用图论方法自动识别爆炸波中的细胞结构，精度达98%。
An Approximate Graph Elicits Detonation Lattice
- 基于图论构建无训练分割算法，自动提取三维压力信号中的爆炸细胞
- 在合成数据上误差仅2%，真实模拟中细胞轴向偏差17%
- 适用于复杂几何形态，为爆炸碰撞研究提供新工具
本研究提出一种基于图论的新型算法，用于从三维压力信号中精确分割与测量爆炸细胞（即爆炸晶格），克服了传统人工及二维边缘检测方法的局限。该无需训练的分割算法首次在两个合成数据集上验证，误差仅为2%。利用三维模拟数据评估图基工作流性能，统计结果与联合概率密度分析显示，细胞呈沿波传播轴方向的长条形，轴向偏差17%；体积分布更大范围的离散性反映出线性变异的立方放大效应。尽管框架具备鲁棒性，但对高度复杂的细胞模式仍存在可靠分割与量化挑战。然而，该图基方法能泛化至多种细胞几何形态，成为爆炸分析的实用工具，并为三重点碰撞研究提供坚实基础。
原文摘要 · Abstract (English)
This study presents a novel algorithm based on graph theory for the precise segmentation and measurement of detonation cells from 3D pressure traces, termed detonation lattices, addressing the limitations of manual and primitive 2D edge detection methods prevalent in the field. Using a segmentation model, the proposed training-free algorithm is designed to accurately extract cellular patterns, a longstanding challenge in detonations research. First, the efficacy of segmentation phase on two synthetic datasets is evaluated with an error of 2%. Next, 3D simulation data is used to establish performance of the graph-based workflow. The results of statistics and joint probability densities show oblong cells aligned with the wave propagation axis with 17% deviation, whereas larger dispersion in volume reflects cubic amplification of linear variability. Although the framework is robust, it remains challenging to reliably segment and quantify highly complex cellular patterns. However, the graph-based formulation generalizes across diverse cellular geometries, positioning it as a practical tool for detonation analysis and a strong foundation for future extensions in triple-point collision studies.
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