用机器学习自动分析爆轰细胞结构，精度超90%。
A Machine Learning Based Approach for Statistical Analysis of Detonation Cells from Soot Foils
- 无需训练数据，直接提取爆轰细胞边界
- 误差控制在10%以内，可测细胞面积与跨度
- 适合研究不同复杂度的爆轰波结构
本研究提出一种基于机器学习的新算法，用于从炭黑箔片图像中精确分割和测量爆轰细胞，解决了传统人工和基础边缘检测方法的局限性。该算法借鉴细胞生物学分割模型，无需训练过程或数据集，克服了爆轰研究中的关键挑战。通过模拟实验与数值研究的测试案例验证，结果表明算法在复杂情况下仍保持稳定精度，误差始终低于10%。该方法能有效提取细胞面积、跨度等关键参数，揭示了从均匀到高度不规则细胞结构的演化趋势。尽管模型表现出强鲁棒性，但在极高复杂度或不规则细胞模式下仍存分析困难。本工作展示了该算法在推动爆轰波动力学理解方面的广泛适用性与潜力。
原文摘要 · Abstract (English)
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.
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