Abstract
In the present paper, as part of an interdisciplinary research project (Priority Programme SPP2045), we propose a possible way to design an open access archive for particle-discrete tomographic datasets: the PARROT database (https://parrot.tu-freiberg.de). This archive is the result of a pilot study in the field of particle technology and three use cases are presented for illustrative purposes. Instead of providing a detailed instruction manual, we focus on the methodologies of such an archive. The presented use cases stem from our working group and are intended to demonstrate the advantage of using such an archive with concise and consistent data for potential and ongoing studies. Data and metadata merely serve as examples and need to be adapted for disciplines not concerned here. Since all datasets within the PARROT database and its source code are freely accessible, this study represents a starting point for similar projects.
Highlights
In particle technology, a fundamental understanding of distributed particle characteristics is essential to develop efficient optimization strategies for processes based on particle–particle interactions
Besides methods like laser diffraction or the measurement of settling velocity, which are based on model assumptions, for example, considering all particles to be perfect spheres, there are methods based on 2D images like optical microscopy, scanning electron microscopy (SEM), or dynamic image analysis (DIA)
The use cases already introduced in the section “Some practical use cases” are embedded into three different research projects with the following titles: (i) Development of process models based on 3D information about the multiphase processes in the pore space of a filter cake, (ii) Stochastic modeling of multidimensional particle properties with parametric copulas for the investigation of microstructure effects on the fractionation of fine particle systems, (iii) Two-scale approach for the simulation of multidimensional fractionation of fine particles
Summary
A fundamental understanding of distributed particle characteristics is essential to develop efficient optimization strategies for processes based on particle–particle interactions. Besides methods like laser diffraction or the measurement of settling velocity, which are based on model assumptions, for example, considering all particles to be perfect spheres, there are methods based on 2D images like optical microscopy, scanning electron microscopy (SEM), or dynamic image analysis (DIA). With the latter, it is even possible to determine the particle morphology for particle sizes above 100 mm by capturing multiple shadow images under random rotation angles (Macho et al, 2019). We would like to briefly motivate why the database solution shown here (Fig. 1) is a benefit in dealing with such 3D data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
