## 4 Results and Discussion
### Algorithm Design Exploration  
Figure 9 indicates the optimisation design exploration. The fitness values were plotted against optimisation objectives to show the influence of these parameters on design quality. Figure 9 shows fitness against turbine efficiency across all design evaluations. An expected linear behaviour pattern is clearly recognisable, as the parameter acts as the main influence. Compared to the total number of design evaluations, an extremely low percentage of random and pseudo-random blades were created with efficiencies below 0.7. The results are encouraging in terms of design process evaluation, as one could conclude that a close to ideal starting point and ranges where selected. Failing elements majority falls between 0.72 and 0.74 efficiency. While it is hard to speculate on such information, the higher-than usual fail rate would principally be a product of crossover between two very different blades with good performance data, creating data incompatibility in the offspring, causing them to fail. Figure 9 shows fitness values against the average safety factor (SF) across stator and rotor structural analysis. It is possible to discern an ideal value between 2.5 and 3. As shown in subsequent Figures, this is likely due to hub thicknesses and wedge angles increasing due to lower throat values required for optimisation. The increase in safety factor means, however, that blade influence the flow in a more invasive way to the higher occupied volume, leading to performance drops after the optimum at 2.75. For higher SF applications, however, turbines are showing surprising fitness values of \(\sim\)77 at an average SF of 4. Figure 9 shows fitness values against the maximum global Mach number. Comparing these scatter plot to the other ones for other design constraints, it is possible to notice the scarcity of failed objects within the constraint's boundaries. This is to be expected as low Mach numbers lead to lower forces on the part, increasing SF. It is not possible to interpret data to extrapolate any sort of particular relationship between this result and fitness values. From a theoretical point of view, the faster the flow would be, the better its performance. Majority of elements, including the final result, are located between 1.25 and 1.28 Mach. While this could be considered the optimum, it is more likely that the genetic pool evolved towards values with a safety margin on such a restrictive penalty to fitness. A lighter penalisation during fitness function definition would likely lead to Mach numbers closer to the accepted maximum values, but the gene pool might simply develop to overcome light penalisation, by proving hyper efficient, powerful blades with excessive Mach values. Figure 9 shows fitness function values against power. While there theoretically is a linear relationship between these two parameters, it did not show in the simulation results. This could be because of a relationship between efficiency and power close to inverse proportionality at high powers. While the outlet static pressure boundary condition did not impact efficiency simulations results in a meaningful way, produced power was highly affected, as high total-to-static pressure ratio turbines (i.e., more efficient) would have led to lower static pressure outlets. In conclusion, high static pressures at the outlet in CFD compromised power estimations. On the other hand, the group was interested in a blade that would produce at least the same power of the baseline design with the same boundary conditions, thus validating the choice of those boundary conditions. Moreover, the degree of correlation between CFD calculated power and simulated power in Ricardo WAVE was extremely high, meaning that design quality evaluation algorithm was consistently underestimating power output, thus effectively representing the changes in design correctly in the fitness function values.