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Full-waveform inversion is an important and widely used method to reconstruct subsurface velocity images. Waveform inversion is a typical nonlinear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle-skipping and local-minima issues, and do not mention that solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform-inversion problem. However, most deep-learning frameworks suffer from the generalization and overfitting issue. In this article, we developed a real-time data-driven technique and we call it VelocityGAN, to reconstruct accurately the subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end to end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoder–decoder-based data-driven seismic waveform-inversion approaches, our VelocityGAN learns regularization from data and further imposes the regularization to the generator so that inversion accuracy is improved. We further develop a transfer-learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments is conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with the existing physics-driven approaches and data-driven frameworks but also conduct several transfer-learning experiments. The experimental results show that VelocityGAN achieves the state-of-the-art performance among the baselines and can improve the generalization results to some extent. [ABSTRACT FROM AUTHOR]

A numerical multiscale model is used to resolve the high computation complexity in the ac loss calculation of large-scale superconducting devices and proves advantageous with regard to time and memory requirements. In the multiscale model, the background field estimation method is of crucial importance to the calculation accuracy. In this paper, we propose four new background field estimation methods, including the Jacobi Gauss–Seidel hybrid and iteration methods, to improve the accuracy and expand the application scope of the multiscale model. A high temperature superconducting solenoid magnet is used to test the performance of these four methods and the results are compared with those of the H-formulation reference model and the multiscale model with a J0 uniform background field estimation method. Results show that in the 19 A/50 Hz ac transport current test case: the Jacobi Gauss– hybrid and iteration methods reduce the error of the ac loss mean value/peak value to 7.88%/5.89%, 5.71%/2.4%, 3.22%/1.22%, and 1.83%/0.69% with the total time cost reduced by 15.7, 8.9, 13.5, and 7.8 times. The multiscale model is expected to achieve even higher accuracy in applications where the ac loss is relatively small due to its coincidence property. The greatly improved accuracy makes the multiscale model a reliable substitute for the H-formulation model. In addition, the idea of iteration and back substitution in background field correction widely expands the application scope of the multiscale model. Alternative background field estimation methods allow the multiscale model to be applied flexibly according to the requirement of the calculation speed and accuracy. The feature of small degrees of freedom resolves the high computation complexity of the ac loss calculation model and reduces the memory requirement. All these merits make the multiscale model competitive in the calculation of ac loss. [ABSTRACT FROM AUTHOR]

The calculation of ac loss is an essential part in the thermal stability estimation of the high-temperature superconductor (HTS) superconducting magnetic energy storage system (SMES). In this paper, we propose an artificial neural network (ANN) prediction model to calculate the ac loss in real-time and apply the ANN prediction model to a 150 kJ self-made HTS SMES. The ANN prediction model is composed of a multi-scale model and an ANN. An ac loss data set calculated with the multi-scale model is used to train the ANN, and a validation data set is used to test the accuracy of the ANN after training. By comparing with the homogenization model, we find that the multi-scale model is reliable when used for ac loss calculation and suitable for building ac loss data set due to its high calculation speed. The ANN prediction model is verified to be reliable for fast estimation of the ac loss, and its accuracy improves as the scale of the training data set increases. [ABSTRACT FROM AUTHOR]

Superconducting fault current limiters (SFCLs) are considered as one of the most promising technologies to limit fault current levels, as they can suppress fault currents without adding impedance to the circuit during normal operation. In this paper, a flux-coupling-type SFCL (FC-SFCL) based on disconnecting coupling windings for current reduction is considered as a potential scheme to limit fault currents due to its low steady impedance before a fault and higher current-limiting impedance after a fault. Three types of winding methods of the FC-SFCL are introduced and the main parameters are discussed, along with the voltage distribution of the coils. Based on these, the optimal winding structure is determined, a small-scale test prototype is designed and fabricated, and a series of experimental tests is conducted. From the demonstrated experimental results, the FC-SFCL prototype is able to effectively suppress the fault current’s steady value to a lower level, and it has good performance in terms of current reduction. [ABSTRACT FROM AUTHOR]

The Time Transfer by Laser Link (T2L2) project allows for the synchronization of remote ultrastable clocks over intercontinental distances. The principle is derived from the satellite laser ranging technology with a dedicated space equipment designed to record arrival times of laser pulses at the satellite. The space segment was launched in 2008 as a passenger instrument on the ocean altimetry satellite Jason 2. For the first time, we have conducted by the end of 2016 a dedicated time transfer comparison campaign between Global Positioning System and T2L2 over intercontinental distances. The campaign was carried out between two laboratories in Europe and two in China. The campaign has demonstrated a consistency of the time transfer techniques at the 1-ns level, together with the confirmation of a subnanosecond level for continental distances. [ABSTRACT FROM AUTHOR]

The MJ-class superconducting magnetic energy storage system (SMES) is most likely put into commercial utility applications. In China, several 1–100-MJ-class high-temperature superconducting (HTS) SMES projects are undergoing preresearch and conceptual design stage by the government and the power grid corporations, and these SMESs will be developed later. The configurations of commonly used SMES magnets are single-solenoid, multiple-solenoid, and toroidal magnets. Each configuration has its own electromagnetic and mechanical characteristics. In this paper, an optimal design and comparison were carried out to explore the suitable configuration for the MJ-class SMES magnet. As the critical current of the conductor is strongly influenced by the magnetic field and its applied direction, optimization of the HTS coil's configuration is required to minimize the length of the HTS wire. The optimization was conducted with the genetic algorithm and the finite-element method. In order to find the dependence of energy storage capacity on the wire cost, the magnet schemes of seven energy requirements were investigated. An optimal design is performed for the seven stored energies with the three types of magnet configurations. The comparisons between single-solenoid, four-solenoid, and toroidal coils are presented. The magnetic field and stress distribution of the SMES magnet are also depicted. [ABSTRACT FROM PUBLISHER]

In the latest design studies of superconducting magnetic energy storage (SMES) systems, the emphasis was put on the electromagnetic design of SMES magnets without giving due attention to the electrical design with a view to eliminating the overvoltage problem. The power conditioning system (PCS) interfaces with the superconducting magnet and the ac system through an electrical connection. With a pulse-width modulation converter usually adopted in the PCS, the high-frequency overvoltage problem is even more serious. Therefore, electrical design based on voltage distribution analysis and protection measures should be taken into account during the SMES design. This paper first gives transient models of PCS, SMES, and the connecting cable in a systematic view. Later on, a design flow path containing both the electromagnetic design and the electrical design is presented. Adopting the proposed design method, one design plan of a 1 MJ HTS SMES is chosen and conclusions are drawn on its optimal comprehensive design plan. [ABSTRACT FROM PUBLISHER]

The article reports on the April 8, 2012 end of the Environmental Satellite (Envisat) mission. A discussion of range measurements to the satellite that allowed determination of the attitude and the spin period of Envisat during seven months of 2013 which were performed by Satellite Laser Ranging (SLR) stations, and found the spin axis of the satellite is 2.52 meters and causes the meter-scale oscillations of the range measurements between the ground SLR system and the satellite, is presented.