Fluid mechanics are fundamental to collective behavior in nature and technology ranging from fishschools to wind farms.
Our knowledge about the role of fluid mechanics in fish schooling, one of the most magnificent manifestations of collective behavior is limited. Here we wish to uncover the hydrodynamics of fish schooling using state of the art flow simulations and method of artificial intelligence.
Some of the questions we investigate include: Is schooling the result of vortex dynamics synthesized by individual fish wakes or the result of behavioral traits? Is fish schooling energetically favorable? What is the emerging structure in predating or escaping schools of swimmers?
We seek to answer these questions through computational methods that resolve the interaction of fluids with multiple, deforming bodies.
Our methods rely on the innovative coupling of flow solvers that have an adaptive resolution with machine learning algorithms deployed effectively on HPC architectures. Our goal is to provide valuable insights into the dynamics of fish schooling. This will open new horizons for understanding of collective behavior and contribute to the rational design of industrial applications involving collective flow phenomena such as robotic schools to wind farms.