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Song W., Yazdani-Asrami M., Sadeghi A., Seyyedbarzegar S.

DC Electro-Magneto-Mechanical Characterization of 2G HTS Tapes for Superconducting Cable in Magnet System Using Artificial Neural Networks

Ключевые слова: HTS, REBCO, coated conductors, tapes, twisting, CORC cables, magnetic systems, neural networks, modeling, numerical analysis, mechanical properties, stress effects, critical caracteristics, critical current density, training effect, stability

IEEE Transactions Applied Superconductivity, 2022, v.32, N 7, p.4505810

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Morandi A., Song W., Parizh M., Murta-Pina J., Yazdani-Asrami M., Sadeghi A., Madureira A.

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Ключевые слова: modeling computational, modeling, large-scale applications, electronics, history, neural networks, MRI magnets, accelerator magnets, fusion magnets, cables, SMES, rotating machines, transformers, FCL, Maglev system, fabrication, review, artificial intelligence

Superconductivity: Theory, Materials and Applications, 2022, v.35, N 12, p.123001

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Morandi A., Yazdani-Asrami M., Russo G., Scheda R., Diciotti S.

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

Ключевые слова: neural networks, HTS, tapes, critical current, n-value, angular dependence, magnetic field dependence, temperature dependence, numerical analysis, modeling, artificial intelligence

Superconductor Science Technology, 2022, v.35, N 12, p.124002

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Das M.K., Bera P., Sarkar P.P.

PID-RLNN controllers for discrete mode LFC of a three-area hydrothermal hybrid distributed generation deregulated power system

Ключевые слова: renewable energy systems, power distribution system, power equipment, control systems, frequency dependence, neural networks, SMES, modeling, numerical analysis

International Transactions Electrical Energy Systems, 2021, v.31, N 5, p.12837

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Wang Y., Liu W., Liu Y., Pi W., Zhu L., Nie Y.

Conceptual Design of HTS Bitter Magnet Above 25T Using a Fast Magnetic Field Computational Method

Ключевые слова: HTS, REBCO, coated conductors, plates, high field magnets, design parameters, modeling computational, design, neural networks, solder, joints, current distribution, stress distribution, magnetic field distribution, numerical analysis, model small-scale, test results

IEEE Transactions Applied Superconductivity, 2021, v.31, N 5, p.4601806

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Shi J., Xu J., Hua J., Zhou X., Gong K., Zhu C.

A Novel Quench Detection Method Based on CNN-LSTM Model

Ключевые слова: new, quench detection, measurement technique, HTS, neural networks, modeling, electronics, voltage, HTS, tapes, experimental results

IEEE Transactions Applied Superconductivity, 2021, v.31, N 5, p.4702105

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Shin Y., Lee Y.H.

Time-Frequency Domain Reflectometry for Live HTS Cable System via Inductive Couplers and Neural Network

Ключевые слова: power equipment, power transmission lines, HTS, cables, control systems, neural networks, measurement setup

IEEE Transactions Applied Superconductivity, 2021, v.31, N 5, p.5400305

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Viatkin D., Garcia-Zapirain B., Mendez-Zorrilla A., Zakharov M.

Deep Learning Approach for Prediction of Critical Temperature of Superconductor Materials Described by Chemical Formulas

Ключевые слова: critical temperature, neural networks, numerical analysis, modeling

Frontiers Materials, 2021, v.8, N , p.714752

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Shi Y., Liu H., Zhang S., Liu F., Qin J., Zhou C., Gao P., Ma H.

Research on Excitation Current Control System of the 50 kA Superconducting Transformer

Ключевые слова: transformers, LTS, NbTi, wires, cable-in-conduit conductor, current, excitation system, control systems, experimental results, modeling, neural networks, numerical analysis, comparison

IEEE Transactions Applied Superconductivity, 2021, v.31, N 8, p.5501104

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Afrasiabi S., Afrasiabi M., Mohammadi M., Parang B.

Designing a composite deep learning based differential protection scheme of power transformers

Ключевые слова: power equipment, transformers, conventional systems, protective system, neural networks, FCL, comparison, modeling, numerical analysis, current waveforms, experimental results, fault currents

Applied Soft Computing J, 2020, v.82, N , p.105975

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Kim J., Lee S., Ko T.K., Baek G.

Artificial neural network interpolation for magnetic field mapping in an air-core HTS quadruple magnet

Ключевые слова: magnets quadrupole, core air, HTS, GdBCO, coated conductors, neural networks, magnetic field distribution, coils pancake, coils racetrack, modeling, numerical analysis

Cryogenics, 2020, v.107, N , p.103043

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Song W., Yuan W., Zhang M., Yazdani-Asrami M., TAGHIPOUR-GORJIKOLAIE M.

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Ключевые слова: HTS, REBCO, coated conductors, ac losses, modeling, neural networks, current, harmonic coefficients, artificial intelligence

IEEE Access, 2020, v.2020, N 8, p.207287-207297

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Wielgosz M., Skoczen A.

USING NEURAL NETWORKS WITH DATA QUANTIZATION FOR TIME SERIES ANALYSIS IN LHC SUPERCONDUCTING MAGNETS

Ключевые слова: LHC, magnets, modeling, quench protection, training effect, numerical analysis, neural networks

International J Applied Mathematic Computer Science, 2019, v.29, N 3, p.503-515

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Wielgosz M., Skoczen A.

Recurrent Neural Networks with Grid Data Quantization for Modeling LHC Superconducting Magnets Behavior

Ключевые слова: neural networks, LHC, magnets, modeling computational

Advances Intelligent Systems Computing, 2019, v.495, N , p.175-90

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Wielgosz M., Skoczen A., Mertik M.

Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

Ключевые слова: LHC, high field magnets, protective system, quench protection, experimental results, numerical analysis, neural networks

Nuclear Instruments Methods in Physical Research A, 2017, v.867, N , p.40-50

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Masson P.J., Lorin C., Leclerc J., Hell L.M.

Artificial neural networks for AC losses prediction in superconducting round filaments

Ключевые слова: wires round, ac losses, rotating machines, modeling, numerical analysis, angular dependence, neural networks

Superconductor Science Technology, 2016, v.29, N 6, p.65008

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Zanino R., Bonifetto R., Carli S., Froio A., Quartararo A., Savoldi. L.

Design and optimization of Artificial Neural Networks for the modelling of superconducting magnets operation in tokamak fusion reactors

Ключевые слова: Tokamak, coils toroidal, central coils, ITER, cryogenic systems, helium liquid, heat loads, modeling, numerical analysis, neural networks

J Computational Physics, 2016, v.321, N , p.476-491

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