Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network
- The efficiency of many chemical engineering applications depends on the surface/volume ratio of the dispersed phase. Knowledge of this particle size distribution is a key factor for better process control. The challenge of measurements acquired by optical imaging techniques is the segmentation of overlapping particles, especially in high phase fraction flows. In this work, a convolutional neural network is trained to segment droplets in images acquired by a shadowgraphic approach. The network is trained on artificial images and implemented into a droplet size algorithm. The results are compared to an OpenSource segmentation approach.
Author: | Jan Schäfer, Philipp Schmitt, Mark W. Hlawitschka, Hans-Jörg Bart |
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URN: | urn:nbn:de:hbz:386-kluedo-79538 |
DOI: | https://doi.org/10.1002/cite.201900099 |
ISSN: | 1522-2640 |
Parent Title (English): | Chemie Ingenieur Technik |
Publisher: | Wiley |
Document Type: | Article |
Language of publication: | English |
Date of Publication (online): | 2024/04/05 |
Year of first Publication: | 2019 |
Publishing Institution: | Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau |
Date of the Publication (Server): | 2024/04/05 |
Issue: | 91/11 |
Page Number: | 8 |
First Page: | 1688 |
Last Page: | 1695 |
Source: | https://onlinelibrary.wiley.com/doi/10.1002/cite.201900099 |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik |
DDC-Cassification: | 6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau |
Collections: | Open-Access-Publikationsfonds |
Licence (German): | Zweitveröffentlichung |