Data and code underlying Chapter 3 of the PhD thesis "Advanced Magnetocaloric Regenerators for Heat Pump Applications"

DOI:10.4121/cd51b1e3-ab4a-4e6c-a668-f53da25475d4.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/cd51b1e3-ab4a-4e6c-a668-f53da25475d4

Datacite citation style

Pineda Quijano, Diego; Infante Ferreira, Carlos A.; Brück, Ekkes (2025): Data and code underlying Chapter 3 of the PhD thesis "Advanced Magnetocaloric Regenerators for Heat Pump Applications". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/cd51b1e3-ab4a-4e6c-a668-f53da25475d4.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This dataset contains the results of numerical simulations performed to investigate the influence of various layering strategies on the performance of active magnetocaloric regenerators (AMRs) for heat pump applications. These results were published in Applied Thermal Engineering 232 (2023) 120962. The simulations were performed using a one-dimensional numerical model of an AMR implemented in Python.

History

  • 2025-06-12 first online, published, posted

Publisher

4TU.ResearchData

Format

simulation data / .txt, Python scripts with simulation parameters / .py

Funding

  • Integrale Energietransitie in Bestaande Bouw (grant code TEUE919003) Ministerie van Economische Zaken & Klimaat en het Ministerie van Binnenlandse Zaken & Koninkrijksrelaties

Organizations

TU Delft, Faculty of Applied Sciences, Department of Radiation Science and Technology, Fundamental Aspects of Materials and Energy

DATA

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/43678a8e-4de4-4636-8a2f-4a5ea17d770e.git "FAME_AMR_model"

Or download the latest commit as a ZIP.

Files (2)