Researchers develop first hull-attached sensor system for predicting underwater radiated noise

Schematic diagram of the underwater noise prediction algorithm. Credit: Korea Institute of Machinery and Materials (KIMM)

Researchers in South Korea have developed the nation’s first hull-attached sensor-based system for predicting underwater radiated noise (URN), a key factor in naval stealth operations. The new technology allows real-time monitoring of underwater noise levels generated by naval vessels, enabling early detection of abnormal vibrations and improving operational efficiency while reducing maintenance costs.

The research team, led by Principal Researcher Seong-Hyun Lee at the Virtual Engineering Research Center of the Korea Institute of Machinery and Materials (KIMM), under the National Research Council of Science and Technology, successfully validated the system through full-scale trials on multiple naval ships.

The team developed a proprietary algorithm and sensor placement technology that accurately monitors and predicts both hull vibration and underwater noise using empirical data gathered during vessel operation.

Unlike conventional URN analysis methods, which require intermittent data collection using external equipment in quiet sea areas free from surrounding ship traffic, the new system enables continuous onboard monitoring with real-time updates.

KIMM develops first hull-attached sensor system for predicting underwater radiated noise
Principal Researcher Dr. Seong-Hyun Lee of KIMM’s Virtual Engineering Research Center. Credit: Korea Institute of Machinery and Materials (KIMM)

Even with a limited number of sensors, the optimized sensor placement and advanced signal processing techniques allow the system to maintain high prediction accuracy, achieving a margin of error within 4 decibels during real-world tests.

The algorithm processes real-time data from accelerometers attached to the vessel’s hull, analyzing vibration characteristics, radiation efficiency, and frequency profiles to calculate underwater radiated noise levels. In addition, a statistical anomaly detection algorithm allows early identification of abnormal vibration patterns, while adaptable threshold settings accommodate various operational conditions.

This real-time capability offers clear advantages for stealth operations, particularly in detecting changes caused by cavitation or shifts in propulsion modes.

KIMM develops first hull-attached sensor system for predicting underwater radiated noise
Principal Researcher Dr. Seong-Hyun Lee of KIMM’s Virtual Engineering Research Center inspects the test equipment for the underwater radiated noise prediction algorithm. Credit: Korea Institute of Machinery and Materials (KIMM)

Beyond its military applications, the system also offers significant benefits for maintenance management, with flexible integration into diverse ship designs and operations. The streamlined sensor configuration helps lower installation and operating costs while improving overall fleet management.

The research team further validated the technology by collecting vibration data from hull-mounted sensors during varying operational conditions, including speed changes. Comprehensive testing was conducted following international standards for underwater noise measurement (ISO 17208-1:2016), with real-world results showing prediction errors consistently within 4 dB.

“This world-class system enables real-time prediction and monitoring of strategically sensitive underwater noise data,” said Principal Researcher Seong-Hyun Lee of KIMM.

“It not only strengthens naval stealth capabilities but also enhances early fault detection and maintenance efficiency across diverse naval operations.”

The project was conducted jointly with defense contractor LIG Nex1 under the project titled “Hull-Attached Sensor-Based Monitoring Technology for Naval Propulsion Systems.”

Provided by
National Research Council of Science and Technology


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Researchers develop first hull-attached sensor system for predicting underwater radiated noise (2025, July 2)
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