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Anisotropic thermally conductive composites are very critical for precise thermal management of electronic devices. In this work, in order to prepare a composite with significant anisotropic thermal conductivity, polyamide 12/styrene–acrylic copolymer–boron nitride (PA12/SA–BN) composites with macro and micro double anisotropic structures were fabricated successfully using 3D printing and micro-shear methods. The morphologies and thermally conductive properties of composites were systematically characterized via SEM, XRD, and the laser flash method. Experimental results indicate that the through-plane thermal conductivity of the composite is 4.2 W/(m·K) with only 21.4 wt% BN, which is five times higher than that of the composite with randomly oriented BN. Simulation results show that the macro-anisotropic structure of the composite (caused by the selective distribution of BN) as well as the micro-anisotropic structure (caused by the orientation structure of BN) both play critical roles in spreading heat along the specified direction. Therefore, as-obtained composites with double anisotropic structures possess great potential for the application inefficient and controllable thermal management in various fields.

The process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect different flow patterns after two-dimensional section reconstruction in closed filling pipeline based on ECT (electrical capacitance tomography) visualization method. Slurry flow in pipeline is regarded as a two-phase flow, and the multishape distribution was reconstructed into images by ECT and intelligently recognized by broad learning system (BLS) algorithm. BLS is a feedforward neural network with few optimization parameters and fast training speed. In this paper, three features of two-phase sample images, the number of regional blocks, the roundness of regional blocks, and barycenter of regional blocks, are combined with network structure of BLS to recognize different flow patterns. Through the simulation, the recognition accuracy of two-phase fillback image is more than 99%. This conclusion indicates the effectiveness of BLS to predict different two-phase flow patterns; it also provides a new solution for the pattern recognition of the flow pattern in the mining filling pipeline.