A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Aug 5, 2026

Upload by Eden

Index-value (n-value) prediction is essential for characterizing superconducting behavior and for accurate modeling when coated-conductor HTS devices are considered. The n-value depends on temperature as well as magnetic field density and orientation, influencing losses and quench propagation. The study evaluates multiple machine learning approaches for estimating n-value and shows that a Cascade Forward Neural Network achieves the best accuracy (0.48 RMSE, 99.72% R-squared).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages31

Reading Time1 h 18 min

AccessFree to download

Introduction

Machine learning methods for n-value estimation

Results and performance comparison

Implications for modeling and future research

Machine Learning: Science and Technology ACCEPTED MANUSCRIPT • OPEN ACCESS A comprehensive machine learning-based investigation for the indexvalue prediction of 2G HTS coated conductor tapes To cite this article before publication: Shahin Al[ipour Bonab](ipour Bonab et al 2024 Mach. Learn.: Sci. Technol. in)[ et al](ipour Bonab et al 2024 Mach. Learn.: Sci. Technol. in)[ 2024](ipour Bonab et al 2024 Mach. Learn.: Sci. Technol. in)[ Mach. Learn.: Sci. Technol.](ipour Bonab et al 2024 Mach. Learn.: Sci. Technol. in)[ in](ipour Bonab et al 2024 Mach. Learn.: Sci. Technol. in) press [https://doi.org/10.1088/2632-](https://doi.org/10.1088/2632-)[ ](https://doi.org/10.1088/2632-)[2153/ad45b1](2153/ad45b1) Manuscript version: Accepted Manuscript Accepted Manuscript is “the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP Publishing of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP Publishing and/or its licensors” This Accepted Manuscript is © 2024 The Author(s) . Published by IOP Publishing Ltd. As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 4.0 licence, this Accepted Manuscript is available for reuse under a CC BY 4.0 licence immediately. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence [https://creativecommons.org/l](https://creativecommons.org/l)icences/by/4 .0 Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record. View the article online for updates and enhancements. Page 1 of 30 AUTHOR SUBMITTED MANUSCRIPT-MLST-101750.R2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A comprehensive machine learning-based investigation for the indexvalue prediction of 2G HTS coated conductor tapes Shahin Alipour Bonab1, Giacomo Russo2, Antonio Morandi2, and Mohammad Yazdani Asrami1* 1 Propulsion, Electrification & Superconductivity group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom 2 Department of Electrical, Electronic, and Information Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy *Corresponding author’s [email: mohammad.yazdani-asrami@glasgow.ac.uk](email: mohammad.yazdani-asrami@glasgow.ac.uk) Abstract: Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modelling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of HTS devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning methods for estimating the n-value has been carried out. The results demonstrated that Cascade Forward Neural Network (CFNN) excels in this scope

FAQs

What does the n-value (index-value) represent in superconductors?

The n-value, also called index-value and power exponent, is fundamental for reproducing the nonlinear constitutive behavior in superconducting models.

Which machine learning model performs best for n-value prediction?

The Cascade Forward Neural Network (CFNN) delivers the highest accuracy, reaching 0.48 RMSE and 99.72% R-squared.

Why is n-value prediction important for coated-conductor HTS devices?

Because the n-value affects superconducting losses and quench propagation, improving reliability of device modeling under varying temperature and magnetic conditions.

Aug 5, 2026

Upload by Eden