Overview of H-Formulation: A Versatile Tool for Modeling Electromagnetics in High-Temperature Superconductor Applications

This paper reviews the modeling of high-temperature superconductors (HTS) using the finiteelement method (FEM) based on the H-formulation of Maxwell\u27s equations. This formulation has become the most popular numerical modeling method for simulating the electromagnetic behavior of HTS, especially thanks to the easiness of implementation in the commercial finite-element program COMSOL Multiphysics. Numerous studies prove that the H-formulation is able to simulate a wide scope of HTS topologies, from simple geometries such as HTS tapes and coils, to more complex HTS devices, up to large superconducting magnets. In this paper, we review the basics of the H-formulation, its evolution from 2D to 3D, its application for calculating critical currents and AC losses as well as magnetization of HTS bulks and tape stacks. We also review the use of the H-formulation for large-scale HTS applications, its use to solve multi-physics problems involving electromagnetic-thermal and electromagnetic-mechanical couplings, and its application to study the dynamic resistance of superconductors and flux pumps

Numerical Modelling of the Dynamic Voltage in HTS Materials under the Action of DC Transport Currents and Different Oscillating Magnetic Fields.

The dynamic voltage is a unique phenomenon of superconducting materials. It arises when the superconductor is carrying a DC transport current and spontaneously in subject to an AC magnetic field. This study excavates the aspects that previous studies have not comprehensively investigated: the dynamic voltage in a DC-carrying superconducting tape exposed to different oscillating AC magnetic fields. First, the fundamental physics of dynamic voltage/flux of superconductors is reviewed and further analysed in detail. We used the superconducting modelling method using the H-formulation merged into the finite-element method (FEM) software, to re-produce the typical dynamic voltage behaviour of a superconducting tape. The modelling was verified by both the analytical and experimental results, in order to precisely prove the reliability of the modelling. Afterwards, the modelling was performed for a DC-carrying superconducting tape under four different oscillating magnetic fields (sine, triangle, sawtooth and square), and their corresponding dynamic voltages and energy losses were analysed and compared. Results show the sinusoidal magnetic field can lead to the optimal combination of reasonable dynamic voltage but relatively lower loss, which is suitable for those superconducting applications requiring dynamic voltage as the energy source, e.g., flux pumps. This article presents novel investigation and analysis of the dynamic voltage in superconducting materials, and both the methodology and results can provide useful information for the future design/analysis of superconducting applications with DC transport currents and AC magnetic fields

An Antioxidant Phytotherapy to Rescue Neuronal Oxidative Stress

Oxidative stress is involved in the pathogenesis of ischemic neuronal injury. A Chinese herbal formula composed of Poria cocos (Chinese name: Fu Ling), Atractylodes macrocephala (Chinese name: Bai Zhu) and Angelica sinensis (Chinese names: Danggui, Dong quai, Donggui; Korean name: Danggwi) (FBD), has been proved to be beneficial in the treatment of cerebral ischemia/reperfusion (I/R).This study was carried out to evaluate the protective effect of FBD against neuronal oxidative stress in vivo and in vitro. Rat I/R were established by middle cerebral artery occlusion (MCAO) for 1 h, followed by 24 h reperfusion. MCAO led to significant depletion in superoxide dismutase and glutathione and rise in lipid peroxidation (LPO) and nitric oxide in brain. The neurological deficit and brain infarction were also significantly elevated by MCAO as compared with sham-operated group. All the brain oxidative stress and damage were significantly attenuated by 7 days pretreatment with the aqueous extract of FBD (250 mg kg−1, p.o.). Moreover, cerebrospinal fluid sampled from FBD-pretreated rats protected PC12 cells against oxidative insult induced by 0.2 mM hydrogen peroxide, in a concentration and time-dependent manner (IC50 10.6%, ET50 1.2 h). However, aqueous extract of FBD just slightly scavenged superoxide anion radical generated in xanthine–xanthine oxidase system (IC50 2.4 mg ml−1) and hydroxyl radical generated in Fenton reaction system (IC50 3.6 mg ml−1). In conclusion, FBD was a distinct antioxidant phytotherapy to rescue neuronal oxidative stress, through blocking LPO, restoring endogenous antioxidant system, but not scavenging free radicals

Comprehensive Assessment of Toxicity in ChatGPT

Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP. The emerging large language models (LLMs), such as ChatGPT, can potentially further accentuate this threat. Previous works have discovered that ChatGPT can generate toxic responses using carefully crafted inputs. However, limited research has been done to systematically examine when ChatGPT generates toxic responses. In this paper, we comprehensively evaluate the toxicity in ChatGPT by utilizing instruction-tuning datasets that closely align with real-world scenarios. Our results show that ChatGPT's toxicity varies based on different properties and settings of the prompts, including tasks, domains, length, and languages. Notably, prompts in creative writing tasks can be 2x more likely than others to elicit toxic responses. Prompting in German and Portuguese can also double the response toxicity. Additionally, we discover that certain deliberately toxic prompts, designed in earlier studies, no longer yield harmful responses. We hope our discoveries can guide model developers to better regulate these AI systems and the users to avoid undesirable outputs

Power flow analysis and optimal locations of resistive type superconducting fault current limiters.

Based on conventional approaches for the integration of resistive-type superconducting fault current limiters (SFCLs) on electric distribution networks, SFCL models largely rely on the insertion of a step or exponential resistance that is determined by a predefined quenching time. In this paper, we expand the scope of the aforementioned models by considering the actual behaviour of an SFCL in terms of the temperature dynamic power-law dependence between the electrical field and the current density, characteristic of high temperature superconductors. Our results are compared to the step-resistance models for the sake of discussion and clarity of the conclusions. Both SFCL models were integrated into a power system model built based on the UK power standard, to study the impact of these protection strategies on the performance of the overall electricity network. As a representative renewable energy source, a 90 MVA wind farm was considered for the simulations. Three fault conditions were simulated, and the figures for the fault current reduction predicted by both fault current limiting models have been compared in terms of multiple current measuring points and allocation strategies. Consequently, we have shown that the incorporation of the E-J characteristics and thermal properties of the superconductor at the simulation level of electric power systems, is crucial for estimations of reliability and determining the optimal locations of resistive type SFCLs in distributed power networks. Our results may help decision making by distribution network operators regarding investment and promotion of SFCL technologies, as it is possible to determine the maximum number of SFCLs necessary to protect against different fault conditions at multiple locations.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), project NMZF / 064. X. Zhang acknowledges a grant from the China Scholarship Council (No. 201408060080).This is the final version of the article. It first appeared from Springer via https://doi.org/10.1186/s40064-016-3649-

GINA-3D: Learning to Generate Implicit Neural Assets in the Wild

Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving. However, manually creating or re-creating real-world-like environments is difficult, expensive, and not scalable. Recent generative model techniques have shown promising progress to address such challenges by learning 3D assets using only plentiful 2D images -- but still suffer limitations as they leverage either human-curated image datasets or renderings from manually-created synthetic 3D environments. In this paper, we introduce GINA-3D, a generative model that uses real-world driving data from camera and LiDAR sensors to create realistic 3D implicit neural assets of diverse vehicles and pedestrians. Compared to the existing image datasets, the real-world driving setting poses new challenges due to occlusions, lighting-variations and long-tail distributions. GINA-3D tackles these challenges by decoupling representation learning and generative modeling into two stages with a learned tri-plane latent structure, inspired by recent advances in generative modeling of images. To evaluate our approach, we construct a large-scale object-centric dataset containing over 520K images of vehicles and pedestrians from the Waymo Open Dataset, and a new set of 80K images of long-tail instances such as construction equipment, garbage trucks, and cable cars. We compare our model with existing approaches and demonstrate that it achieves state-of-the-art performance in quality and diversity for both generated images and geometries.Comment: Accepted by CVPR 202

Dynamic Modeling of Surface-Mounted Permanent Magnet Motors Considering Saturation

This paper developed a dynamic hybrid field model (DHFM) for surface-mounted permanent magnet (SPM) motors coupled with external drive circuit considering motor saturation. In the proposed model, the iron saturation of SPM motor is equivalently replaced by the saturation current in the slot opening and therefore the analytical solution of the air-gap field is determined. Based on the air-gap field, the total flux linkage and electromagnetic torque is calculated. The instantaneous inductance is also derived from DHFM using frozen permeability method. The instantaneous back-EMF is determined by the flux linkage produced by PM separating from the total flux linkage. Hence, according to the circuit model and mechanical model, the winding current at next step can be obtained. The proposed model is more efficient in terms of computation and its accuracy is validated by FEM results