Python app for stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method

Marmet, Philip; Holzer, Lorenz; Hocker, Thomas; Muser, Vinzenz; Boiger, Gernot K.; Fingerle, Mathias; Reeb, Sarah; Michel, Dominik; Brader, Joseph M.

doi:10.5281/zenodo.7744110

Publication type:	Research data
Title:	Python app for stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method
Authors:	Marmet, Philip Holzer, Lorenz Hocker, Thomas Muser, Vinzenz Boiger, Gernot K. Fingerle, Mathias Reeb, Sarah Michel, Dominik Brader, Joseph M.
et. al:	No
DOI:	10.5281/zenodo.7744110
Issue Date:	22-Mar-2023
Publisher / Ed. Institution:	Zenodo
Language:	English
Subjects:	Solid Oxide Fuel Cell (SOFC); Stochastic geometry; Microstructure modeling; Pluri-Gaussian method; Digital Materials Design; Structure generation software; Virtual materials testing
Subject (DDC):	005: Computer programming, programs and data 621.3: Electrical, communications, control engineering
Abstract:	Digital Materials Design (DMD) offers new possibilities for data-driven microstructure optimization of solid oxide cells (SOC). Despite the progress in imaging technology, 3D-imaging still represents a bottleneck for the application of DMD. Experimental microstructure variation studies are typically limited to a few 3D datasets from tomography. In contrast, stochastic microstructure modeling allows to explore a much larger design space by performing parametric studies. Therefore, the availability of an appropriate virtual structure generator is a crucial prerequisite for realistic design studies. The stochastic microstructure modeling based on the pluri-Gaussian method (PGM) has proven to be well-suited for the virtual reconstruction of SOC electrodes. This dataset provides a Python app for the stochastic microstructure modeling of SOC electrodes in GeoDict based on a pluri-Gaussian method (PGM). The PGM-app allows for an efficient construction of virtual but realistic SOC microstructures consisting of three phases (two solid-phases and one pore-phase). This dataset consists of the following files: 1. The PGM-app is decribed in detail in the file "01_Read_Me_PGM_SOC_App_Zenodo.pdf". 2. The script of the PGM-app is provided in the file "02_PGM_SOC_App.zip".
URI:	https://digitalcollection.zhaw.ch/handle/11475/27933
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Computational Physics (ICP)
Published as part of the ZHAW project:	Versatile oxide fuel cell microstructures employing WGS active titanate anode current collectors compatible to ferritic stainless steel interconnects (VOLTA) GeoCloud – Simulation Software for Cloud-based Digital Microstructure Design of New Fuel Cell Materials
Appears in collections:	ZHAW Forschungsdaten School of Engineering

Files in This Item:

There are no files associated with this item.

Show full item record

Marmet, P., Holzer, L., Hocker, T., Muser, V., Boiger, G. K., Fingerle, M., Reeb, S., Michel, D., & Brader, J. M. (2023). Python app for stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7744110

Marmet, P. et al. (2023) ‘Python app for stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method’. Zenodo. Available at: https://doi.org/10.5281/zenodo.7744110.

P. Marmet et al., “Python app for stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method.” Zenodo, Mar. 22, 2023. doi: 10.5281/zenodo.7744110.

MARMET, Philip, Lorenz HOLZER, Thomas HOCKER, Vinzenz MUSER, Gernot K. BOIGER, Mathias FINGERLE, Sarah REEB, Dominik MICHEL und Joseph M. BRADER, 2023. Python app for stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method. Data set. 22 März 2023. Zenodo

Marmet, Philip, Lorenz Holzer, Thomas Hocker, Vinzenz Muser, Gernot K. Boiger, Mathias Fingerle, Sarah Reeb, Dominik Michel, and Joseph M. Brader. 2023. “Python App for Stochastic Microstructure Modeling of SOC Electrodes Based on a Pluri-Gaussian Method.” Data set. Zenodo. https://doi.org/10.5281/zenodo.7744110.

Marmet, Philip, et al. Python App for Stochastic Microstructure Modeling of SOC Electrodes Based on a Pluri-Gaussian Method. Zenodo, 22 Mar. 2023, https://doi.org/10.5281/zenodo.7744110.