Reduction of Eddy Current Loss of Permanent-Magnet Machines with Fractional Slot Concentrated Windings

Abstract-Fractional slots concentrated windings (FSCWs) are characterized with high magnetic motive force (MMF) harmonics which results in undesirable effects on permanent-magnet (PM) machines. A new design technique is reported in this paper in order to simultaneously reduce the sub-and high MMF harmonics. By using multiple layer windings and different turns per coil, a new 18-teeth/10-poles FSCWs PM machine is designed. Then, this machine is evaluated as compared with a conventional 12-teeth/10-poles FSCWs PM machine. Both machines are designed under the same electrical and geometrical constrains. The obtained results verify the high performances of the newly designed machine. Due to the adopted new winding type, the proposed design can effectively reduce eddy current loss in PMs as compared with the conventional design

Predicting Transition Temperature of Superconductors with Graph Neural Networks

Predicting high temperature superconductors has long been a great challenge. The difficulty lies in how to predict the transition temperature (Tc) of superconductors. Although recent progress in material informatics has led to a number of machine learning models predicting Tc, prevailing models have not shown adequate generalization ability and physical rationality to find new high temperature superconductors, yet. In this work, a bond sensitive graph neural network (BSGNN) was developed to predict the Tc of various superconductors. In BSGNN, communicative message passing and graph attention methods were utilized to enhance the model's ability to process bonding and interaction information in the crystal lattice, which is crucial for the superconductivity. Consequently, our results revealed the relevance between chemical bond attributes and Tc. It indicates that shorter bond length is favored by high Tc. Meanwhile, some specific chemical elements that have relatively large van der Waals radius is favored by high Tc. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc

High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error

The Orbital Angular Momentum (OAM)-based vortex radar has drawn increasing attention because of its potential for high-resolution imaging. The vortex radar high resolution imaging with limited OAM modes is commonly solved by sparse recovery, in which the prior knowledge of the imaging model needs to be known precisely. However, the inevitable phase error in the system results in imaging model mismatch and deteriorates the imaging performance considerably. To address this problem, the vortex radar imaging model with phase error is established for the first time in this work. Meanwhile, a two-step self-calibration imaging method for vortex radar is proposed to directly estimate the phase error. In the first step, a sparsity-driven algorithm is developed to promote sparsity and improve imaging performance. In the second step, a self-calibration operation is performed to directly compensate for the phase error. By alternately reconstructing the targets and estimating the phase error, the proposed method can reconstruct the target with high imaging quality and effectively compensate for the phase error. Simulation results demonstrate the advantages of the proposed method in enhancing the imaging quality and improving the phase error estimation performance

The elastoplastic numerical model and verification by macroindentation experiment of femoral head

For internal fixation of proximal femoral fractures, a screw is commonly placed into the femoral head; therefore, mechanical matching of the femoral head and screw is important. This article proposes an elastoplastic numerical model of the femoral head that takes nonlinear deformation and cancellous bone heterogeneity into account. Force-depth curves from finite element analysis based on the model were compared with those from macroindentation experiments. The maximum difference between the indentation depth shown by the finite element model and that found with macroindentation testing was 5.9%, which demonstrates that the model is valid

Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods

Fine Detection and Analysis of Hidden Karst in Wellsite with Quasi-Three-Dimensional TDEM Based on Lateral Constraint

Considering that karst caves, underground rivers, and dissolution fractures in shallow carbonate formation in the Sichuan Basin are extremely developed, leakage, failure and plugging difficulties are easy to occur in the drilling process. The TDEM was used to carry out the exploration of hidden karst geological bodies in well QM2, and the quasi-three-dimensional inversion based on lateral constrain was used to invert the TDEM data. Three NW trending anomalous bands were identified in the lower Triassic Jialingjiang Formation within the range of drilling, consisting of seven relatively low-resistivity anomalous zones. Under the guidance of TDEM quasi-three-dimensional inversion resistivity data, the low-resistivity karst development area is avoided, and the specific drilling location of well QM2 is determined. No karst cave and underground river were drilled in the later drilling process of well QM2, as well as no instability phenomenon occurred. It indicates that the TDEM detection results are consistent with the actual drilling, and the quasi-three-dimensional TDEM inversion interpretation data based on lateral constraints is reliable and can accurately detect the buried karst in the wellsite

Effects of Heat Stress and Exogenous Salicylic Acid on Secondary Metabolites Biosynthesis in Pleurotus ostreatus (Jacq.) P. Kumm

Pleurotus ostreatus (Jacq.) P. Kumm has high medicinal value, but few studies exist on regulating secondary metabolite biosynthesis. Environmental factors play a substantial role in the accumulation of microbial secondary metabolites. In this study, the effects of heat stress (24 h) and salicylic acid (0.05 mmol/L) treatment on the secondary metabolism of P. ostreatus were analyzed by metabolome, transcriptome, and gene differential expression analysis. Metabolome and transcriptome analyses showed that salicylic acid significantly increased the accumulation of antibiotics and polyketones, while heat stress increased the accumulation of flavonoids, polyketones, terpenoids, and polysaccharides. The content and the biosynthetic genes expression of heparin were markedly increased by heat stress, and the former was increased by 4565.54-fold. This study provides a reference for future studies on secondary metabolite accumulation in edible fungi