Optimization and Computational Fluid Dynamics - Department of ...

182 René A. Van den Braembussche
Fig. 6.27 Convergence history of the optimization
split into dedicated ANNs enhances the accuracy by which important values
such as efficiency and maximum stress can be predicted.
A2 n−p factorial design is used where n is 23 while p is fixed at 17. This
results in a total of 2 6 = 64 samples in the initial database. An initial database
contains only 53 geometries since 13 geometries defined by the DOE technique
could not be analyzed due to geometrical constraints (intersection between
the main blade and the splitter blade). Two additional geometries have been
added, namely the baseline geometry and the central case. The latter is a
geometry with all parameters at 50% of their range.
Figure 6.27 shows the convergence history of the optimization. The “aero
penalty” (based only on NS predictions of the efficiency, the Mach number
distribution and mass flow), the “stress penalty” (based on the result of
the FEA analyses) and the “total penalty” are all compared to the ones
predicted by the ANNs. One observes a decrease in the discrepancy between
both prediction methods with iteration number. This is the consequence of
an increasing number of samples in the database, resulting in more accurate
ANNs.