Enhancing AUV Dynamic Models with CFD-Derived Hydrodynamic Coefficients

Efficient navigation and manoeuvrability are crucial for Autonomous Underwater Vehicles (AUVs), widely used in underwater missions. This study proposes a numerical method to enhance the dynamic model of the Light Autonomous Underwater Vehicles (LAUVs) engineered by Laboratório de Sistemas e Tecnologia Subaquática (LSTS), which rely on dynamic models derived from the REMUS 100 AUV, a well-documented benchmarking model. The original dynamic model's reliance on analytical, empirical, and semi-empirical approaches introduces challenging assumptions, potentially compromising the accuracy of controller design and vehicle navigation. To address these issues, a comprehensive six degrees of freedom (DOF) Computational Fluid Dynamics (CFD) of the REMUS 100 was developed using ANSYS FLUENT. The study aimed to extract key hydrodynamic coefficients and derivatives for the AUV and fins like drag, lift, and added mass through steady-state and transient simulations across various degrees of freedom. Mesh sensitivity analysis identified that a medium grid with approximately 500,000 cells provided the optimal balance between accuracy and computational efficiency. Turbulence sensitivity studies confirmed that the K-kl-omega model was most effective for capturing laminar-transitional flow regimes. Results show that CFD-derived coefficients offer improved precision compared to available analytical and experimental data, highlighting the limitations of traditional methods. This enhanced accuracy supports the development of more reliable control systems, thereby improving AUV navigation and maneuverability.