Project: University of Washington DAISY
MRE devices create sound that we need to quantify, but it is challenging to distinguish background noise from device noise. For example, it can be difficult to distinguish between radiated noise from the blades and the natural sound produced by movement of gravel on the seabed around a tidal turbine. To separate the background from the sound produced by the device, the University of Washington developed the Drifting Acoustic Instrumentation SYstem (DAISY).
The lightweight, drifting spar buoys have a GPS, a marker light, an inertial measurement unit, a meteorological station, and data-collection electronics. Underneath the floating portion is the listening device, or hydrophone, connected by one of several attachment methods aimed to further improve the accuracy of characterizing the sound of a marine energy device. There are DAISYs for measuring in both current (C-DAISY) and wave (W-DAISY) environments.
In multiple tests, three C-DAISYs were deployed in the Sequim Bay channel simultaneously and were set to drift alongside a boat playing back a known sound source. Using information from multiple DAISYs, researchers can triangulate the source of a sound and determine whether it originated from an MRE device or from another activity. In other tests, observations of the hydrodynamics of the DAISY have been made to assess how effective flow shields—protective, acoustically transparent covers—are at eliminating pseudo-noise that is associated with neither an MRE device or the natural environment, but can be mistaken for real low-frequency noise..
UW researchers have successfully reduced noise associated with waves contacting the floating portion of the W-DAISY as well as water flow around the hydrophone, and are now working to deploy groups of W-DAISYs around wave energy converters. Additionally, PNNL and UW have developed an app that allows the DAISY units to be tracked in real time during deployments on a ruggedized tablet, making the system easier to use in wavy or low-visibility conditions.
DAISYs are one type of several drifting hydrophone technologies available for MRE underwater noise monitoring. PNNL supported a comparison test of three different acoustic monitoring devices for MRE, including UW’s DAISY, Integral Consulting’s NoiseSpotter, and Oregon State University’s drifting hydrophone, in calm, tidal current, and wave resource locations. Testing with other research groups benchmarks instrument performance and strengthens technology performance.
Project: Integral NoiseSpotter
An obstacle to monitoring acoustic sounds in marine environments is locating the source of sound propagating through the water. Integral Consulting, Inc. is tackling this challenge with the NoiseSpotter project, a sensor system that classifies and provides accurate location information about sounds related to marine energy installations.
In addition to using typical hydrophones that measure sound pressure and strength, the NoiseSpotter measures particle velocity, which provides information to discern the bearing of a source of sound. This is accomplished with an acoustic vector sensor array that triangulates particle velocity vectors to determine the location and identity of a sound.
Researchers performed multiple field tests of the vector sensor array between 2017 and 2019. Field testing was performed in a quiet part of Sequim Bay and in a more energetic environment in the Sequim Bay channel. PNNL and the Triton program were instrumental to NoiseSpotter hardware and software development, enabling the development of the location estimation algorithm, flow noise mitigation system, electronics, pressure housing, vector sensor mounts, deployment platform, and deployment methodologies. Since these deployments, there have been improvements to estimating localized underwater sound using particle velocity sensors, as well as improved water pressure housing and onboard electronics. Triton support informed the device’s final modular design, which is easy to assemble dockside. Additionally, researchers developed a location estimation algorithm on board the NoiseSpotter for real-time processing of the vector data that can be transferred to a surface buoy and accessed by end users around the world via the cloud.