Exergetic analysis of reverse Brayton cryocooler with different turbine arrangements for HTS power cables

Dhillon, Aman Kumar; Ghosh, Parthasarathi

doi:10.1016/j.cryogenics.2021.103262

Cited by 10 publications

(1 citation statement)

References 36 publications

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“…The temperature at which the resistance suddenly drops is called the superconducting critical temperature ( T c ). The two unique physical properties of superconductors (complete electrical conductivity and complete diamagnetism) play vital roles in many industrial manufacturing fields, such as mechanical equipment, superconducting motors, nondestructive parts testing, instruments, and power systems . In recent years, with the discovery of MgB 2 and other superconductors, the application scope of superconductors has been increased due to the advantages of easy preparation and low cost. , On the other hand, La(H 10 ) and other superconductors have been discovered, which show room-temperature superconductivity under high pressure, and research on T c has attracted more attention .…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Critical Temperature of Superconductors Based on Two-Layer Feature Selection and the Optuna-Stacking Ensemble Learning Model

Zhao

Pan

et al. 2023

ACS Omega

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The study of superconductors' critical temperature (T c ) has been a matter of interest. A method combining a twolayer feature selection (TL) and Optuna-Stacking ensemble learning model is proposed in the study for predicting T c from physicochemical components. Since most machine-learning models require a large amount of prior knowledge to construct the feature vectors associated with T c manually, they may contain redundant or invalid features that adversely affect the analysis and prediction of T c . The TL model combines the advantages of filtered and packed feature selection. In the first layer, feature importance is ranked by "SHapley Additive explain (SHAP)" in combination with CatBoost, followed by maximum mutual information coefficient (MIC) and distance correlation coefficient (DCC) for initial feature selection in terms of feature importance ranking. The second layer uses a cross-validation-based genetic algorithm (cv-GA) to eliminate the remaining redundant/invalid features. The selected features are fed into the Stacking integrated learning model to achieve prediction of Tc, and the multidimensional hyperparametric optimization of the metamodel is achieved by Optuna, an improved Bayesian hyperparametric optimization framework based on the Tree-structured Parzen Estimator (TPE) and pruning strategy. The model has obvious advantages and generality in terms of prediction performance and feature reduction rate, and it also proves to be suitable for hightemperature superconductor T c prediction. It provides an efficient and cost-effective method for data-driven superconductor research.

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