Nonlinear Levitation-Guidance Coupling Force Prediction for HTS Pinning Maglev Under Arbitrary Motion Based on Gated Recurrent Unit

Ke, Zhihao; Liu, Xiaoning; Yi, Huiyang; Jiang, Kunyu; Wang, Li; Deng, Zigang

doi:10.1109/tasc.2024.3356460

Cited by 1 publication

(1 citation statement)

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“…AI's ability to handle complex problems extends to the prediction of levitation forces, a critical aspect of maglev technology. Aligning with [24,25], where the authors utilize neural networks, such as back-propagation and gated recurrent unit models, to predict complex and nonlinear levitation and lateral forces, this study tries to implement robust AI techniques to develop intelligent models for prediction of these forces for MgB 2 bulks with various dimensional sizes.…”

Section: Introductionmentioning

confidence: 99%

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

Alipour Bonab,

Xing,

Russo

et al. 2024

Supercond. Sci. Technol.

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The advent of superconducting bulks, because of their compactness and performance, offers new perspectives and opportunities in many applications and sectors, such as magnetic field shielding, motors/generators, NMR/MRI, magnetic bearings, flywheel energy storage, Maglev trains, among others. The investigation and characterization of bulks typically relies on time-consuming and expensive experimental campaigns; hence the development of effective surrogate models would considerably speed up the research progress around them. In this study, we have first produced an experimental dataset with the levitation and lateral forces between different MgB2 bulks and one permanent magnet under different operating conditions. Next, we have exploited the dataset to develop surrogate models based on Artificial Intelligence (AI) techniques, namely Extremely Gradient Boosting (XGBoost), Support Vector Machine Regressor (SVR), and Kernel Ridge Regression (KRR). After the tuning of the hyperparameters of the AI models, the results demonstrated that SVR is the superior technique and can predict levitation and lateral forces with a worst-case accuracy scenario 99.86% in terms of goodness of fit to experimental data. Moreover, the response time of these models for prediction of new datapoints is ultra-fast.

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Section: Introductionmentioning

confidence: 99%

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

Alipour Bonab,

Xing,

Russo

et al. 2024

Supercond. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Nonlinear Levitation-Guidance Coupling Force Prediction for HTS Pinning Maglev Under Arbitrary Motion Based on Gated Recurrent Unit

Cited by 1 publication

References 20 publications

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

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Nonlinear Levitation-Guidance Coupling Force Prediction for HTS Pinning Maglev Under Arbitrary Motion Based on Gated Recurrent Unit

Cited by 1 publication

References 20 publications

Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques

Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques

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Resources

About

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques