Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes

Russo, G., Yazdani-Asrami, M. , Scheda, R., Morandi, A. and Diciotti, S. (2022) Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes. Superconductor Science and Technology, 35(12), 124002. (doi: 10.1088/1361-6668/ac95d6)

Abstract

For modelling superconductors, interpolation and analytical formulas are commonly used to consider the relationship between the critical current density and other electromagnetic and physical quantities. However, look-up tables are not available in all modelling and coding environments, and interpolation methods must be manually implemented. Moreover, analytical formulas only approximate real physics of superconductors and, in many cases, lack a high level of accuracy. In this paper, we propose a new approach for addressing this problem involving artificial intelligence (AI) techniques for reconstructing the critical surface of high temperature superconducting (HTS) tapes and predicting their index value known as n-value. Different AI models were proposed and implemented, relying on a public experimental database for electromagnetic specifications of HTS tapes, including artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and kernel ridge regressor (KRR). The ANN model was the most accurate in predicting the critical current of HTS materials, performing goodness of fit very close to 1 and extremely low root mean squared error. The XGBoost model proved to be the fastest method, with training computational times under 1 s; whilst KRR could be used as an alternative solution with intermediate performance.

• Item Type: Articles
• Additional Information: The authors would like to acknowledge the support of the COST Action CA19108 “High-Temperature SuperConductivity for AcceLerating the Energy Transition”.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yazdani-Asrami, Dr Mohammad and Russo, Giacomo
• Authors: Russo, G., Yazdani-Asrami, M., Scheda, R., Morandi, A., and Diciotti, S.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Superconductor Science and Technology
• Publisher: IOP Publishing
• ISSN: 0953-2048
• ISSN (Online): 1361-6668
• Published Online: 21 October 2022
• Copyright Holders: Copyright © 2022 The Author(s)
• First Published: First published in Superconductor Science and Technology 35(12): 124002
• Publisher Policy: Reproduced under a Creative Commons License