# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 3 Underwater Radiation Noise Prediction Method for Cabin Structures
### Simulation Calculation and Analysis
#### 3.3.2 Performance Analysis of Far-Field Radiation Noise Prediction outside the Cabin  
The coordinates of the observation point are \((100,0,L/2)\). As shown in Figure 10, the prediction error of radiation noise gradually increases with the increase in the frequency, and the average prediction error of far-field radiation noise in the one third octave band in the 1 kHz band is less than 4 dB. Compared with vibration monitoring, synchronous monitoring can significantly improve the prediction accuracy of underwater radiation noise, especially in the low-frequency band below 500 Hz. In the high-frequency band above 500 Hz, the sound excitation causes lower radiated sound power, and the underwater radiated noise is mainly caused by the force source excitation, so the prediction performance of the synchronous monitoring is essentially consistent with that of the vibration monitoring. It can be seen from the simulation results that the prediction accuracy of underwater radiation noise of the cabin structure under hybrid excitation can be effectively improved by adding acoustic monitoring points. 3.3 Comparative Analysis of Underwater Radiation Noise Prediction Performance under Different Monitoring Schemes  
Three monitoring schemes were set in the simulation, as shown in Table 1. The prediction results of underwater radiated noise are shown in Figure 11.  
It can be seen that the prediction error of Scheme 1 is greater than that of Scheme 2 and Scheme 3, indicating that the monitoring information of vibration and sound inside the cabin of Scheme 1 is missing or incomplete. However, the noise prediction performance of Scheme 3 is not significantly improved compared with Scheme 2, indicating that the vibration and acoustic monitoring information of Scheme 2 and Scheme 3 is essentially complete, and that continuing to add vibration and sound monitoring points has little significance for improving the prediction accuracy of underwater noise, which is essentially consistent with the research conclusions in Section 2.2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vibration Monitoring Point**} & \multicolumn{2}{c}{**Acoustic Monitoring Point**} \\ \cline{2-5}
**Monitoring** & \multicolumn{2}{c}{**Circumferential**} & \multicolumn{2}{c}{**Axial Number**} & \multicolumn{2}{c}{**Circumferential**} & \multicolumn{1}{c}{**Axial Number**} \\ \cline{2-5}
**Scheme No.** & \multicolumn{2}{c}{**Circumferential**} & \multicolumn{1}{c}{**Axial Number**} & \multicolumn{1}{c}{**Circumferential**} & \multicolumn{1}{c}{**Axial Number**} \\ \hline Scheme 1 & 9 & 5 & 5 & 3 \\ Scheme 2 & 15 & 6 & 9 & 5 \\ Scheme 3 & 20 & 8 & 15 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Monitoring schemes. Figure 10: Prediction results of far-field radiated noise outside the cabin: (**a**) Far-field radiation noise spectrum. (**b**) Far-field radiation noise one third octave in-band mean error. 3.4 Analysis of the Influence of External Sound Response Points on Underwater Radiation Noise Prediction Performance  
Three sound response point schemes were set, namely, 9 \(\times\) 5, 15 \(\times\) 9, and 24 \(\times\) 11. The calculation results are shown in Figure 12. When the number of sound response points on the external surface of the cabin is increased, the prediction accuracy of underwater radiated noise is significantly improved. However, when the number of sound response points on the holographic surface is increased from 135 to 264, the prediction accuracy of underwater radiated noise is limited, indicating that the monitoring information of Scheme 3 is essentially complete, which is generally consistent with the research conclusions in Section 2.2. 3.5 Analysis of the Influence of Underwater Radiation Noise Prediction Performance under Different SNR Conditions  
The SNR is defined by  
\[SNR=20\lg(\frac{E_{S}}{E_{N}}) \tag{22}\]  
where \(E_{S}\) is the monitor signal strength and \(E_{N}\) is the introduced noise intensity.