DEVELOPMENT OF A 3D MODEL OF A ROBOT FISH WITH EXPERIMENTAL ANALYSIS

Biomimetic underwater vehicle design has attracted the attention of researchers for various reasons such as ocean investigation, marine environmental protection, exploring fish behaviors and detecting the leakage of oil pipe lines. Fish and other aquatic animals have good maneuverability and trajectory following capability. They also efficiently stabilize themselves in currents and surges leave less noticeable wake than conventional underwater vehicles equipped with thrusters. This paper presents the hydrodynamic simulation of a biomimetic robot fish that is fabricated at the Advanced Dynamic and Control Systems Laboratory (ADCSL), University of Tehran. In order to simulate a fish-like swimming robot, a comprehensive hydrodynamic analysis was performed. Extensive study of the biology of fish particularly their motion was performed. Carangiform swimming mode, which is the swimming mode of fish that use their tail and peduncle for propulsion was chosen. A hydrodynamic simulation is performed using computational fluid dynamics. In simulation Reynolds averaged Navier-Stokes equations (RANS) and Large Eddy Simulation (LES) method were employed to solve turbulence conditions. In this analysis the fluid was supposed to be single phased and the flow to be distributed and incompressible. The presence of a complex shaped moving boundary makes a difficult proposition for the computational fluid dynamics. Dynamic mesh method is employed to simulate moving boundaries. A hydrodynamic model of a robot fish permits us to determine the properties of the robot fish and facilitates the development of control algorithms. In order to verify these numerical models, an experimental test bed is fabricated at ADCSL. Experimental results show a smooth, repeatable and controllable motion of the robot fish. Data gathered through this method demonstrated reasonable agreement with simulation results. Consequently, the numerical model maybe utilized for further analysis and optimization process to reduce the experimental trail and error process cost.