|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Preparation and Properties of Calcium Hexaaluminate Porous Ceramics显示文摘In this paper,calcium hexaaluminate(CA6)porous ceramics were prepared by a foaming method combined with cement solidification molding.The effects of the foaming agent addition on the microstructure and the properties of the ceramics were studied.The results show that the microstructure and the porosity of the ceramics can be controlled by adjusting the addition of the foaming agent.As the addition of the foaming agent increased,the bulk density reduced from 1 g·cm^-3 to 0.35 g·cm^-3,the porosity increased from 70.4%to 89.6%,the compressive strength reduced from 18.46 MPa to 1.66 MPa,and the thermal conductivity at 1000°C decreased from 0.382 W·m^-1·K^-1 to 0.144 W·m^-1·K^-1.It is indicated that calcium hexaaluminate porous ceramics with low density,high strength and low thermal conductivity can be obtained by the foaming method.As the lining material of high temperature electric furnaces,the ceramic has excellent energy saving effect. | WANG Gang ZHANG Qi HAN Jianshen YUAN Bo LI Hongxia | 2020 | China's Refractories2020，29，1： | 2 |
| 2 | Thermal Insulation Materials of Alumina Based Porous Ceramics显示文摘Significant energy saving effects can be made through the improvement of furnace refractories,especially the thermal insulation refractories. In this study,the preparation and the application of different alumina based porous ceramics were briefly introduced. Alumina based porous ceramics were prepared combined foaming method with gelcasting,sol- gel process or cement curing process. The influences of different preparation methods on the sintering shrinkage, porosity, phase composition, microstructure, compressive strength and thermal conductivity were discussed. Alumina based porous ceramics with relatively high strength and low thermal conductivity could be obtained through the above mentioned methods. Compared with the traditional lining materials,about 40% energy could be saved when they were used as the furnace wall. | YUAN Bo CHEN Kuo HAN Jianshen DONG Binbin WANG Gang LI Hongxia | 2015 | China's Refractories2015，24，3： | 2 |
| 3 | Anti-inflammatory Effects of Rebamipide According to Helicobacter pylori Status in Patients with Chronic Erosive Gastritis: A Randomized Sucralfate-Controlled Multicenter Trial in China—STARS Study显示文摘 | Yiqi Du Zhaoshen Li Xianbao Zhan Jie Chen Jun Gao Yanfang Gong Jianlin Ren Liping He Zhijian Zhang Xiaozhong Guo Jianshen Wu Zibin Tian Ruihua Shi Bo Jiang Dianchun Fang Youming Li | 2008 | Digestive Diseases and Sciences2008，，11： | 1 |
| 4 | Formation of multiple water - in - ionic liquid - in - water emulsions显示文摘 | LI JIANSHEN ZHANG JIANLING HAN BUXING | 2012 | Journal of Colloid and Interface Science2012，368，： | 1 |
| 5 | Preparation and Properties of Alumina Heat Insulation Materials with High Purity显示文摘With the wide using of transparent alumina ceramics and synthetic sapphire,the demand of the furnace for them is promoted steadily,and the furnace lining materials are upgrading. Having low thermal conductivity and the same composition with transparent alumina ceramics and sapphire,porous alumina ceramics with high purity are expected to lower the high energy consumption without contamination. In this paper,porous alumina ceramics with porosity of 75. 3%- 81. 9% and impurity less than 0. 1% were prepared by a foaming method combined with gelcasting,using high purity alumina powders as raw materials. By changing the amount of foaming agent and the solid content,the microstructure and properties of porous ceramics were tailored. The compressive strength of the porous ceramics ranged from( 22. 4 ± 2. 5) MPa to( 48. 1 ± 3. 1) MPa,the thermal conductivity of porous ceramics at 1 000 ℃ ranged between 0. 41- 0. 65 W·( m·K)^(-1). | WANG Gang YUAN Bo WU Haibo DONG Binbin HAN Jianshen CHEN Kuo LI Hongxia | 2015 | China's Refractories2015，24，4： | 1 |
| 6 | Composite Model-free Adaptive Predictive Control for Wind Power Generation Based on Full Wind Speed显示文摘Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions. | Shuangxin Wang Jianshen Li Zhongsheng Hou Qingye Meng Meng Li | 2022 | CSEE Journal of Power and Energy Systems2022，8，6： | 0 |
| 7 | Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks显示文摘Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels. | Jinxi Guo Kai Chen Jiehui Liu Yuhao Ma Jie Wu Yaochun Wu Xiaofeng Xue Jianshen Li | 2024 | Computer Modeling in Engineering & Sciences2024，138，3： | 0 |
| 8 | The Researches on Performance and Technology of Strengthened Pure Platinum Wire显示文摘This paper discusses about the purity of strengthened pure platinum wire and the development method of platinum micro wire, in order to solve the difficulties of low tensile strength, easy to break, and low rate of micro wire. And it contrasts some performance of strengthened pure platinum wire and sponge Pt wire. The researches draw a conclusion that the thermoelectric properties of strengthened pure platinum micro wire was in accordance with national standards and satisfied users' requirements. | ZHAI Buying WU Baoan PAN Xiong YANG Hao WANG Yunchun CHEN Xiaojun WANG Jianshen LI Guogang XUE Liqian | 2012 | 贵金属2012，33，A01： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员