With lightweight, multifunctional, and designable characteristics, porous/lattice structures have started to be used in aerospace applications. Porous/lattice structures applied in the thermal management technology of aerospace vehicles have attracted much attention. In the past few years, many related numerical and experimental investigations on flow, heat transfer, modelling methodology, and manufacturing technology of porous/lattice structures applied in thermal management systems have been widely conducted. This paper lists the investigations and applications of porous/lattice structures applied in thermal management technology from two aspects, i.e., heat transfer enhancement by porous/lattice structures and transpiration cooling. In addition, future developments and challenges based on the previous investigations are analyzed and summarized. With the higher requirements of thermal protection for aerospace applications in the future, thermal management technology based on porous/lattice structures shows good prospects.
With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type error rate is reduced by 5.0%, which verifies the superiority and effectiveness of k-means-SVM model applied to enterprise credit risk assessment under supply chain finance mode.
The use of the Maisotsenko cycle (M-Cycle) in traditional wet cooling towers (TWCTs) has the potential to reduce the costs of electricity generation by cooling water below the inlet air’s wet-bulb temperature. TWCTs cannot provide sufficient cooling capacity for the increasing demand for cooling energy in the power and industrial sectors—especially in hot and wet climates. Due to this fact, an experimental system of an M-Cycle cooling tower (MCT) with parallel counter-flow arrangement fills was constructed in order to provide perspective on the optimal length of dry channels (ldry), thermal performance under different conditions, and pressure drops of the MCT. Results showed that the optimal value of ldry was 2.4 m, and the maximum wet-bulb effectiveness was up to 180%. In addition, the impact of air velocity in wet channels on the pressure drops of the novel fills was also summarized. This study confirms the great potential of using the M-Cycle in TWCTs, and provides a guideline for the industrial application and performance improvement of MCTs.
With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type error rate is reduced by 5.0%, which verifies the superiority and effectiveness of k-means-SVM model applied to enterprise credit risk assessment under supply chain finance mode.
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