Zhang, G. orcid.org/0000-0002-0553-0778, Xu, H., Wu, D. orcid.org/0000-0003-4500-4390 et al. (5 more authors) (2024) Deep learning-driven analysis for cellular structure characteristics of spherical premixed hydrogen-air flames. International Journal of Hydrogen Energy, 68. pp. 63-73. ISSN 0360-3199
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Premixed combustion; Instability; Spherical flame; Deep learning; Image processing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/W002299/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Jun 2024 10:51 |
Last Modified: | 11 Jun 2024 10:51 |
Published Version: | https://www.sciencedirect.com/science/article/pii/... |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ijhydene.2024.04.232 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213299 |
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