Abstract
Going with the flow, through use of drifters, is a cost-effective approach for obtaining valuable information on the flow field and sound scape in high-energy tidal environments. Applying a simple and cost-effective philosophy to ocean observation, an inexpensive low-profile surface flow drifter (SF drifter) was developed in 2012 for initial assessment of potential tidal energy development opportunities as part of the OERA funded “Southwest Nova Scotia Tidal Energy Resource Assessment”. The SF drifters provide a cost-effective approach for obtaining information on the flow field needed for initial site selection and for planning next steps in detailed site characterization. The drifters have also been used to supply information for “micro-siting” turbine berth locations and numerical model validation as part of the NRCan ecoEII project, “Reducing the cost of in-stream tidal energy generation through comprehensive hydrodynamic site assessment” (ecoEII project). The GWTF project built upon previous success by the Project Team, and addressed limitations in drifter design by increasing functionality and making them useful at large scale sites such as the Minas Passage, including the FORCE site. Drifter development included adapting the platform to increase payload capacity enabling the drifters to carry a tracking device and an ADCP. Proof of concept testing was carried out in the Digby Region, followed demonstration and commercial application in the Minas Passage. In addition to sensor development, research was conducted to advance data processing and assessment techniques. The calculation of surface flow speeds from GPS drifter data is relatively straight forward. However, the amount of measurements collected in surface flow field mapping requires smart and robust methods for data collection, processing, and analysis. To help streamline data processing, Luna Ocean developed a drifter data processing module as part of the Luna Ocean Data Analysis Software (LODAS). The tide time (T) and relative tidal range (R) parameters are useful for data analysis. The surface flow speed data are organized by T and indexed by R, then used to generate flow field maps that highlight areas of potential development for tidal energy projects, provide information for designing tidal power systems, and a means for testing (and improving) the spatial and temporal accuracy of numerical models. The surface flow measurements are also useful for combining with concurrent bottom mounted ADCP data collection to fill near surface gaps in ADCP data sets. A summary of surface flow assessments to date for Grand Passage is provided in short videos available at https://vimeo.com/lunaocean/driftgp and https://vimeo.com/lunaocean/driftgp2. The ADCP drifter has undergone successful proof-of-concept level development, including deign, build, deploy, and data processing. Luna Ocean is continuing to work with the Dalhousie Ocean Acoustics Laboratory on ADCP drifter design and data processing and analysis software. With funding support from the GWTF project the Nortek Signature 500 kHz ADCP has received an upgrade to the heading, pitch, and roll sensors that will allow us to conduct research focused on evaluating the spatial variability in turbulence, including the vertical structure of turbulent dissipation rate, and vorticity at the surface. Comparison of measurements from the SF drifters to FVCOM (the Model) simulations of tidal flows through Grand Passage show: a) the majority of Model predictions to be within 0.5 m/s of the SF drifter measurements, with several locations within 0.1 m/s, and b) spatial and temporal variation in the performance of the Model with a tendency for the model to be a bit faster than measurements in most locations in Grand Passage, a known model issue. Luna Ocean is continuing to conduct research with Acadia University on calibration of the Model using surface flow measurements. A draft video showing Minas Passage SF drifter data compared to flow speed predictions from the Model is available at https://vimeo.com/lunaocean/driftmp.