LOWNOISER Webinar: TSI shares its progress in monitoring and predicting noise radiated into water (URN).

TSI took part in the webinar “Analytical Modelling Approaches for Underwater Noise Reduction in Shipping”, organised by the LOWNOISER team and coordinated by Emilie Dorgeville from Maritime CleanTech. During the session, our colleague Roque Andrés, R&D engineer and acoustics expert at TSI, shared the progress of our work on improving the assessment and prediction of Underwater Radiated Noise (URN) from ships by combining analytical models, onboard monitoring and operational data.

Underwater Radiated Noise from shipping is receiving increasing attention across the maritime sector. Understanding how this noise is generated, how it is transmitted through the vessel structure and how it changes under different operating conditions is essential for developing effective measures to reduce it.

At TSI, our experience in noise and vibration, underwater acoustics and monitoring systems allows us to address this challenge from different perspectives. Part of this research is currently being carried out within the European LOWNOISER project.

Using onboard monitoring to improve Underwater Radiated Noise (URN) prediction.

One of the questions we are working on is how to obtain a more accurate understanding of the noise radiated into the water by a vessel under different operating conditions, and how to assess the effect of measures implemented to reduce it. Our approach combines analytical models with data from onboard monitoring systems and vessel operational parameters.

Sensors installed at different locations provide information on machinery and structural vibration, while other measurements associated with the propulsion system allow us to analyse phenomena such as cavitation. This information helps us understand what is happening onboard during vessel operation and study how it relates to the noise ultimately radiated into the water.

Connecting vibration, cavitation and Underwater Radiated Noise.

One of the challenges in assessing Underwater Radiated Noise is establishing the relationship between the different noise and vibration sources onboard and the vessel’s underwater acoustic signature. Vibration generated by engines, auxiliary equipment and propulsion systems can be transmitted through the vessel structure and subsequently radiated into the water. Propeller noise, and particularly cavitation, can also make an important contribution to the overall underwater noise signature.

Acoustic behaviour is not constant either. Vessel speed, engine load, propulsion operating conditions and other operational parameters can all influence URN levels. Combining onboard monitoring, operational data and prediction models therefore allows us to study a vessel’s acoustic behaviour beyond a single measurement condition.

What can onboard URN monitoring provide?

Underwater noise measurement campaigns provide essential information about a vessel’s acoustic signature under specific conditions. However, throughout its operational life, a ship operates at different speeds, loads and propulsion regimes.

Onboard monitoring provides complementary information that can help us understand how this acoustic behaviour changes during operation.

Our research aims to further improve the correlation between vibration, cavitation, operational parameters and Underwater Radiated Noise, progressively improving the ability of prediction models to represent the vessel’s actual behaviour.

In the medium and long term, this information can support shipowners, operators, shipyards and engineering companies in assessing the acoustic performance of vessels and studying the effect of different mitigation measures and operating conditions.

LOWNOISER: different approaches to reducing underwater noise from ships.

TSI’s work is part of LOWNOISER, a European project bringing together companies, research centres, and maritime organisations to develop and demonstrate solutions for understanding and reducing underwater noise from shipping. An important part of the project is moving from prediction and modelling to the demonstration and validation of solutions onboard real vessels.

During the webinar, several LOWNOISER demonstrators were presented, approaching the URN challenge from different perspectives.

Andre Böhme from Kongsberg Maritime presented work on optimising the microgeometry of the azimuth thruster bevel gears onboard Havila Capella, with the aim of reducing excitation forces that can contribute to noise generation.

Luca Savio from SINTEF Ocean discussed research into Air Lubrication Systems (ALS) and their interaction with underwater sound propagation.

Jørgen Løtvedt from Bergen Engines presented work carried out onboard Fridtjof Nansen to investigate how engine mounting and isolation systems influence the transmission of vibration into the vessel structure.

These areas of research are complemented by the work presented by Roque Andrés from TSI, focusing on the use of onboard monitoring and operational data to improve the assessment and prediction of Underwater Radiated Noise. The session was coordinated by Emilie Dorgeville from Maritime CleanTech, who also provided an overview of the project and its next steps.

The next challenge: validating URN predictions at full scale.

One of the next steps will be the continued validation of the models through full-scale measurements.

Comparing predictions with the vessel’s actual acoustic behaviour will allow us to assess model accuracy, identify areas where further refinement is needed and understand how different solutions perform under real operating conditions.

At TSI, we will continue working on the relationship between onboard measurements and underwater acoustic measurements, as well as the influence of different operating conditions on URN.

The objective is to continue developing tools that can not only predict Underwater Radiated Noise, but also provide a better understanding of the main contributing sources, how the vessel’s acoustic signature changes during operation and the actual effect of measures implemented to reduce it.

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