| Summary: | Degree: Master of Science Abstract: Natural Resources Canada (NRCan) has been using an autonomous underwater vehicle (AUV) to map the vast Canadian Artic seabed. The logistics of under-ice survey is currently complicated due to limited lifetime of lithium-ion batteries of AUVs. An AUV with longer endurance would allow exploration of a larger seafloor area from a single Arctic camp, reducing costs and advancing the rate of exploration. A 1:15 scaled-down model of NRCan AUV was designed and manufactured to test different passive drag reduction techniques with the goal of improving its endurance in future. The developed scaled-down underwater vehicle has a modular design consisting of the front edge, central body, and aft body sections. The main component of the aft body is a replaceable cylinder of 0.190 m length (l), where the different drag reduction techniques are applied. The aft body module is mounted on a submersible s-beam load cell for direct measurement of the axial force (i.e., drag) on this module. The vertical forces of weight and buoyancy in the aft body have been balanced in order to avoid off-axis forces and momentums on the load cell. Three passives drag reduction methods were studied, including: superhydrophobic surfaces (SHS), air injection, riblets, and also their combinations. The experiments were carried out in the high-speed water loop of the University of Alberta, over a Reynolds number (Re) range of 5.0×10^5 to 1.5×10^6 (based on the free-stream velocity of 0.9 to 3.3 m/s and model length of 0.508 m. The test section of this facility is equipped with transparent walls for visualization and is 0.25 m wide, 0.45 m high and 2.1 meters long. For the study of an SHS with random texture, direct measurement of the drag force is complemented with the simultaneous use of shadow-based long-range microscopic particle tracking velocimetry (micro-PTV). The use of the shadowgraph technique over the body-of-revolution allowed the visualization of the air plastron and its characteristic over time and ...
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