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Plasma vertical displacement control is essential for the stable operation of tokamak devices. The traditional plasma vertical displacement calculation method is not suitable for balancing speed and accuracy simultaneously, which is necessary for real-time feedback control. In this study, neural networks are used to rapidly detect vertical displacement recognition. Based on a fully connected neural network, the vertical displacement calculation model is trained and tested using magnetic data of approximately 2000 shots. To compare the effects of different inputs on vertical displacement calculation, different magnetic measurement diagnostic signals are used to train and test the model. Compared with a full magnetic measurement dataset, 39 magnetic measurement signals (38 magnetic probes and plasma current) show better accuracy with mean square error <0.0005. The model is tested using historical experimental data, and it demonstrates accurate vertical displacement calculation even in the case of a vertical displacement event. In general, neural network algorithm has great application potential in vertical displacement calculation.
Piezoelectric semiconductors (PS) nanofibers, which simultaneously exhibit piezoelectricity and unique electric conductive behavior, have huge applications in sensors, energy harvesters, and piezoelectric field effect transistors. Electromechanical fields and charge carrier in PS nanofibers can be effectively controlled by a mechanical force. One-dimensional linear equations for PS nanofibers, which are suitable for small axial force and small electron concentration perturbation, are presented. Analytical expressions for the electromechanical fields and electron concentration in the fiber are obtained. Numerical results show that the electromechanical fields near the two ends are sensitive to the initial electron concentration and the applied axial force.
The mechanical properties of intermetallic compounds in the Au–Sn system were investigated by nanoindentation. Measurements of hardness and elastic modulus were obtained for all of the confirmed room-temperature intermetallics in this system as well as the β phase (8 at.% Sn) and AuSn4. Overall, it was found that the Au–Sn compounds have lower hardness and stiffness than common Cu–Sn compounds found in solder joints. This finding is in contrast to common knowledge of “Au embrittlement” due to the formation of either AuSn4 or (Au,Ni)Sn4 intermetallic compounds. This difference in understanding of mechanical properties of these phases and the resulting joint strength is discussed in terms of reliability and possible failure mechanisms related to interface strength or microstructural effects. Indentation creep measurements performed on Au5Sn, Au–Sn eutectic (29 at.% Sn) and AuSn indicate that these alloys are significantly more creep resistant than common soft solders, in keeping with typical observations of actual joint performance.
Sol - gel technology is used to prepare sub-micron to micron size particles of Chromium (IV) doped materials. These materials show strong luminescence, as single crystals, in the 1-2 μm range. Such materials include; Cr (IV): (Ca2GeO4, Mg2SiO4, LiScGeO4). The optically active particles are dispersed into glassy matrixes to form optical composites. Ongoing research is aimed at, a) reducing the size and the size distribution of the optically active particles, and b) finding suitable matrix materials.
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