A Public Database of High-Temperature Superconductor Critical Current Data

Wimbush, Stuart C.; Strickland, N. M.

doi:10.1109/tasc.2016.2628700

Cited by 150 publications

(112 citation statements)

References 4 publications

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“…In Figure 4, the measured transport critical current vs magnetic field dependence is shown in One can see that at temperature below 40 K in the field perpendicular to the tape plane (Bc) the critical current has a global minimum. No secondary I c maxima occur at other orientations except Bab, which is consistent with other authors [7]. So Bc is the worst case of field orientation.…”

Section: Investigations Of the Hts Tape Propertiessupporting

confidence: 92%

“…For the solenoid winding the double pancake structure is chosen. The main advantage of this structure is maintainability: in the case of quench the damaged pancakes could be easily 7 replaced. In addition, this structure allows the use of pancakes with lower I c , taking into account different orientations and values of the field in different coils.…”

Section: Investigations Of the Hts Tape Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Solenoid from Experimental HTS tape for Magnetic Refrigeration

Krasnoperov¹,

Guryev²,

Shavkin³

et al. 2019

JESTR

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Section: Investigations Of the Hts Tape Propertiessupporting

confidence: 92%

Section: Investigations Of the Hts Tape Propertiesmentioning

confidence: 99%

Solenoid from Experimental HTS tape for Magnetic Refrigeration

Krasnoperov¹,

Guryev²,

Shavkin³

et al. 2019

JESTR

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“…Although there are many public data available for superconductors [9], [10], the present study used only the SuperCon database. We will restate the refined data from the study [11] because of significant reasons.…”

Section: Critical Temperature Predictive Model a High-tc Datamentioning

confidence: 99%

Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach

Noumeir

Quach

et al. 2020

IEEE Trans. Appl. Supercond.

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Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits when superconductivity data often come from costly and arduously experimental work. However, this assessment cannot be based solely on an open black-box machine learning, which is not fully interpretable, because it can be counterintuitive to understand why the model may give an appropriate response to a set of input data for superconductivity characteristic analyses, e.g., critical temperature. The purpose of this study is to describe and examine an alternative approach for predicting the superconducting transition temperature Tc from SuperCon database obtained by Japan's National Institute for Materials Science. We address a generative machine-learning framework called Variational Bayesian Neural Network using superconductors chemical elements and formula to predict Tc. In such a context, the importance of the paper in focus is twofold. First, to improve the interpretability, we adopt a variational inference to approximate the distribution in latent parameter space for the generative model. It statistically captures the mutual correlation of superconductor compounds and; then, gives the estimation for the Tc. Second, a stochastic optimization algorithm, which embraces a statistical inference named Monte Carlo sampler, is utilized to optimally approximate the proposed inference model, ultimately determine and evaluate the predictive performance. As a result, in comparison with the standard evaluation metrics, the results are promising and also agree with the existing models prevalent in the field. The R 2 value obtained is very close to the best model (0.94), whereas a considerable improvement is seen in the RMSE value (3.83 K). Notably, the proposed model is known as the first of its kind for predicting a superconductor's Tc.Index Terms-Critical transition temperature, machine learning, Bayesian neural network, variational inference, stochastic optimization algorithm, high temperature superconducting (HTS).

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“…For this work, the n value was fixed to 20. The critical current data J c (B, θ, T) was taken from [7], corresponding to SuperOx superconducting tape and the data is shown in Fig. 1a, b.…”

Section: Modelling Framework and Materials Propertiesmentioning

confidence: 99%

Remanent Magnetic Flux Distribution in Superconducting-Ferromagnetic Layered Heterostructures

Baškys

Patel

Climente-Alarcón

et al. 2019

J Supercond Nov Magn

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Stacks of superconducting tape, made of second-generation coated conductors, present an interesting alternative to bulk superconductors. The layered geometry allows incorporation of other functional, namely ferromagnetic, materials. The ferromagnetic material changes the field configuration within the superconductor and can be used to shape or divert the flux density from its interior. This can be used to optimise the remanent flux density after magnetisation. FEM simulations are employed to test the performance of a variety of superconductor/ferromagnet structures and explain the role of the ferromagnetic material in the composite structure. Cases where trapped field is much higher and lower than remanent magnetisation of the ferromagnetic material are considered, showing that performance can be enhanced or maintained by using ferromagnetic material while reducing the usage of costly superconducting tape.

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A Public Database of High-Temperature Superconductor Critical Current Data

Cited by 150 publications

References 4 publications

Solenoid from Experimental HTS tape for Magnetic Refrigeration

Solenoid from Experimental HTS tape for Magnetic Refrigeration

Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach

Remanent Magnetic Flux Distribution in Superconducting-Ferromagnetic Layered Heterostructures

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