# Sonic Crystal Noise Barrier with Resonant Cavities for Train Brake Noise Mitigation
## 4 Conclusions  
This work introduces the issue of noise pollution, particularly that posed by TBN in the 2 to 4.5 kHz range. The most annoying frequencies of TBN are mitigated by the introduction of SCNBs, which strategically attenuate noise within certain frequency ranges, offering a promising solution for this type of noise. More specifically, large differences were observed in numerical simulations (20 dB) and in the experimental results (25 dB) between the 0\({}^{\circ}\) SCNB and the 90\({}^{\circ}\) SCNB in the TBN frequency range.  
The present research explores the integration of LRs and their interaction with Bragg scattering in resonant SCNBs, emphasizing the crucial role of resonator orientation. Advanced numerical simulations, employing FEM, assess the acoustic efficacy of 2D SCNB models, while experimental validation using 3D-printed prototypes confirms their competitive noise reduction levels compared to conventional barriers.  
The appropriate configuration of the SCNBs, particularly the orientation of the resonant cavity, proves to be crucial for effective noise reduction, especially when the local resonators are oriented at 0\({}^{\circ}\). Our study designed for the specific frequency range of TBN, from 2 kHz to 4.5 kHz, focusing on this spectrum allows for in-depth analysis and targeted solutions for TBN mitigation. Additionally, we highlight the potential for scalability of our findings, with a note that large-scale applications should consider the role of viscothermal losses. This opens avenues for future research, allowing for the exploration of our model's adaptability and its impact across various scales and contexts. The study opens up new avenues for future investigations in noise reduction and SCNBs. Potential future research areas include exploring scalability to diverse transportation environments, optimizing SCNB materials and resonator designs, integrating smart technologies for active noise mitigation and assessing long-term durability and maintenance requirements.  
Conceptualization, D.R.-S., J.G.-N., J.R. and R.P.; methodology, J.G.-N., D.R.-S., V.S., A.V.G., N.P., J.R. and R.P.; software, D.R.-S., V.S., A.V.G., N.P. and R.P.; validation, J.R. and R.P.; formal analysis, J.G.-N., D.R.-S., J.R. and R.P.; investigation, J.G.-N., V.S., A.V.G., N.P., J.R. and R.P.; resources, V.S., A.V.G., N.P., J.R. and R.P.; data curation, J.G.-N. and D.R.-S.; writing--original draft preparation, D.R.-S. and J.G.-[PERSON]; writing--review and editing, [PERSON]-N., [PERSON], V.S., A.V.G., N.P., J.R. and R.P.; visualization, [PERSON]-N. and [PERSON]-[PERSON]; supervision, J.R. and R.P; project administration, V.S., A.V.G., N.P., J.R. and R.P.; funding acquisition, V.S., A.V.G., N.P. and R.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spanish Ministry of Economy and Innovation (MINECO) and the European Union FEDER (project PID2019-109175 GB-C22).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **SPL without Barrier** & **SPL SCNB 0\({}^{\circ}\)** & **SPL SCNB 90\({}^{\circ}\)** \\  & **(dBA)** & **(dBA)** & **(dBA)** \\ \hline [PERSON] et al. [6] (1) & 71.3 & 63.4 & 63.6 \\ [PERSON] et al. [6] (2) & 77.9 & 70.7 & 71.0 \\ [PERSON] et al. [6] (3) & 78.2 & 68.9 & 69.7 \\ [PERSON] et al. [7] (1) & 80.1 & 66.2 & 69.4 \\ [PERSON] et al. [7] (2) & 79.5 & 75.4 & 75.3 \\ [PERSON] et al. [7] (3) & 68.8 & 55.2 & 58.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the A-weighted SPL in dBA for each SCNB arrangement at 0\({}^{\circ}\) and 90\({}^{\circ}\) when added to real cases of TBN measurements.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors acknowledge FabLab Bitonto and [PERSON].  
Conflicts of Interest: The authors declare no conflicts of interest.