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The design of a light source for a molten salt receiver experimental system has become challenging owing to the complex heating characteristics of the half-circumference surface during operation. Electromagnetic induction heating is an innovative technology that can replicate a half-circle heating scenario. However, its feasibility must be verified. In this study, we constructed a single receiver tube experimental system using induction heaters and developed a 3D numerical model coupling electromagnetic field to analyze the tube temperature distribution and molten salt temperature rise during preheating and salt circulation, considering various parameters. The results indicated that the numerical simulation agreed well with the experimental results, and the induction heater successfully reproduced the half circumference heating scenario. During preheating, a lower heat flux and higher wind speed result in a more uniform temperature distribution along the circumference of the tube wall, facilitating comprehensive preheating. During salt circulation, the heat flux and inlet salt mass flow significantly affected the temperature of the tube wall but had a relatively small effect on the back-side wall. Wind speed had the opposite effect, which was related to the arrangement of the experimental site. A higher heat flux, lower wind speed, and higher inlet salt mass flow led to a higher temperature increase in the molten salt. [ABSTRACT FROM AUTHOR]

With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability. [ABSTRACT FROM AUTHOR]