Open Material Property Library with Native Simulation Tool Integrations-MASTO

Antti Stenvall*, Valtteri Lahtinen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Reliable material property data are crucial for trustworthy simulations throughout different areas of engineering. Special care must be taken when materials at extreme conditions are under study. Superconductors and devices assembled from superconductors and other materials, like superconducting magnets, are often operated at such extreme conditions: at low temperatures under high magnetic fields and stresses. Typically, some library or database is used for getting the data. We have started to develop a database for storing all kind of material property data online called Open Material Property Library With Native Simulation Tool Integrations-MASTO. The data that can be imported includes, but is not limited to, anisotropic critical current surfaces for high-temperature superconducting materials, electrical resistivities as a function of temperature, RRR and magnetic field, general fits for describing material behaviour. etc. Data can also depend on other data and it can be versioned to guarantee permanent access. The guiding idea in MASTO is to build easy-to-use integration for various programming languages, modeling frameworks and simulation software. Currently, a full-fledged integration is built for MATLAB to allow users to fetch and use data with one-liners. In this paper, we briefly review some of the material property databases commonly used in superconductor modelling, present a case study showing how selection of the material property data can influence the simulation results, and introduce the principal ideas behind MASTO. This work serves as the reference document for citing MASTO when it is used in simulations.

Original languageEnglish
Article number4702005
JournalIEEE Transactions on Applied Superconductivity
Volume28
Issue number3
DOIs
Publication statusPublished - 1 Apr 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Experimental data
  • material property database
  • numerical modelling
  • simulations

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