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Numerical and Experimental Investigation of Particle Size Distributions in Multiphase Flows

  • This thesis investigates the numerical and experimental characterization of the particle size distribution in two different chemical engineering applications (liquid-gas and liquid-liquid). First, a gas-liquid flow in a pseudo 2D bubble column is investigated using optical imaging techniques. Objective of the experiment is the localized measurement of the bubble size distribution, as well as the orientation and movement of the gas bubbles. This is achieved using an automated camera traverse system, capturing high-speed images of different zones in the bubble column. The images are then evaluated using an algorithmic approach consid- ering the bubble size, movement direction and orientation of the gas bubbles. The measured quantities are evaluated in each zone of the bubble column. The captured experimental data is then used as a validation for the implementation of the Sectional Quadratic Method of Moments (SQMoM), as a solution for the population balance equation, in the computational fluid dynamics (CFD) solver multiphaseEulerFoam of the open-source software OpenFOAM. The simulation is specifically compared to the zone-wise data of the bubble column experi- ments. The adapted solver is able to simulate the development of the bubble size distribution in the different zones of the bubble column. The second investigation considers a liquid-liquid flow in a mixer-settler with the objective to analyze knitted meshes as coalescing aids. The experiment is executed using an automated mixer-settler set-up. To control the droplet size distribution, leaving the mixer and entering the settler, a control loop is developed to set the mixer speed. The setup uses a convolutional neural network (CNN) to segment droplet clusters, in the live captured camera images. The CNN is trained in a two-step approach, which uses an algorithmic method for trainings database generation, to segment a representative image, in the first step. In a second step, the previously trained network is used to create a database of images to train a second network for the live evaluation. This allows a training without the need of for a large already evaluated database. An adaption of the presented method and an alternative method using a generative adversarial neural network (GAN) for trainings database generation is tested considering gas-liquid multiphase flows. The automated mixer-settler allows for an investigation of the knitted meshes as coalescing aids under a controlled load, while the total flow rate, or the phase ratio, or the droplet size distribution is gradually changed. The results of the experiments are used to adapt an iterative design approach for settlers to implement the usage of knitted meshes as coalescencing aids.

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Metadaten
Author:Jan Schäfer
URN (permanent link):urn:nbn:de:hbz:386-kluedo-65339
DOI:https://doi.org/10.26204/KLUEDO/6533
Advisor:Hans-Jörg Bart
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2021/08/29
Year of Publication:2021
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2021/06/17
Date of the Publication (Server):2021/08/30
Number of page:XXVI, 116
Faculties / Organisational entities:Fachbereich Maschinenbau und Verfahrenstechnik
DDC-Cassification:6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Licence (German):Creative Commons 4.0 - Namensnennung, nicht kommerziell, keine Bearbeitung (CC BY-NC-ND 4.0)