Onboard noise monitoring, using sensors placed directly on the vessel, offers advantages like real-time data collection during normal operations.
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An alternative approach to acoustic ranges involves measuring noise using pressure sensors placed directly onboard the vessel. While onboard monitoring doesn’t allow comparison of measurements between different vessels without further analysis, it does offer several advantages. These include access to noise level measurements without the need to repeatedly visit an acoustic range and real-time and ongoing noise monitoring during normal vessel operations.
Monitoring Propellers
With propellers – particularly propeller cavitation – being a significant source of underwater vessel noise, several QVI projects focused on detecting cavitation in real time.
Continuous Logging of Underwater Noise Emissions (CLUE)
One such tool used pressure sensors and the characterization of radiated noise from the propulsion system was developed through a joint initiative between DNV and ABB. The project CLUE was tested on a cruise ship from Royal Caribbean Cruise Lines equipped with three azimuth propulsor/thruster systems. These Azipod® systems have an electric drive motor in a pod placed on the outside of the vessel. Able to rotate 360 degrees, Azipod® systems give vessels a great deal of manoeuvrability. Pressure sensors were fitted through the hull above the propellers to capture cavitation noise. Noise measurements from external hydrophones were also collected to calibrate the measurements and confirm that the sensors accurately estimated underwater radiated noise.
Feasibility of Real-Time Shipboard Cavitation Monitoring and Management
Another JASCO Applied Sciences’ project focused on predicting the speeds at which propeller cavitation occurs for a particular vessel. In theory, if this speed can be identified, vessel operators can slow down and avoid cavitation in critical marine habitats. Working aboard a Canada Steamship Lines’ bulk carrier, researchers used measurements from onboard pressure sensors placed inside and outside the hull near the propeller, as well as hydrophone measurements, to develop a cavitation detection algorithm . While the system successfully detected when cavitation was occurring, the system was not able to predict at what speed cavitation would occur. The project also highlighted another issue. For this vessel, cavitation started at speeds of just six knots. From a safety perspective, consistently sailing at this low speed is impractical for effective manoeuvring. Safe navigation is one of many complex factors that need to be considered when developing a vessel noise management plan. Each vessel’s unique design, operational characteristics, and environment influence how noise is generated and transmitted. In this context, feasible and specific operational changes can offer valuable solutions, and these are explored in more detail in the third article in this series: Quieter operation for ships: large and small vessels.
Hydrodynamic Propeller Noise Monitoring System (HyPNoS)
Working with BC Ferries, Schottel’s HyPNoS project developed a prototype monitoring system that focused on hull vibrations above the propeller. Hull vibrations above the propulsion system can capture operational, environmental, and wear-related noise variations from the propeller. Capturing this type of noise is important because the vessel’s design – placement of the rudder and shape of the hull, for example – influences noise from the propulsion system. Operational conditions, such as vessel speed, rudder angle, shallow water operations, and level of biofouling, can also change the noise from the propulsion system.
Schottel used the measurements taken by a permanently installed real-time onboard system of sensors to develop and train an AI-ready algorithm to estimate underwater radiated noise. The measurements from the sensors were compared to hydrophone data to correlate the onboard vibrations with externally measured underwater radiated noise. Once the algorithm had sufficient data (measuring the noise levels from two propeller designs – original propeller and a quieter design – at different speeds on one of the ferries), the algorithm was able to estimate underwater noise based on vibrations – no need for hydrophones to quantify underwater noise. The team aims to improve the algorithm by incorporating other measurements, such as the vessel location and direction, the propeller pitch setting, water depth, and vessel speed.
Report on Relationships Between Noise, Vibration, and URN Measurements
AllSalt Maritime also worked with BC Ferries to develop a prototype machine-learning-based monitoring system. This project, Report on Relationships Between Noise, Vibration, and URN Measurements, had two phases. The first involved taking measurements from multiple locations, such as machinery spaces and steering gear compartments, to track down the dominant noise source. The second phase homed in on that dominant noise source – in this case, the main engine machinery space. Noise measurements collected from the onboard sensors and external hydrophones calibrated the machine-learning model used to estimate underwater radiated noise levels. The project also hoped to measure the relationship between underwater radiated noise and the operational efficiency of the vessel, but the data were not suitable for this purpose.
Mitigation of Radiated Noise of Small Marine Craft using Condition-Based Monitoring
While the previously mentioned projects assessing onboard noise monitoring for large vessels, Lloyd’s Register’s project looked at small fishing vessels – specifically “Cape Islanders,” a common vessel design on Canada’s East Coast, often used by lobster fishers. While these vessels are mechanically and structurally simple, space on board is limited to install monitoring systems. This project created a small prototype system using Raspberry PI, an accelerometer placed on the hull above the propeller, and a tachometer to measure the propeller’s revolutions per minute. Combining these sensor measurements with the season, the year the vessel was built, and the engine’s horsepower, the system could successfully identify when a vessel was cavitating and estimate the level of underwater radiated noise it emitted based on its operational conditions at that moment in time.
This article is part of a five articles series on technology for detecting and analysing underwater vessel noise.
Continue learning about the new discoveries and challenges in making vessels quieter with the other topics in this series here
The Quiet Vessel Initiative is a federally funded program through Transport Canada. Industry partners and researchers interested in potential research and development collaborations to advance innovative solutions in marine technology are invited to contact the Quiet Vessel Initiative team at Marine-RDD-maritime@tc.gc.ca.
Cavitation: is a change in phase from liquid to vapour, like boiling, but caused by a change in pressure rather than a change in temperature. When areas of sufficiently low pressure are generated in water, vapour bubbles form. As the vapour bubbles leave the area of low pressure, they collapse (implode). Because the pressure differences are usually large, the collapse of cavitation bubbles is very powerful and loud. Other vessel factors, such as the hull design, also influence cavitation. Measurements have shown that propeller cavitation is more common at higher speeds due to greater loads on propeller blades. Cavitation can also occur when propeller blades are misaligned or damaged. In addition to creating a great deal of sound, cavitation bubbles can damage or degrade metallic surfaces like propellers, reducing their performance and efficiency.
Underwater noise: sound generated below water by human activity in the ocean environment. Various industries contribute to underwater noise—offshore energy, construction, military operations, and of course vessel traffic. The noise generated by vessels is referred to as underwater radiated noise.
Azimuth propulsion system or Azipod®: a configuration of marine propellers placed in pods that can be rotated to any horizontal angle (azimuth), making a rudder redundant. These give ships better maneuverability than a fixed propeller and rudder system. An Azipod® is essentially an azimuth thruster where the electric drive motor is contained inside the pod itself, beneath the water surface.
Hydrophone: an underwater microphone that can be deployed individually or in groups. Groups of hydrophones can be arranged either horizontally on the seafloor or vertically at different depths in the water column. Hydrophones detect pressure changes in the water caused by sound waves. These sensors convert the underwater pressure fluctuations into electrical signals, which can then be analyzed to determine the properties of sound, such as volume and frequency.
Propeller pitch: the distance a propeller will travel through the water in one revolution. An increase in pitch allows the propeller to grip a larger amount of water (more distance travelled), a decrease in pitch reduces grip in the water (less distance travelled).
Machine-learning model: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Accelerometer: a device that measures the vibration, or acceleration of motion, of a structure. The force caused by vibration or a change in motion (acceleration) causes the mass to “squeeze” the piezoelectric material which produces an electrical charge that is proportional to the force exerted upon it. Since the charge is proportional to the force, and the mass is constant, then the charge is also proportional to the acceleration. These sensors are used in a variety of ways – from space stations to handheld devices like smartphones.
Tachometer: an instrument measuring the rotation speed of a shaft or disk, as in a motor or other machine. The device usually displays the revolutions per minute (RPM).
Algorithm: a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer. Algorithms act as an exact list of instructions that conduct specified actions step by step in either hardware- or software-based routines.
