## 4 Results and Discussions
### Schlieren Imaging Analysis  
In the following section, the processing of images obtained using the Schlieren technique is addressed. A significant challenge with this visualization technique is primarily because Schlieren flow visualization typically provides only an external or integrated view of the flow with density gradients, being challenging to extract relevant information of velocity or turbulence data directly. Moreover, the resulting images are sensitive to setup alignment and become complex to interpret without advanced image processing. Despite the limitations of the Schlieren technique, the authors previously disseminated some interesting findings in [26], along with preliminary conclusions on the impact of chevrons on the flow pattern relative to the baseline configuration, solely using image processing with the Phantom VEO710 camera software (PCC 3.9.40). To explore whether the Schlieren approach can provide additional insights into flow dynamics, a code for image processing was developed. This method offers a new perspective by creating color contours of equal-density gradients across grayscale subdomains (range: [0, 255]), enabling \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **SPL(Z)** & **SPL [dB]** & **SPL [dB]** & **SPL(dB]** & **SPL [dB]** \\  & **[dB]** & **Spectrogram** & **STFT from** & **Welch** & **In-House** \\  & **Power** & **Spectrogram** & **Periodogram** & **In-House** \\ \hline B & 118.42 & 118.42 & 118.67 & 118.42 & 118.42 \\ C & 116.89 & 116.88 & 117.14 & 116.88 & 116.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SPL comparison in time and frequency domains for baseline and chevron configurations. Figure 11: Acoustics metrics for chevrons (**a**) \(L_{p}(t)\), (**b**) \(L_{\text{Aeq},T_{\sigma}}\), \(L_{\text{Zeq},T_{\sigma}}\), \(L_{\text{Ceq},T_{\sigma}}\), (**c**) power spectrum from periodogram, (**d**) spectrum SPL(\(f\)), (**e**) spectrum SPL (detail) and (**f**) one-sided spectrum. a more detailed analysis of density variations. While Schlieren imaging primarily highlights density gradients, this enhanced processing allows us to infer flow characteristics indirectly. The development of this method included several steps, specifically designed for this study, to enhance the extraction of flow characteristics:  
Images were normalized to enhance contrast across the density gradient spectrum, allowing for a more precise delineation of flow structures;  
Grayscale intensity values were segmented into multiple iso-levels, each corresponding to specific density gradients, enabling detailed visualization of subtle flow features;  
An apropiate color scheme was applied to the iso-levels, providing visual clarity and distinguishing fine-scale turbulence from larger coherent structures;  
Contours were superimposed on the grayscale images to highlight regions of interest, such as shear layers and potential flow areas, automatically generating consistent visualizations for all configurations. The processing workflow allowed the extraction and comparison of multiple consecutive frames, identifying features such as the formation, interaction and dissipation of vortices, even when subtle differences were not visible in raw images. The novelty lies in the tailored combination of normalization, segmentation and visualization techniques, which provided enhanced insights into turbulence features, such as vortex formation, interaction and dissipation. These tools proved particularly valuable for comparing configurations like chevrons and ejectors, which exhibit subtle but critical differences in flow behavior. The aim of the current work is to improve the recognition of flow features, such as counter-rotating streamwise vortices and coherent structures, while identifying factors contributing to noise reduction observed in the acoustic analysis. This study compares the flow characteristics of two different nozzle configurations, including baseline and chevron nozzles across three operating regimes.