Marine Robotic Vechicles

Objective: In this study we investigate the dynamics of a marine robotic vehicle (MRV) for deep-sea environments. The objective is to estimate the forces acting on the MRV for different scenarios. Computational Fluid Dynamics (CFD) techniques were used to calculate drag and lift on the vehicle for various operational conditions (e.g., angle of attack, shapes of the vehicle and sub-components, fluid velocity, etc.

Problem Definition: Prior to deployment the marine robotic vehicle (MRV) is fabricated with different types of payloads for and the vehicle performance needs to be estimated. Hence, the forces acting on the MRV need to be estimated for different operational conditions. This requires a detailed external flow analysis over the geometry of the drone. For a given range of angle of attack the lift and drag forces that would be encountered can be estimated using CFD simulations. Other forces to look into would be the actuator loads and the increase in pressure as the MRV submerges into various depths in the sea. Since these MRV are designed to operate in deep sea environments for extensive periods of time, while running on limited battery power they need to be highly energy efficient for the actuation schemes. The applications for these MRV can range from scientific study of marine environments to energy industries. By conducting CFD simulations the drone can be further modified to adapt to variety of surface and body forces for the anticipated range of operational conditions.

General Procedure: Multiple CFD simulations were performed to best predict the range of surface and body forces experienced by the MRV for a specified set of operational conditions. The hardware facilities and the commercial software tools Star-CCM+ available at the Supercomputing Center at Texas A&M University were used for performing these calculations.

Acknowledgements: We gratefully acknowledge the research sponsorship from MRV Systems MRV Systems for performing this study. We gratefully acknowledge the complimentary access to the Supercomputing Center at Texas A&M University, that is made available free of charge to students, staff and faculty.

Graduate Students: Nicholas Niedbalski

Undergraduate Student: Ryan Von Ness

Project funded by: Marine Robotic Vechicles