Deep Learning Model of Flow Interactions in Large Schools

Computations are presented for two to ten foils pitching about their leading edges and aligned side-by-side. By examining their thrust, lift and efficiency performance optimal arrangements are determined that maximize the performance of individuals within the school. In contrast, the collective performance is increased with the compactness of the school. Distinct performance differences between interior and exterior swimmers are observed. Given this insight, interior swimmer and exterior swimmer deep learning models are developed. The neural networks are trained on nearest-neighbor interactions from a small school and, using the nearest-neighbor formulation, are able to predict the performance of large schools.