Deep learning-driven analysis for cellular structure characteristics of spherical premixed hydrogen-air flames

Abstract

The extraction of cellular structure feature on the spherical premixed flame surface faces accuracy challenges. The Schlieren technique was employed to obtain the hydrogen-air premixed spherical flames images in a constant volume vessel at room temperature and atmospheric pressure under an equivalent ratio of 0.8 in this work. A bio-inspired Cellpose 2.0, driven by deep learning, is innovatively introduced to train the cell segmentation model in the combustion field. After labeling and training cells of different shapes and sizes, an efficient and accurate model suitable for cell feature extraction was finally obtained to identify and quantify various cells characteristics, such as number, size, and distribution. Results show that the average precision (AP) during the model online pre-training process reaches 0.625. Meanwhile, the critical flame radius of transition acceleration obtained is 36 mm and the crack length tends to grow linearly after the flame radius exceeds this critical point. Additionally, the average cell area gradually converges to a stable value after the flame radius exceeds the uniform cellularity critical radius. The cell segmentation model obtained in this work can be further used to train different spherical flames under various conditions, helping to develop hydrogen combustion and explosion modelling.