Optimization and Computational Fluid Dynamics - Department of ...

6 Numerical Optimization for Advanced Turbomachinery Design 161
Fig. 6.9 Flowchart of optimization system
The meta-function used in the present method is an Artificial Neural Network
(ANN). This interpolator uses the information contained in the database
to correlate the performance to the geometry, similar to what is done by an
NS solver. However, an ANN is a very fast predictor and allows the evaluation
of the numerous geometries generated by the GA with much less effort than
an NS solver. Unfortunately, a verification by means of a more accurate but
time consuming NS solver indicates that such a fast prediction is not always
very accurate. The results (geometry and performance) of this verification are
added to the database and a new optimization cycle is started. It is expected
that the new learning on an extended database will result in a more accurate
ANN. This procedure is repeated until the ANN predictions are in agreement
with the NS calculations, i.e., once the GA optimization has been made with
an accurate performance predictor. In this way, there will be no discrepancy
between the optimum found by a GA, driven by the meta-function or by the
results of NS analyses. However, the number of time-consuming NS analyses
is much smaller than what would have been required by a GA and NS
combination.
The TRAF3D NS solver [3] is used to predict the aerodynamic performance.
Similar grids with the same number of cells are used for all computations
to guarantee a comparable accuracy for all the predictions.