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Dynamic modeling of levitation of a superconducting bulk by coupled HH-magnetic field and Arbitrary Lagrangian-Eulerian formulations

Intrinsically stable magnetic levitation between superconductors and permanent magnets can be exploited in a variety of applications of great technical interest in the field of transportation (rail transportation), energy (flywheels) and industry. In this contribution, we present a new model for the calculation of levitation forces between superconducting bulks and permanent magnet, based on the $H$-formulation of Maxwell's equations coupled with an Arbitrary Lagrangian-Eulerian formulation. The model uses a moving mesh that adapts at each time step based on the time-change of the distance between a superconductor bulk and a permanent magnet. The model is validated against a fixed mesh model (recently in turn validated against experiments) that uses an analytical approach for calculating the magnetic field generated by the moving permanent magnet. Then, it is used to analyze the magnetic field dynamics both in field-cooled and zero-field-cooled conditions and successively used to test different configurations of permanent magnets and to compare them in terms of levitation forces. The easiness of implementation of this model and its flexibility in handling different geometries, material properties, and application scenarios make the model an attractive tool for the analysis and optimization of magnetic levitation-based applications

Advent of a Link between Ayurveda and Modern Health Science: The Proceedings of the First International Congress on Ayurveda, “Ayurveda: The Meaning of Life—Awareness, Environment, and Health” March 21-22, 2009, Milan, Italy

The First International Congress on Ayurveda was held in Milan, Italy in March 2009 and it has been the first scientific event of this kind in western world. This groundbreaking international congress was devoted to human being as the product of interactions between Awareness, Environment and Health, subjects that the West tends to consider separate and independent, but that are believed deeply connected in Ayurveda, whose interdependence defines “The Meaning of Life”. The Congress established a bridge between indian and western philosophy, scientific and biomedical thinking in order to expand knowledge and healthcare. Main attention and address of the invited speakers was on the concept of “relationships” that, connecting living beings with environment, shape Nature itself. This concept is central in Ayurveda but is also common to other western scientific disciplines such as quantum physics and epigenetics that, in the four Sessions of the Congress, were represented by eminent experts. The importance of this event was underlined by the attendance of more than 400 participants and by noteworthy institutional endorsements, that added a significative political dimension of high social impact due to the topical period for CAM acceptance and integration in Europe

Feasibility of high temperature superconducting cables for energy harvesting in large space-based solar power satellite applications: electromagnetic, thermal and cost considerations

The aim of this paper is to present feasibility of application of High Temperature Superconducting (HTS) cables for Space-Based Solar Power (SBSP) application. SBSP is a promising technology that can deliver an infinite amount of clean and eco-friendly energy to the Earth. To deliver the harvested solar energy to the power systems on Earth, efficient energy transmission is critically important. To address the challenges of conventional transmission means, an ideal solution would be using HTS cables. In this research, the design procedure of a Direct Current (DC) HTS cable considering electromagnetic, thermal, and cost constraints is presented. The study considered a 2 MW bipolar DC HTS cable in five operational temperatures: 20 K, 30 K, 50 K, 65 K, and 77 K. The results were showed that the cost and weight of the designed HTS cables were increased by operational temperature increase. However, the cooling cost of HTS cable in higher temperatures, was less than lower temperatures. Also, the total efficiency of the HTS cable and cooling system were increased, when operational temperatures were changed from 20 K to 77 K, from 99.70% to 99.97%, respectively

CIFRA: Challenging the ICT Patent Framework for Responsible Innovation. D2.1: Literature Review

The European Commission. CIFRA: Challenging the ICT Patent Framework for Responsible Innovation. Grant Agreement No.731940. Research and Innovation Action. Call: H2020-ICT-35-201

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

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. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 Root Mean Squared Error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). On the other hand, the Rigid Regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, Random Forest, boosting methods, and simple Feed Forward Neural Network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modelling of superconductors but also pave the way for applications and further research on machine learning plug-and-play codes for superconducting studies including modelling of superconducting devices

A new benchmark problem for electromagnetic modelling of superconductors: the high-Tc_{c} superconducting dynamo

The high-T$_{c}$ superconducting (HTS) dynamo is a promising device that can inject large DC supercurrents into a closed superconducting circuit. This is particularly attractive to energise HTS coils in NMR/MRI magnets and superconducting rotating machines without the need for connection to a power supply via current leads. It is only very recently that quantitatively accurate, predictive models have been developed which are capable of analysing HTS dynamos and explain their underlying physical mechanism. In this work, we propose to use the HTS dynamo as a new benchmark problem for the HTS modelling community. The benchmark geometry consists of a permanent magnet rotating past a stationary HTS coated-conductor wire in the open-circuit configuration, assuming for simplicity the 2D (infinitely long) case. Despite this geometric simplicity the solution is complex, comprising time-varying spatially-inhomogeneous currents and fields throughout the superconducting volume. In this work, this benchmark problem has been implemented using several different methods, including H-formulation-based methods, coupled H-A and T-A formulations, the Minimum Electromagnetic Entropy Production method, and integral equation and volume integral equation-based equivalent circuit methods. Each of these approaches show excellent qualitative and quantitative agreement for the open-circuit equivalent instantaneous voltage and the cumulative time-averaged equivalent voltage, as well as the current density and electric field distributions within the HTS wire at key positions during the magnet transit. Finally, a critical analysis and comparison of each of the modelling frameworks is presented, based on the following key metrics: number of mesh elements in the HTS wire, total number of mesh elements in the model, number of degrees of freedom, tolerance settings and the approximate time taken per cycle for each model. This benchmark and the results contained herein provide researchers with a suitable framework to validate, compare and optimise their own methods for modelling the HTS dynamo

A new benchmark problem for electromagnetic modelling of superconductors: the high- T c superconducting dynamo

Abstract: The high-T c superconducting (HTS) dynamo is a promising device that can inject large DC supercurrents into a closed superconducting circuit. This is particularly attractive to energise HTS coils in NMR/MRI magnets and superconducting rotating machines without the need for connection to a power supply via current leads. It is only very recently that quantitatively accurate, predictive models have been developed which are capable of analysing HTS dynamos and explain their underlying physical mechanism. In this work, we propose to use the HTS dynamo as a new benchmark problem for the HTS modelling community. The benchmark geometry consists of a permanent magnet rotating past a stationary HTS coated-conductor wire in the open-circuit configuration, assuming for simplicity the 2D (infinitely long) case. Despite this geometric simplicity the solution is complex, comprising time-varying spatially-inhomogeneous currents and fields throughout the superconducting volume. In this work, this benchmark problem has been implemented using several different methods, including H-formulation-based methods, coupled H-A and T-A formulations, the Minimum Electromagnetic Entropy Production method, and integral equation and volume integral equation-based equivalent circuit methods. Each of these approaches show excellent qualitative and quantitative agreement for the open-circuit equivalent instantaneous voltage and the cumulative time-averaged equivalent voltage, as well as the current density and electric field distributions within the HTS wire at key positions during the magnet transit. Finally, a critical analysis and comparison of each of the modelling frameworks is presented, based on the following key metrics: number of mesh elements in the HTS wire, total number of mesh elements in the model, number of degrees of freedom, tolerance settings and the approximate time taken per cycle for each model. This benchmark and the results contained herein provide researchers with a suitable framework to validate, compare and optimise their own methods for modelling the HTS dynamo

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

For modelling superconductors, interpolation and analytical formulas are commonly used to consider the relationship between the critical current density and other electromagnetic and physical quantities. However, look-up tables are not available in all modelling and coding environments, and interpolation methods must be manually implemented. Moreover, analytical formulas only approximate real physics of superconductors and, in many cases, lack a high level of accuracy. In this paper, we propose a new approach for addressing this problem involving artificial intelligence (AI) techniques for reconstructing the critical surface of high temperature superconducting (HTS) tapes and predicting their index value known as n-value. Different AI models were proposed and implemented, relying on a public experimental database for electromagnetic specifications of HTS tapes, including artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and kernel ridge regressor (KRR). The ANN model was the most accurate in predicting the critical current of HTS materials, performing goodness of fit very close to 1 and extremely low root mean squared error. The XGBoost model proved to be the fastest method, with training computational times under 1 s; whilst KRR could be used as an alternative solution with intermediate performance