# Estimation of the Noise Source Level of a Commercial Ship Using On-Board Pressure Sensors
## 4 Conclusions  
A simple beamforming method for propeller noise monitoring was validated in a model-scale measurement. The method assumed a source grid with virtual monopole sources. An estimated source level was calculated at each source point using noise data measured from the hydrophone array. At each frequency, the beamforming power was calculated for the source grid and the source strength was determined where the beamforming power was at its maximum. For the validation of the method, a commercial ship at model scale was tested in the Large Cavitation Tunnel at the KRISO. In the model-scale measurement, the radiated noise was measured using a hydrophone located in an acoustic window at the bottom. An ITC-1032 transducer was used to measure the transfer function between the source and the receiver. Using the data sheet of the transmitting voltage  
Figure 24: Comparison of source levels estimated for other parameters: (a) frequency resolution and (b) number of sensors.  
response, the measured source level was obtained. Good agreement was found between the measured and calculated source levels at frequencies from 2 to 50 kHz.  
Full-scale underwater radiated noise and hull pressure measurements were carried out for the application of the estimation method at full scale. The underwater radiated noise was measured using a buoy-type hydrophone array, and the measured result was converted into the source level based on the ISO 17208-1 procedure. For the source-level estimation using on-board sensors, a set of flush-mounted pressure transducers were installed in the hull above the propeller. Comparisons were made between the measured and estimated source levels in one-third octave bands for different operating conditions, showing good agreement above 400 Hz. From the sensitivity analysis, it was revealed that the recording time had little effect on the result at high frequencies, whereas changing the frequency resolution and the number of sensors showed deviations at low frequencies. Nonetheless, the results were consistent in most of the frequency bands.  
As the method was only applied to a single ship and the operating conditions were similar, it may need other cases to ensure its robustness and accuracy. In addition, optimization of the source region, number of sensors, and data processing may be studied further.  
Conceptualization, H.J. and H.S.; methodology, J.-H.L. and Y.-H.K.; full-scale measurement, H.S.; model-scale measurement, H J.; data processing and analysis, H.J.; original draft preparation, H.J.; Supervision, H.S.; project administration, H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Research Institute of Ships and Ocean Engineering (KRISO) under grant number \"PES3890\" and the Ministry of Trade, Industry, and Energy (MOTIE) under grant number \"20012974\".  
Not applicable.  
We would like to thank [PERSON] for providing the URN data measured at full scale.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BPF & Blade-Passing Frequency \\ IMO & International Maritime Organization \\ ITTC & International Towing Tank Conference \\ KIOST & Korea Institute of Ocean Science and Technology \\ KRISO & Korea Research Institute of Ships and Ocean Engineering \\ LCT & Large Cavitation Tunnel \\ MVDR & Minimum-Variation Distortionless Response \\ PSD & Power Spectral Density \\ SL & Source Level \\ SPL & Sound Pressure Level \\ TVR & Transmitting Voltage Response \\ URN & Underwater Radiated Noise \\ \end{tabular}