Numerical Optimization for Advanced Turbomachinery Design

The multilevel-multidisciplinary-multipoint optimization system developed at the von Kármán Institute and its applications to turboma-chinery design is presented. To speed up the convergence to the optimum geometry, the method combines an Artificial Neural Network, a Design Of Experiment technique and a Genetic Algorithm. The different components are described, the main requirements are outlined and the basic method is illustrated by the design of an axial turbine blade.

A procedure for multipoint optimization, aiming for optimal performance at more than one operating point, is outlined and applied to the optimization of a low solidity diffuser.

The extension to a multidisciplinary optimization, by combining a Navier-Stokes solver with a Finite Element Analysis, allows an efficient search for a compromise between the sometimes conflicting demands of high efficiency and respect of mechanical constraints. It is shown that a significant reduction of the stresses is possible with only a small penalty on the performance and that this approach may lead to geometries that would normally be excluded when using less sophisticated methods.