AEROTHERMAL OPTIMIZATION OF SQUEALER GEOMETRY IN AXIAL FLOW TURBINES USING GENETIC ALGORITHM

Year 2018, Volume: 4 Issue: 3, 1896 - 1911, 22.03.2018

Emre Alpman

https://doi.org/10.18186/journal-of-thermal-engineering.408701

Cited By: 7

Abstract

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

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