A Machine Learning Based Approach for Statistical Analysis of Detonation Cells from Soot Foils
This addresses the limitations of manual and primitive methods in detonation research, offering a tool for improved analysis of detonation wave dynamics, though it is incremental as it adapts existing segmentation models to a new domain.
The study tackled the problem of accurately segmenting and measuring detonation cells from soot foil images by developing a novel machine learning algorithm, which achieved consistent accuracy with errors within 10% in test cases.
This study presents a novel algorithm based on machine learning (ML) for the precise segmentation and measurement of detonation cells from soot foil images, addressing the limitations of manual and primitive edge detection methods prevalent in the field. Using advances in cellular biology segmentation models, the proposed algorithm is designed to accurately extract cellular patterns without a training procedure or dataset, which is a significant challenge in detonation research. The algorithm's performance was validated using a series of test cases that mimic experimental and numerical detonation studies. The results demonstrated consistent accuracy, with errors remaining within 10%, even in complex cases. The algorithm effectively captured key cell metrics such as cell area and span, revealing trends across different soot foil samples with uniform to highly irregular cellular structures. Although the model proved robust, challenges remain in segmenting and analyzing highly complex or irregular cellular patterns. This work highlights the broad applicability and potential of the algorithm to advance the understanding of detonation wave dynamics.