Tuning Hydrodynamic Coefficients Using a Genetic Algorithm for a Numerical Model of a Bio-Robotic Sea Lion

Abstract

Bio-inspired swimming vehicles are increasingly being developed to understand the locomotion strategies of aquatic animals to expand the performance envelope of engineered systems. However, the increasing complexity of these multi-segmented vehicles makes it challenging to understand and optimize their performance. Accurate numerical models of these systems can provide a pathway forward, but it depends critically on reliable estimation of hydrodynamic coefficients. Traditional approaches to estimate these coefficients, such as tow-tank testing can be costly and often impractical. In this work, a numerical model of a bio-robotic sea lion was developed and validated, in which hydrodynamic coefficients critical for estimating fluid forces were first obtained through computational fluid dynamics (CFD) simulations and analytical methods such as strip theory. These coefficients were then refined using a genetic algorithm to improve agreement with experimental trials of the robot. This hybrid framework bridges the gap between simulation and reality, enabling accurate force estimation across different body segments. Validation experiments showed a close alignment between the numerical model and the physical robot's performance in position and orientation during various trials. The validated model could enable large-scale parametric studies to evaluate the effectiveness of different control surfaces, optimize gaits, and explore control strategies without extensive prototyping of the bio-robotic platform. Beyond design and analysis, the model can also provide a high-fidelity environment for the application of reinforcement learning, supporting the development of adaptive controllers and advancing bio-inspired robots toward autonomous operation.