An approximate graph elicits detonation lattice

TL;DR

This paper introduces a graph theory-based algorithm for accurate, training-free segmentation and measurement of detonation cells from 3D pressure data, improving analysis of cellular patterns in detonations.

Contribution

The study presents a novel, robust graph-based method for 3D detonation lattice segmentation that outperforms traditional 2D techniques and generalizes across diverse cellular geometries.

Findings

Prediction error of 2% on generated data

Cells are aligned with wave propagation axis with 17% deviation

Framework handles diverse cellular geometries

Abstract

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 on generated data is shown with a prediction error 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.