# Analysis of Flame Evolution Generated from Methyl Laurate Droplet Using Deep Learning
## 4 Conclusions  
This study offers an analysis of the combustion dynamics of methyl laurate droplets, focusing on flame evolution, surface area, and volume. The findings highlight the effectiveness of integrating deep learning techniques, particularly YOLOv9, for object detection and segmentation of combustion processes. The insights gained contribute to advancing our understanding of biodiesel combustion, emphasizing its significance for sustainable energy solutions.  
* Flame visualization showed a progression from bluish flames near the droplet to red, yellow, and white hues further away, reflecting inherent combustion properties unaffected by external temperature variations.
* The application of YOLOv9 achieved high accuracy (mAP value of 0.974 for flame segmentation), underscoring its capability in analyzing combustion dynamics. This approach enables precise tracking of flame parameters, paving the way for enhanced combustion modeling and optimization.  
Figure 14: Flame volume and surface evolution during combustion of methyl laurate.  
* The integration of machine vision and machine learning techniques, particularly using YOLO-based algorithms, successfully reconstructed flame structures with high accuracy.
* Temporal variations in flame structure were correlated with transient instabilities, shedding light on complex interactions between fuel vaporization, heat transfer, and combustion intensity.  
These findings provide valuable insights into the combustion characteristics of methyl laurate droplets, demonstrating the efficacy of deep learning (YOLOv9) in automating flame segmentation and quantifying dynamic combustion parameters. By integrating traditional geometric principles with advanced machine learning algorithms, this study establishes a scalable framework for high-precision combustion diagnostics, applicable to diverse biofuel systems. To advance these findings, future work will extend this AI-driven methodology to comparative studies of methyl laurate against conventional diesel and biodiesel blends under engine-relevant pressures while correlating droplet-scale phenomena with engine performance metrics (e.g., NOx emissions, thermal efficiency). Such efforts will not only validate methyl laurate's industrial viability but also showcase the transformative role of AI in accelerating sustainable energy research.  
Conceptualization, F.A.A.; methodology, F.A.A.; formal analysis, F.A.A., C.-C.C.; software, F.A.A.; validation, F.A.A., C.-C.C.; investigation, F.A.A., C.-C.C.; data curation, F.A.A.; writing--original draft, F.A.A., C.-C.C.; writing--review and editing, F.A.A., C.-C.C.; visualization, F.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available from the corresponding author upon request. The data are not publicly available due to privacy.  
The authors would like to express their sincere gratitude to [PERSON], a faculty member at Universitas Brawijaya, Indonesia, for her invaluable technical support and guidance, which greatly contributed to the success of this research. Her expertise and assistance were instrumental in navigating the technical challenges encountered throughout this study.  
The authors declare no conflicts of interest.