Preparation and Microstructure of Machinable Al\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e3\u3c/sub\u3e/Mica Composite by Ball Milling and Hot-Press Sintering

A machinable α-Al2O3/mica composite was prepared by hot-press sintering. In this experiment, a mica-contained glass ceramic in the MgO-Al2O3-SiO2-F glassy system was employed and the base glass powders were obtained by traditional melting-quenched method. Then, α-Al2O3 milling swarf was introduced by medium α-alumina milling ball to the glass powders. The test results indicate that the composites consist of mica crystal and mullite crystal, which are precipitated in the base glass. The α-Al2O3 shows an irregular polygon, which is inlayed in the base material. With the decrease of size of the base glass powders, the boundaries of composites among the sintered powders gradually vanish. The mica crystals in the composite also show an interlocking characteristic, which is a prerequisite of mica-contained glass ceramics with good machinability. Under different pressures, the tendency of preferred orientation is decreased with the reduction in grain size of glass powders, and the microstructure is proved to be consistent, significantly decreasing the composite’s hardness. Therefore, the machinability of the composite is improved

Deep Intraspecific Divergence in the Endemic Herb Lancea tibetica (Mazaceae) Distributed Over the Qinghai-Tibetan Plateau

Qinghai-Tibetan Plateau (QTP) is an important biodiversity hub, which is very sensitive to climate change. Here in this study, we investigated genetic diversity and past population dynamics of Lancea tibetica (Mazaceae), an endemic herb to QTP and adjacent highlands. We sequenced chloroplast and nuclear ribosomal DNA fragments for 429 individuals, collected from 29 localities, covering their major distribution range at the QTP. A total of 19 chloroplast haplotypes and 13 nuclear genotypes in two well-differentiated lineages, corresponding to populations into two groups isolated by Tanggula and Bayangela Mountains. Meanwhile, significant phylogeographical structure was detected among sampling range of L. tibetica, and 61.50% of genetic variations was partitioned between groups. Gene flow across the whole region appears to be restricted by high mountains, suggesting a significant role of geography in the genetic differences between the two groups. Divergence time between the two lineages dated to 8.63 million years ago, which corresponded to the uplifting of QTP during the late Miocene and Pliocene. Ecological differences were found between both the lineages represent species-specific characteristics, sufficient to keep the lineages separated to a high degree. The simulated distribution from the last interglacial period to the current period showed that the distribution of L. tibetica experienced shrinkage and expansion. Climate changes during the Pleistocene glacial-interglacial cycles had a dramatic effect on L. tibetica distribution ranges. Multiple refugia of L. tibetica might have remained during the species history, to south of the Tanggula and north of Bayangela Mountains, both appeared as topological barrier and contributed to restricting gene flow between the two lineages. Together, geographic isolation and climatic factors have played a fundamental role in promoting diversification and evolution of L. tibetica

Capacitor Voltage Balancing Control of MMC Sub-Module Based on Neural Network Prediction

The issue of sub-module (SM) capacitor voltage unbalance is a hot topic in the current research into the modular multilevel converter (MMC). An excellent strategy comprises mitigating the SM capacitor voltage imbalance by adjusting the SM on time. The traditional capacitor voltage balancing control regulates the speed to maintain accuracy. A unique SM capacitor voltage balancing control strategy is presented in this paper and is based on conventional capacitor voltage balance management and neural network prediction. Firstly, the SM capacitor voltage and arm current are speculated by operating the time series forecasting technique in real time, considering the dynamic changes in the SM capacitor voltage and arm current. Secondly, the SM capacitor voltage distinction between the actual and theoretical value is determined, and a deviation’s mixed Gaussian distribution is established to estimate its compensation voltage. Thirdly, the SM triggering sequence is anticipated by using the neural network along with the pilot values of the SM capacitor voltage, arm current, and the offset compensation value, and the control is executed. Finally, a three-phase, six-leg, eight-module, nine-level MMC model is built to verify the feasibility of the suggested approach

THE INFLUENCES OF FLUORIDES ON THE TRANSFORMATION OF α -ALUMINA CRYSTALS

Influence of addition of aluminum fluoride (AlF₃) and ammonium fluoride (NH₄F) on transformation of α-Al₂O₃ from boehmite was investigated. Transformation of α-Al₂O₃ occurred at about 1050°C and 950°C, when boehmite contained AlF₃ and NH₄F additives respectively, which was 150°C and 300°C lower than that without any additives. The formed α-Al₂O₃ consisted of clear, hexagonal plate-like particles about 2-3 μm in diameter and 200 nm in thickness. However, the formed hexagonal platelet particles gradually became blunt at edges and decreased in sizes at the higher calcinating temperatures. The transfer of α-Al₂O₃ morphology was due to the diminution and disappearance of fluorine. The fluoride coated on the surfaces were firstly decomposed and then fluorine entered the lattices of crystals was volatilized. The fluoride with tiny particles attached on the surface had a same chemical bonding of F-Al-O with that in the lattices of crystals

Advance on Al2O3 Particulates Reinforced Aluminum Metal Matrix Composites (Al-MMCs) Manufactured by the Power Metallurgy(PM) Methods- Improved PM Techniques

Aluminum metal matrix composites (Al-MMCs) with Al2O3 particulates as reinforcement fabricated by the power metallurgy (PM) methods have gained much attention due to their unique characteristics of the wide range of Al2O3 particles addition, easy-operating process and effectiveness. The improved PM techniques, such as the high energy ball milling, powder extruder and high pressure torsion were applied to further strengthening the properties or/and diminishing the agglomeration of strength particles. The formation of liquid phase assisted densification of compacts to promote the sintering of composites. Complex design of Al2O3 particles with other particles was another efficient method to tailor the properties of Al-MMCs

Advance on Al

Aluminum metal matrix composites (Al-MMCs) with Al2O3 particulates as reinforcement fabricated by the power metallurgy (PM) methods have gained much attention due to their unique characteristics of the wide range of Al2O3 particles addition, easy-operating process and effectiveness. The improved PM techniques, such as the high energy ball milling, powder extruder and high pressure torsion were applied to further strengthening the properties or/and diminishing the agglomeration of strength particles. The formation of liquid phase assisted densification of compacts to promote the sintering of composites. Complex design of Al2O3 particles with other particles was another efficient method to tailor the properties of Al-MMCs

Evaluation on power information data asset management system based on BP neural network

With the popularization and development of the current power system, power information data asset management plays a crucial role in modern power systems. However, traditional management methods have some problems, such as low information processing efficiency, low prediction accuracy, and insufficient decision support. In order to better promote its development and achieve efficient management of power information data assets, this article aimed to use BP neural network (Back Propagation Neural Network) to design a power information data asset management system, achieving efficient processing and accurate analysis of power information data. In the article, data preprocessing was achieved through data separation, data cleaning, and data normalization processing. Compared with the traditional power asset management system, it has better management efficiency, lightens the difficulty of asset management and reduces the error rate. In this paper, the power information data is modeled and trained by BP neural network modeling, and the performance index is minimized by error back propagation, and the optimized BP neural network model is integrated into the power information data asset management system to realize data processing and decision support. In order to verify the performance of the power information data asset management system based on BP neural network, this paper tested its system performance. The research results showed that the average processing accuracy of the system under this method for basic data in 10 test cases reached 91.467 %, and the average rationality of decision support reached 89.6 %. The average processing accuracy of real-time data reached 91.625 %, and the average rationality of decision support reached 90.25 %. The average processing accuracy of application data reached 90.675 %, and the average rationality of decision support reached 90.2 %. The results showed that the system under this method has higher accuracy in data processing and can better achieve decision support. This study highlighted the important impact of BP neural networks on data processing, data prediction, decision support, and data security in power information data asset management systems, providing more possibilities for achieving efficient processing and accurate analysis of power information data

Study on Properties of Waste Concrete Powder by Thermal Treatment and Application in Mortar

Waste concrete must be crushed, screened, and ground in order to produce high-quality recycled aggregate. In this treatment process, 15–30% waste concrete powder (<0.125 mm) can be generated. Hydration activity and the reuse of waste concrete powders (WCPs) were studied in this work, and the results illustrated that the particle size changed after a series of thermal treatments at temperatures from 400 ℃ to 800 ℃. The particle size of waste concrete powder decreased by 700 ℃ thermal treatment, and by 600 ℃ thermal treatment, it increased. More active elements appeared in WCP heated by 800 ℃. Nevertheless, the activity index (AI) of WCP, measured by the ratio of mechanical strengths between mortar with a 30% replacement of the cement with WCP and normal mortar without WCP, indicated that the WCP by 700 ℃ thermal treatment had an optimal AI value, which meant WCP treated at 700 ℃ could be used in mortar or concrete as an admixture