AEROTHERMAL OPTIMIZATION OF SQUEALER GEOMETRY IN AXIAL FLOW TURBINES USING GENETIC ALGORITHM - ALPMAN, EMRE (YILDIZ TECHNICAL UNIV, 2018)
In turbomachines, a tip gap is required in order to allow the relative motion of the blade and to prevent the blade tip surface from rubbing. This gap which lay out between the blade tip surface and the casing, results in fluid leakage due to the pressure difference between the pressure side and the suction side of the blade. The tip leakage flow causes almost one third of the aerodynamic loss and unsteady thermal loads over the blade tip. Previous experimental and numerical studies revealed that the squealer blade tip arrangements are one of the effective solutions in increasing the aerothermal performance of the axial flow turbines. In this paper the tip leakage flow is examined and optimized with the squealer geometry as a means to control those losses related with the tip clearance. The squealer height and width have been selected as design parameters and the corresponding computational domain was obtained parametrically. Numerical experiments with such parametrically generated multizone structured grid topologies paved the way for the aerothermal optimization of the high pressure turbine blade tip region. Flow within the linear cascade model has been numerically simulated by solving Reynolds Averaged Navier-Stokes (RANS) equations in order to produce a database. For the numerical validation a well-known test case, Durham cascade is investigated in end wall profiling studies has been used. Sixteen different squealer tip geometries have been modeled parametrically and their performance have been compared in terms of both aerodynamic loss and convective heat transfer coefficient at blade tip. Also, these two values have been introduced as objective functions in the optimization studies. A state of the art multi-objective optimization algorithm, NSGA-II, coupled with an Artificial Neural Network is used to obtain the optimized squealer blade tip geometries for reduced aerodynamic loss and minimum heat transfer coefficient. Optimization results are verified using CFD.
Keywords: Multi-objective Optimization; Squealer; Tip Leakage Flow; ARTIFICIAL NEURAL-NETWORK; TIP-LEAKAGE FLOW; MULTIOBJECTIVE OPTIMIZATION; PERFORMANCE