Highlights

• Flat heave plates produce greater reaction forces than hexagonal conic topologies.

• Enclosing fluid in a previously open hexagonal conic reduces its reaction force.

• For KC = 1—2, vortex and force asymmetry depend on experimental initial condition.

This study presents an efficient system that smooths fluctuations in electrical power from a cross-flow (i.e., "vertical-axis") turbine. The proposed solution is a two-stage approach consisting of a low-pass filter and a bi-directional buck-boost converter. The design and stability characteristics of the system are presented, followed by time-domain simulation and validation against small-scale experiments. When this validated simulation is applied to a full-scale system, we demonstrate a 99% root mean square reduction in fluctuating power output with only a 3% drop in electrical system efficiency. This could allow intracycle control strategies to increase mechanical power output without causing electrical power fluctuations that are incompatible with direct use.

Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass, and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations, new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as emerging glider operations (EGO) should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.

Integrated instrumentation packages are an attractive option for environmental and ecological monitoring at marine energy sites, as they can support a range of sensors in a form factor compact enough for the operational constraints posed by energetic waves and currents. Here we present details of the architecture and performance for one such system — the Adaptable Monitoring Package — which supports active acoustic, passive acoustic, and optical sensing to quantify the physical environment and animal presence at marine energy sites. we describe cabled and autonomous deployments and contrast the relatively limited system capabilities in an autonomous operating mode with more expansive capabilities, including real-time data processing, afforded by shore power or in situ power harvesting from waves. Across these deployments, we describe sensor performance, outcomes for biological target classification algorithms using data from multibeam sonars and optical cameras, and the effectiveness of measures to limit biofouling and corrosion. On the basis of these experiences, we discuss the demonstrated requirements for integrated instrumentation, possible operational concepts for monitoring the environmental and ecological effects of marine energy converters using such systems, and the engineering trade-offs inherent in their development. Overall, we find that integrated instrumentation can provide powerful capabilities for observing rare events, managing the volume of data collected, and mitigating potential bias to marine animal behavior. These capabilities may be as relevant to the broader oceanographic community as they are to the emerging marine energy sector.

When experimentally evaluating the performance of a wind or water current turbine, one must impose a regulating torque on the turbine rotor by electrical or mechanical means. Some options limit this controlling torque to a purely resistive quantity, while servomotors and stepper motors allow torque to be applied in the direction of turbine rotation. Any control mode that results in net positive power for a turbine may be of interest for energy harvesting, and all of these are net "fluid-driven." Here, we present experiments that characterize the power, torque, and force coefficients of a cross-flow turbine operated at a constant rotational speed or under a constant imposed control torque. Time- and phase-average performance coefficients are largely equivalent for the two strategies although torque-regulated control is restricted to a narrower range of rotational speeds and the two strategies result in slightly different blade kinematics.

Velocity spectra are essential in characterizing turbulent flows. The Acoustic Doppler Velocimeter (ADV) provides three-dimensional time series data at a single point in space which are used for calculating velocity spectra. However, ADV data are susceptible to contamination from various sources, including instrument noise, which is the intrinsic limit to the accuracy of acoustic Doppler processing. This contamination results in a flattening of the velocity spectra at high frequencies (O(10)Hz).

This paper demonstrates two elementary methods for attenuating instrument noise and improving velocity spectra. First, a "Noise Auto-Correlation" (NAC) approach utilizes the correlation and spectral properties of instrument noise to identify and attenuate the noise in the spectra. Second, a Proper Orthogonal Decomposition (POD) approach utilizes a modal decomposition of the data and attenuates the instrument noise by neglecting the higher-order modes in a time-series reconstruction. The methods are applied to ADV data collected in a tidal channel with maximum horizontal mean currents up to 2 m/s. The spectra estimated using both approaches exhibit an f^-5/3 slope, consistent with a turbulent inertial sub-range, over a wider frequency range than the raw spectra. In contrast, a Gaussian filter approach yields spectra with a sharp decrease at high frequencies.

In an example application, the extended inertial sub-range from the NAC method increased the confidence in estimating the turbulent dissipation rate, which requires fitting the amplitude of the f^-5/3 region. The resulting dissipation rates have smaller uncertainties and are more consistent with an assumed local balance to shear production, especially for mean horizontal currents less than 0.8 m/s.

Flow-noise resulting from oceanic turbulence and interactions with pressure-sensitive transducers can interfere with ambient noise measurements. This noise source is particularly important in low-frequency measurements (f < 100 Hz) and in highly turbulent environments such as tidal channels. This work presents measurements made in the Chacao Channel, Chile, and in Admiralty Inlet, Puget Sound, WA. In both environments, peak currents exceed 3 m/s and pressure spectral densities attributed to flow-noise are observed at frequencies up to 500 Hz. At 20 Hz, flow-noise exceeds mean slack noise levels by more than 50 dB. Two semi-empirical flow-noise models are developed and applied to predict flow-noise at frequencies from 20 to 500 Hz using measurements of current velocity and turbulence. The first model directly applies mean velocity and turbulence spectra while the second model relies on scaling arguments that relate turbulent dissipation to the mean velocity. Both models, based on prior formulations for infrasonic (f < 20 Hz) flow-noise, agree well with observations in Chacao Channel. In Admiralty Inlet, good agreement is shown only with the model that applies mean velocity and turbulence spectra, as the measured turbulence violates the scaling assumption in the second model.

Integrated instrumentation packages designed for operation at marine renewable energy sites have the potential to reduce the risk uncertainty around high- priority interactions between stressors and receptors. Such packages can leverage the competitive strengths of individual instruments and reduce risk in a rapid, cost-effective manner. One emerging example of environmental infrastructure to achieve these objectives, the Adaptable Monitoring Package, is presented and its capabilities described. The development and adoption of such packages requires close coordination between resource managers, technology developers, and researchers.

The Adaptable Monitoring Package (AMP), along with a remotely operated vehicle (ROV) and custom tool skid, is being developed to support near-field (≤ 10 meters) monitoring of hydrokinetic energy converters. The AMP is intended to support a wide range of environmental monitoring in harsh oceanographic conditions, at a cost in line with other aspects of technology demonstrations. This paper, which is the second in a two part series, covers the hydrodynamic analysis of the AMP and deployment ROV given the strong waves and currents that typify marine renewable energy sites. Hydrodynamic conditions from the Pacific Marine Energy Center's wave test sites (PMEC) and Admiralty Inlet, Puget Sound, Washington are considered as early adoption case studies. A methodology is presented to increase the AMP's capabilities by optimizing its drag profile through a combination of computational fluid dynamic (CFD) modeling and sub-scale experiments. Preliminary results suggest that AMP deployments should be possible in turbulent environments with a mean flow velocity up to 1 m/s.

Environmental monitoring of marine renewable energy projects is needed to reduce environmental uncertainties and en- able sustainable commercial implementations. An Adaptable Monitoring Package (AMP), along with the support infrastruc- ture required to perform maintenance of the AMP, is being de- veloped to enable real-time environmental monitoring of marine renewable energy converters. The monitoring capabilities sup- ported by the AMP include marine animal interactions with con- verters, noise levels, current and wave fields, and water qual- ity. The core instrumentation on the AMP is comprised of a hy- brid stereo-optical and acoustical camera system, a localizing hy- drophone array, acoustic Doppler current profilers and velocime- ters, a water quality sensor, cetacean click detector, and fish tag receiver. For an initial deployment to monitor a tidal turbine in deep water, the AMP is integrated into the converter structure and connected to shore via the turbine export cable, but can be dis- connected and recovered for maintenance independently of the turbine. The AMP is deployed by a SeaEye Falcon inspection- class ROV and a custom tool skid. This paper describes the func- tion, design, and dynamic stability of the AMP and deployment ROV. The conceptual design and approach to operations, to be confirmed through field testing, suggests that the AMP is likely to meet the need for high-bandwidth monitoring of marine re- newable energy converters at an acceptable cost.

Acoustic Doppler sensors used for flow measurements at energetic tidal sites present an inherent "Doppler noise" in the measured signal, varying with hardware configuration and flow conditions. At scales comparable to the sensors' sampling frequencies, the corresponding perturbations notably contaminate the signal, and cannot be corrected in the time series.

At such scales, dynamic phenomena are of particular interest in the process of increasing reliability and effectiveness of tidal turbines, and are mostly addressed in terms of statistics. In the case of inflow speed variations, the bias due to Doppler noise should be taken into account, and can be assessed via manufacturer specifications.

Here, a method is presented that enables a direct estimation of the Doppler noise strength from the measured signal itself. Inspired from polynomial least square regression, it is based on a spectral analysis of the measured signal respect to turbulence theory, under the hypothesis of a white Doppler noise contamination. The subsequent limitations are discussed and illustrated by practical cases.

The values found are generally higher than suggested by manufacturers, but still in the same order of magnitude. The use of the highest sampling frequency available is recommended.

The Adaptable Monitoring Package (AMP) along with a Remotely Operated Vehicle (ROV) and custom tool skid, is being developed to support near-field (󖼚 meters) and long-range monitoring of hydrokinetic energy converters. The goal for the AMP is to develop a system capable of supporting a wide range of environmental monitoring in harsh oceanographic conditions, at a cost in line with other aspects of technology demonstrations. This paper presents a system description of all related infrastructure for the AMP, including supported instrumentation, deployment ROV and tool skid, launch platform, and docking station. Design requirements are driven by the monitoring instrumentation and the strong waves and currents that typify marine renewable energy sites. Hydrodynamic conditions from the Pacific Marine Energy Centers wave test sites and Admiralty Inlet, Puget Sound, Washington are considered in the design as early adoption case studies. A methodology is presented to increase the capabilities to deploy and operate the AMP in strong currents by augmenting thrust and optimizing the system drag profile through computational fluid dynamic modeling. Preliminary results suggest that the AMP should be deployable in turbulent environments with mean flow velocities up to 1 m/s.

Station keeping, a vessel-based spatial surveying method for resolving details of the hydrokinetic resource, is presented in the context of the general methodology and also for the specific case of a survey conducted in northern Admiralty Inlet, Puget Sound, WA, in June 2011. The acoustic Doppler current profiler (ADCP) measurements collected during the June 2011 survey were part of a broader effort to characterize the resource at this location before tidal turbine installation. Autonomous bottom-lander (bottom-mounted) ADCP measurements are used to evaluate the accuracy with which data collected from this vessel-based survey reflect stationary measurements and also to analyze the potential for cycle-to-cycle variations in the conclusions drawn. Results indicate good agreement between shipboard and bottom-mounted observations in capturing spatial resource differences. Repeated surveys over several tidal cycles are required to obtain results consistent with long-term observations. Station-keeping surveys help to optimize bottom-mounted ADCP deployments that are then used to estimate turbine power generation potential and make final siting decisions.

Tidally driven currents and bed stresses can result in noise generated by moving sediments. At a site in Admiralty Inlet, Puget Sound, Washington State (USA), peak bed stresses exceed 20 Pa. Significant increases in noise levels are attributed to mobilized sediments at frequencies from 4–30 kHz with more modest increases noted from 1–4 kHz. Sediment-generated noise during strong currents masks background noise from other sources, including vessel traffic. Inversions of the acoustic spectra for equivalent grain sizes are consistent with qualitative data of the seabed composition. Bed stress calculations using log layer, Reynolds stress, and inertial dissipation techniques generally agree well and are used to estimate the shear stresses at which noise levels increase for different grain sizes. Regressions of the acoustic intensity versus near-bed hydrodynamic power demonstrate that noise levels are highly predictable above a critical threshold despite the scatter introduced by the localized nature of mobilization events.

One calendar year of Automatic Identification System (AIS) ship-traffic data was paired with hydrophone recordings to assess ambient noise in northern Admiralty Inlet, Puget Sound, WA (USA) and to quantify the contribution of vessel traffic. The study region included inland waters of the Salish Sea within a 20 km radius of the hydrophone deployment site. Spectra and hourly, daily, and monthly ambient noise statistics for unweighted broadband (0.02–30 kHz) and marine mammal, or M-weighted, sound pressure levels showed variability driven largely by vessel traffic. Over the calendar year, 1363 unique AIS transmitting vessels were recorded, with at least one AIS transmitting vessel present in the study area 90% of the time. A vessel noise budget was calculated for all vessels equipped with AIS transponders. Cargo ships were the largest contributor to the vessel noise budget, followed by tugs and passenger vessels. A simple model to predict received levels at the site based on an incoherent summation of noise from different vessels resulted in a cumulative probability density function of broadband sound pressure levels that shows good agreement with 85% of the temporal data.