DC electro-magneto-mechanical characterisation of 2G HTS tapes for superconducting cable in magnet system using artificial neural networks

Yazdani-Asrami, M. , Sadeghi, A., Seyyedbarzegar, S. and Song, W. (2022) DC electro-magneto-mechanical characterisation of 2G HTS tapes for superconducting cable in magnet system using artificial neural networks. IEEE Transactions on Applied Superconductivity, 32(7), 4605810. (doi: 10.1109/TASC.2022.3193782)

Abstract

Characterization of the exact critical current density (Jc) and stress values in twisted superconducting tapes plays an important role for analysing their magnetic, thermal, and mechanical behaviours. In this paper, a model based on Artificial Neural Network (ANN) is introduced to estimate the electro-magneto-mechanical characteristic of different superconducting tapes. For this purpose, magnetic flux density, temperature, strain, total thickness of tape, their width, thickness of stabilisers, and thickness of substrates are used as inputs to ANN model whilst minimum normalised Jc and maximum stress are considered as outputs. The required experimental data are extracted from published papers in literature. The ANN model was trained for Jc/stress estimation using extracted data for different inputs. Sensitivity analysis was conducted on ANN models which were used to estimate the Jc and stress values of tapes, to choose an optimum structure for ANN models to be used in future by other scientists in the superconductivity community. To check the reproducibility, repeatability, and stability of presented results, the estimations with ANN optimum structure were tested for 500 testing runs. We found that the ANN optimum structure was as 1 hidden layer with Levenberg-Marquardt training method and 7 inputs. Comparing to the literature, the proposed ANN model offers about 15% and 1.1% higher accuracy in Jc and stress estimations, respectively.

• Item Type: Articles (Other)
• Keywords: ANN, critical current, magnetic field, HTS tapes, stress, strain, temperature.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yazdani-Asrami, Dr Mohammad and Song, Dr Wenjuan
• Authors: Yazdani-Asrami, M., Sadeghi, A., Seyyedbarzegar, S., and Song, W.
• Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Applied Superconductivity
• Publisher: IEEE
• ISSN: 1051-8223
• ISSN (Online): 1558-2515
• Published Online: 25 July 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Applied Superconductivity 7: 4605810
• Publisher Policy: Reproduced in accordance with the publisher copyright policy