Please use this identifier to cite or link to this item: doi:10.22028/D291-37263
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Title: Resistance monitoring of shape memory material stabilization during elastocaloric training
Author(s): Michaelis, Nicolas
Welsch, Felix
Kirsch, Susanne-Marie
Seelecke, Stefan
Schütze, Andreas
Language: English
Title: Smart materials and structures
Volume: 28
Issue: 10
Publisher/Platform: IOP Publishing
Year of Publication: 2019
DDC notations: 530 Physics
Publikation type: Journal Article
Abstract: Before elastocaloric shape memory alloys (SMAs) can be used as heat transfer medium in cooling applications, the material needs to be stabilized in its mechanical and thermal behaviour. This process consists in tensile loading and unloading with low strain rates for up to 100 cycles and is currently observed with the help of the sample's stress–strain diagram as well as infrared images of the sample to illustrate the phase transformation. Afterwards the sample can be used in cooling applications by applying high strain rates for loading and unloading to achieve relevant temperature changes and high cooling efficiency. This contribution discusses a new approach for monitoring the material stabilization by analysing the self-sensing properties of the SMA. With the help of a scientific test setup different self-sensing parameters have been investigated, with the result that even a relatively simple resistance measurement of the SMA during the elastocaloric training process reflects the stress behaviour and therefore the material stabilization. These results allow an implementation of the monitoring approach directly in SMA-based cooling devices without expensive components such as force and thermographic sensors.
DOI of the first publication: 10.1088/1361-665X/ab3d62
URL of the first publication: https://iopscience.iop.org/article/10.1088/1361-665X/ab3d62/meta
Link to this record: urn:nbn:de:bsz:291--ds-372636
hdl:20.500.11880/33779
http://dx.doi.org/10.22028/D291-37263
ISSN: 1361-665X
0964-1726
Date of registration: 16-Sep-2022
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Andreas Schütze
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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