbulent flow. First, efficiency and accuracy of the optimizer sets have been evaluated using inverse optimization. Objective functions were differences between drag and lift with their corresponding values of the NACA0012 objective airfoil, as a set of airfoils randomly were chosen as starter airfoils, in this case and the aim was to obtain the airfoils that satisfy the considered objective functions. In direct optimization, gravitational search algorithm that has been used in the present work, has achieved proper parameters (related to the Parsec method) and consequently has found optimized airfoils with maximum lift and minimum drag objective functions. This algorithm starts to slove using a set of airfoils and it is directed towards the airfoils that provide the mentioned objective functions. Comparison of the results (Pareto fronts) shows better and more proper performance of the gravitational search algorithm rather than particle swarm optimization algorithm and former researches (done using other meta-heuristic algorithms) for aerodynamic optimizations.

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In recent years, due to the optimal geometry and lower pressure drop, diffusion control vanes have significant applications, especially in the aviation industry and in subsonic and transsonic conditions. In the current research, the airfoil of the axial stator compressor section designed in the National Aerospace Laboratory of India has been selected as the basic geometry. The goal of optimization is to minimize the pressure drop of the entire fluid flow and consequently reduce the drop rate. The working method in this research is the change in the profile geometry of the blade by changing the parameters of the parsec method, which leads to the creation of new geometries at each stage of the code execution. The used optimization method is developed based on Genetic Algorithm. For the aerodynamic analysis of the generated geometry in each step and extracting the total pressure drop value, the MATLAB code is coupled with Ansys software and in each step, after numerical solution for each generated geometry, the total pressure drop value is extracted and returned to the code. Finally, the work output of the vane is more optimal and with a lower pressure drop, which is finally compared with the original vane and introduced as a suitable alternative. The total pressure drop between inlet and outlet in the optimized vane has decreased by 18% compared to the original vane, and the mass flow rate has also increased by 0.083 kg/s, which is a significant amount. The improvement of various aerodynamic characteristics such as Mach number distribution and pressure and drop coefficients can also be seen between the two basic and optimized blades, which is detailed at the end of the article