Miniature Mobile Sensor Platforms for Condition Monitoring of Structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability.     

Hexane activation over vanadium modified zeolite ZSM-5

The influence on the form of ZSM-5, vanadium content and the elimination of the exterior surface, on the activity and selectivity of n-hexane oxidation was studied using a fixed bed reactor. Blank reactor studies (carborundum packed reactor) showed no conversion below 450 °C with the highest conversion (8 %) at 500 °C. The dominant products were found to be carbon oxides (Sel./% = 90) with minor selectivities to the hexene isomers (7 %) with the remainder being cracked products, THF and benzene. H-ZSM-5 with different SiO₂/Al₂O₃ ratios (100 and 320) and Na-ZSM-5 (SiO₂/Al₂O₃ ratio of 100) were tested under non-oxidative conditions. As the ratio of the SiO₂/Al₂O₃ increased the aluminium content decreases and so too does the cracking ability of the zeolite (i.e. yield of cracked products dropped from 36 to 8 %). However, the use of the Na- form of the ZSM-5 zeolite completely eliminated acid cracking and therefore this system was further investigated. Na-V-ZSM-5 (~1 % loading) was synthesized using the solid state ion-exchange method. Time on stream experiments (fresh batch of catalyst for each experiment with sampling at the same time for a period of 24 h) were conducted and temperature (350, 400 and 450 °C), contact time (0.5, 0.8, 1.1 and 1.5 s) and fuel/air ratios (0.7, 1.3 and 2) were varied. The optimum conditions (Conv./% = 39) for terminal functionalised products were found to be at 400 °C at a contact time of 1.1 s and a fuel air ratio of 1.3. With the lower fuel air ratio of 0.7 (oxygen rich conditions), hexanal formation was favoured.     

热处理中气体淬火采用的物理和工艺基础

与液体淬火介质相比,气体在生态和经济性方面有优势,为获得所需的质量,必须仔细选择和调整淬火气体和工厂的工艺,理论上讲,轻气体和“冷却室”将带来许多好处。     

Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method. Then, the three components are used as input factors for the BP neural network. A BP neural network prediction model is constructed based on genetic algorithm optimization. The crossover operator and mutation operator in the adaptive genetic algorithm are improved. Finally, the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper. The results demonstrate that, the model can solve the local optimization problem of the BP neural network model. The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy. Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method. This method can provide more reliable forecast information for the blooming period, which can provide decision-making reference for improving the development of tourism industry.     

Effects of open-circuit operation on membrane and catalyst layer degradation in proton exchange membrane fuel cells

Durability issues have been attracting a great deal of attention in hydrogen/air proton exchange membrane (PEM) fuel cell research. In the present work, membrane electrode assembly (MEA) degradation under open circuit (OC) conditions was carried out for more than 250 h. By means of several on-line electrochemical measurements, the performance of the fuel cell was analysed at different times during the degradation process. The results indicate that structural changes in the PEM and catalyst layers (CLs) are the main reasons for the decline in performance during OC operation. The results also show that degradation due to platinum oxidation or catalyst contamination can be partially recovered by a subsequent potential cycling process, whereas the same cycling process cannot recover the membrane degradation.