Machine learning enhanced multi-particle tracking in solid fuel combustion

TL;DR

This study enhances particle detection and tracking in high-density solid fuel combustion using machine learning models, demonstrating improved velocity measurements and insights into particle behavior under varying densities.

Contribution

It introduces ML-based detection methods like YOLO and RT-DETR for particle tracking in combustion, outperforming traditional techniques and revealing PND's impact on particle dynamics.

Findings

ML models trained on low-PND data predict high-PND particle velocities.

SAHI algorithm improves detection model performance.

Particle velocity decreases with increasing PND due to interactions.

Abstract

Particle velocimetry is essential in solid fuel combustion studies, however, the accurate detection and tracking of particles in high Particle Number Density (PND) combustion scenario remain challenging. The current study advances the machine-learning approaches for precise velocity measurements of solid particles. For this, laser imaging experiments were performed for high-volatile bituminous coal particles burning in a laminar flow reactor. Particle positions were imaged using time-resolved Mie scattering. Various detection methods, including conventional blob detection and Machine Learning (ML) based You Only Look Once (YOLO) and Realtime Detection Transformer (RT-DETR) were employed and bench marked.~Particle tracking was performed using the Simple Online Realtime Tracking (SORT) algorithm. The results demonstrated the capability of machine learning models trained on low-PND data for prediction of high-PND data. Slicing Aided Hyper Inference (SAHI) algorithm is important for the better performance of the used models. By evaluating the velocity statistics, it is found that the mean particle velocity decreases with increasing PND, primarily due to stronger particle interactions. The particle dynamics are closely related to the position of combustion zone observed in the previous study. Thus, PND is considered as the dominant factor for the particle group combustion behavior of high-volatile solid fuels.