A coupled framework for modeling fish schooling.

Area of Science:

• Computational Biology
• Fluid Dynamics
• Animal Behavior

Background:

• Simulating collective animal behavior, like fish schooling, requires integrating individual interactions with environmental dynamics.
• Existing models often face challenges in balancing computational efficiency with physical realism.

Purpose of the Study:

• To develop and present a loosely coupled simulation framework for fish schooling.
• To integrate social interactions (SPP model) with fluid dynamics (CFD model).
• To investigate emergent behaviors and hydrodynamic effects in schooling fish.

Main Methods:

• Utilized a self-propelled particle (SPP) model for fish social interactions and movement rules.
• Employed computational fluid dynamics (CFD) to simulate the fluid environment and fish hydrodynamics.
• Generated fish undulatory kinematics using a pre-trained deep reinforcement learning model.
• Coupled SPP and CFD models, allowing CFD to follow SPP trajectories with flexibility.

Main Results:

• The CFD trajectories closely approximated SPP model trajectories, offering a balance of realism and efficiency.
• Emergent behavior, such as side-slip for stable locomotion in trailing fish, was observed.
• Simulations of large schools showed increased group efficiency with higher Reynolds numbers due to favorable hydrodynamic interactions.

Conclusions:

• The loosely coupled SPP-CFD framework effectively simulates fish schooling, capturing complex interactions and emergent phenomena.
• The model provides a flexible approach for studying fish collective behavior with physical realism and computational efficiency.
• Hydrodynamic interactions play a significant role in the efficiency of large fish schools.