4.2.1. Optimal Amount of Clusters

The K-Means clustering algorithm uses the number of clusters as an input parameter. The optimal amount of six clusters was obtained beforehand by using the "elbow method" [54] as in the first workshop [12]. The generated clusters are visualized in Figure 8 and described in Table 3.

### 4.2.2. Cluster Metrics

The metrics used to describe the clusters in Table 3 have been introduced in Reference [12]. These are defined as follows:


According to Table 3, no cluster yields a solution which is a definitive global optimum considering all three criteria simultaneously—the maximal solution scores in each cluster vary within a 5% interval referred to the average solution space score. Meanwhile, the average cluster scores are distributed around the global solution space average, exhibiting normalized scores between 0.96 and 1.05.

Nevertheless, if a compromise among all three criteria is sought, one recognizes the solution marked with the letter D. It has a normalized score of merely 8% above the solution space average, which is still the maximal solution score in the whole space. This morphing configuration implements morphing of the entire airfoil with mechanical components solely near the wingtips.
