Tuning Hydrodynamic Coefficients Using a Genetic Algorithm for a Numerical Model of a Bio-Robotic Sea Lion
Shraman Kadapa, Nicholas Marcouiller, ANTHONY DRAGO, James Tangorra, Harry Kwatny
AI summary
Problem
Accurately estimating hydrodynamic coefficients for complex, shape-changing bio-robotic swimmers is critical for numerical modeling but traditionally relies on costly, impractical experimental methods that struggle with multi-body configurations.
Approach
The authors decomposed the robot into segments to estimate initial drag and added-mass coefficients via CFD and strip theory, then used a genetic algorithm to optimize these values against physical experiment data.
Key results
- Numerical model trajectories matched physical robot data with low RMSE (0.06 m and 0.04 m)
- Genetic algorithm successfully optimized drag, added-mass, and inertial parameters
- Model accurately captured progressive velocity gains and pitch angle changes across swimming cycles
- Validated framework enables cost-effective parametric studies and reinforcement learning for gait optimization
Why it matters
Provides a reliable, low-cost simulation tool for designing and optimizing control strategies in complex bio-inspired underwater vehicles without extensive physical prototyping.
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.