# A Machine Learning Based Approach for Statistical Analysis of Detonation Cells from Soot Foils

arXiv: https://arxiv.org/abs/2409.06466 · HTML: https://mlsanity.com/paper/2409.06466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models](https://mlsanity.com/paper/2401.00544/) (2023)

## Built on by (most cited first, 3 of 3)

- [AutoHood3D: A Multi-Modal Benchmark for Automotive Hood Design and Fluid-Structure Interaction](https://mlsanity.com/paper/2511.05596/) (2025)
- [An Approximate Graph Elicits Detonation Lattice](https://mlsanity.com/paper/2603.16524/) (2026)
- [Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records](https://mlsanity.com/paper/2607.03764/) (2026)

## Abstract (arXiv)

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.