Optimization of an internal blade cooling passage configuration using a Chimera approach and parallel computing.

Improving gas turbine performance depends almost exclusively on the maximum temperature of the combustion gases. This temperature is limited by the thermomechanical strength of the turbine vanes. In this work, a Chimera approach for overlapping grids in the finite element method (FEM) context is proposed to optimize the arrangement of several cooling passages within the vane to minimize its average temperature, thereby improving the thermal transfer efficiency. The scheme is based on a fixed background mesh covering the entire domain, while finer meshes surrounding the coolant passages can move around over the airfoil while searching for the best configuration of the cooling system. Information is exchanged between meshes through a high-order interpolation algorithm. The optimization strategy is developed based on the use of the population-based augmented Lagrangian particle swarm optimizer (ALPSO) to approach a probable global minimum, and then a refinement of the solution is performed using the gradient-based sequential least squares quadratic programming (SLSQP) algorithm. Both the gradient, for the gradient-based optimizer, and the function evaluations, for the population-based optimizer, are solved in parallel to speed up the solution. • A Chimera approach in the FEM context to address optimization problems is presented. • The refrigeration system of a gas turbine vane is enhanced. • Large displacements of the cooling passages are allowed without remeshing the domain. • Good speed up is obtained when using the parallel capabilities of pyOpt. • We provide a tool to efficiently enhance the thermal performance of engineering parts. [ABSTRACT FROM AUTHOR]