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Gas‐insulated lines (GIL) are widely used due to their many advantages in power plants and substations. The security and reliability of GIL is achieved by continues testing and monitoring. The main fault in the GIL compartments is overheating, which is rarely to occur, nonetheless, it cannot be ignored. In the present study, GIL steady‐state thermal behavior was investigated. For this reason, a multiphysics finite element model (FEM) was built to analyze the magneto‐hydro‐thermal characteristics of the GIL insulation gas. The simulation coupling includes the electromagnetic field, flow field and thermal heat transfer field. SF6/N2 and CF3I/N2 mixtures were studied and the influence of different gas properties on the magnetic flux density, heat distribution and flow velocity had been investigated. The results were compared to an experimental setup to validate the model calculations. Temperature rise tests have been conducted on the GIL inner and outer shell. The measurements were in good agreement with the simulation results. Finally, the presented work is also to serve as a good reference in future design and optimization of GIL structure. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Traditional modeling and optimization methods for high voltage equipment, specially, circuit breaker components and nozzles, require manual testing and improvement of a large number of parameters, and the efficiency is relatively low. The strong processing power of artificial intelligence technology in the identification and prediction of complex systems is an effective solution in such case. This paper presents the optimization approach of image recognition combined with deep learning for circuit breaker nozzle. The nozzle model was conducted using the high Mach flow model in a commercial software (COMSOL Multiphysics 5.4) to study the gas flow state and behavior during cold flow, and obtains Shock wave image recognition model of the nozzle chamber based on the convolutional neural network (CNN) method of multiscale layered features. On the basis of effective image recognition, combined with deep learning Convolutional Recurrent Neural Network (CRNN), the image sequence under different parameters is sent to the convolutional layer for feature extraction, and then the feature map is input into the loop. The prediction sequence is obtained through the layer, and finally the relationship between the kinematic parameters of the nozzle and the internal gas flow state is predicted through the prediction layer. Results indicated, according to the prediction of CRNN, the range of the throat length should be between 7 and 13 mm and the angle should be between 8 ∼ 15°. The presented method could be also used for similar materials and components, with certain universality. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Frictional heating during earthquake rupture reveals important information on earthquake mechanisms and energy dissipation. The amount of annealing varies widely and is, as yet, poorly constrained. Here we use magnetic susceptibility versus temperature measurements during cycling to increasingly elevated temperatures to constrain the maximum temperature a slip zone has experienced. The case study comprises sheared clay cored from the Japan Trench subduction plate-boundary fault zone (décollement), which accommodated the large slip of the 2011 Mw 9.0 Tohoku-oki earthquake. The décollement was cored during the Integrated Ocean Drilling Program (IODP) Expedition 343, the Japan Trench Fast Drilling Project (JFAST). Heating signatures with estimated maximum temperatures ranging from ∼300 to over 500 °C are determined close to the multiple slip surfaces within the décollement. Since it is impossible to tie a specific slip surface to a certain earthquake, thermal evidence for the cumulative effect of several earthquakes is unveiled. This as yet preliminary rock magnetic 'geothermometer' would be a useful tool to detect seismic heating along faults that experienced medium temperature rise, a range which is difficult to assess with other approaches. [ABSTRACT FROM AUTHOR]