Polagye, Brian L.Harrison, Trevor William2021-08-262021-08-262021-08-262021Harrison_washington_0250E_23106.pdfhttp://hdl.handle.net/1773/47639Thesis (Ph.D.)--University of Washington, 2021Buoyancy-controlled underwater floats have produced a wealth of in situ observational data from the open ocean. When deployed in large numbers, or 'swarms,' floats offer a unique capacity to simultaneously map, in three-dimensions, environmental variables, such as currents, temperatures, and dissolved oxygen. This sensing paradigm is equally relevant in coastal waterways, yet remains underutilized due to economic and technical limitations of existing platforms. This dissertation describes the development, performance, and utilization of a prototype coastal float swarm. Chapter 2 describes a virtual experiment evaluating performance of float swarms in providing high-resolution, four-dimensional mapping of currents in tidally-dominated, coastal settings. They are assessed against standard methods for measuring currents: an X-band radar, a stationary (bottom-mounted) acoustic Doppler current profiler (ADCP), and a mobile (vessel-based) ADCP. Using the output from a hydrodynamic simulation, a virtual field campaign was performed at twenty-four locations in Admiralty Inlet, Puget Sound, Washington during spring and neap tidal exchanges. A reconstruction of the volumetric currents was generated for each platform every 15 minutes and evaluated against the true currents to assess accuracy over a horizontal extent of 400 x 500 m at 5 m resolution and vertically through the entire water column (20-80 m) at 2 m resolution. Simulation results suggest that, for this survey extent and resolution, a vessel-based ADCP survey is most accurate, followed closely by the float swarm. The overall performance hierarchy persists over most locations and stages of the tide. Thus, if mapping currents at high resolution (< 10 m) and short time scales (< 1 day) is the primary scientific objective, vessel-based ADCP surveys are likely the best option. For longer duration surveys, a combined deployment with a stationary ADCP and X-band radar system is the best choice. Lastly, if in situ measurements of scalar properties (e.g., salinity, temperature, dissolved oxygen) are desired, float swarms offer the unique capability of simultaneously sampling these properties while also surveying currents with accuracy comparable to mobile ADCPs. Encouraged by the simulated effectiveness of floats swarms, a prototype swarm of twenty-five inexpensive, buoyancy controlled floats, dubbed μFloats (''microfloats'') was developed, filling a technological gap in coastal observational platforms. μFloats move vertically in the water column by controlling their buoyancy, but are otherwise Lagrangian. Underwater positioning is achieved by acoustic localization using low-bandwidth communication with GPS-equipped surface buoys. The μFloat features a high-volume buoyancy-engine that provides a 9% density change, enabling automatic ballasting and vertical control from fresh to salt water (~ 3% density change) and reserve capacity for external sensors. Chapter 3 describes the μFloat system in detail, and provides background on float dynamics and controls. In Chapter 4, we outline three field tests undertaken to benchmark buoyancy control and acoustic localization accuracy: a quiescent-flow test in Lake Washington, WA; a controlled disturbance test in a 4 m deep salt water test tank; and an energetic tidal channel deployment in Agate Pass, WA. Results demonstrate depth-holding accuracy within ± 0.2 m of target depth in quiescent flow and ± 0.5 m in energetic flows, which is superior to that reported for other floats in the literature, and matched only by a float with hybrid buoyancy-engine and propeller-driven combined control technique. In the salt water test tank, divers added calibrated weights to the float, emulating disturbances from vertical currents and water density changes and demonstrated the floats' ability to correct for both with minimal deviation from the target depth. Unburdened by auxiliary sensors, μFloats can achieve a terminal velocity of ~ 0.5 m/s, though overshoot and time to settle at target depth are improved by constraining the vertical speed to ~ 0.3 m/s. This performance proved satisfactory for tidal channel deployments in Agate Pass: even in high currents (> 2 m/s) and a short region of interest (~ 1 km), float were able to reach and maintain depth on operationally relevant timescales (~ 30 seconds). Additionally, range, robustness, and accuracy of the underwater acoustic localization array was evaluated based on results from both Lake Washington and Agate Pass. A strong thermocline in Lake Washington resulted in downward refraction of acoustic localization messages, reducing connectivity and requiring supplementary post-processing to extract accurate float trajectories. Nonetheless, localization accuracy was demonstrated to within ± 5 m during periods with sufficient connectivity. Localizations in Agate Pass were similarly successful. While the acoustic environment was more favorable, the drift paths of GPS-track surface buoys produced inferior array configurations that resulted in larger localization errors. Tidal currents, particularly in narrow channels, can be challenging to characterize, due to high current speeds (> 1 m/s), strong spatial gradients, and relatively short synoptic windows. Chapter 5 presents, in detail, the μFloat deployments in Agate Pass, WA, which represent the first use of float swarms toward small-scale (< 1 km), high resolution (< 5 m) measurements characterizing mean currents in energetic tidal channels. Float data was evaluated against data products from station-keeping and drifting ADCPs. Bearing out the simulation results from Chapter 2, we show that a modest float swarm can provide water velocity data with similar quality to ADCPs, while simultaneously capturing in situ properties that cannot be remotely observed, such as temperature. In summary, this work contextualizes float swarm data products against standard in situ and remote observational methods, providing both a measure of anticipated accuracy and operational practicality. Second, it describes the design and benchmarking of the μFloat system, which fills a technological gap in coastal observation platforms. Finally, the scientific relevance of the μFloat system is demonstrated in a novel tidal channel survey, with data products shown to agree with standard instruments, while expanding operational possibilities for coastal sensing. Future developments will build on these proven capabilities to explore the dynamics, physical properties, and soundscapes of our coastal waters.application/pdfen-USCC BY-SAbuoyancy-controlcoastal oceanographyfloatinstrumentationswarmtidal currentsMechanical engineeringOcean engineeringPhysical oceanographyMechanical engineeringThesis