Finite element implementation of a thermo-damage-viscoelastic constitutive model for hydroxyl-terminated polybutadiene composite propellant

A thermo-damage-viscoelastic model for hydroxyl-terminated polybutadiene (HTPB) composite propellant with consideration for the effect of temperature was implemented in ABAQUS. The damage evolution law of the model has the same form as the crack growth equation for viscoelastic materials, and only a single damage variable \(S\) is considered. The HTPB propellant was considered as an isotropic material, and the deviatoric and volumetric strain-stress relations are decoupled and described by the bulk and shear relaxation moduli, respectively. The stress update equations were expressed by the principal stresses \(\sigma_{ii}^{R}\) and the rotation tensor \(M\), the Jacobian matrix in the global coordinate system \(J_{ijkl}\) was obtained according to the fourth-order tensor transformation rules. Two models having complex stress states were used to verify the accuracy of the constitutive model. The test results showed good agreement with the strain responses of characteristic points measured by a contactless optical deformation test system, which illustrates that the thermo-damage-viscoelastic model perform well at describing the mechanical properties of an HTPB propellant.     

Effect of Sintering Temperature on Magnetic and Electrical Properties of GdBaCo<Subscript>2</Subscript><Emphasis Type="Italic">O</Emphasis><Subscript>5 + <Emphasis Type="Italic">δ</Emphasis></Subscript>

In this study, we report the results of an investigation into the sintering temperature dependence of magnetic and transport properties for GdBaCo₂ O _{5 + δ} synthesized through a sol-gel method. The lowering of sintering temperature leads to the increase of oxygen content and the reduction of grain size. The increase of oxygen content results in the enhancement of magnetic interactions and the weakening of Coulomb repulsion effect, while the reduction of grain size improves the magnetoresistance effect. Metal-insulator transition accompanied with spin-state transition is observed in all samples.     

Comparison of gas sensing properties based on hollow and porous α-Fe<Subscript>2</Subscript>O<Subscript>3</Subscript> nanotubes

Hollow and porous α-Fe₂O₃ nanotubes were successfully synthesized by single nozzle electrospinning method followed by annealing treatment. The crystal structures and morphologies of the as-prepared materials were characterized by X-ray diffraction and scanning electron microscopy, respectively. The as-prepared materials were applied to construct gas sensor devices which gas sensing properties were further investigated. The obtained results revealed that porous α-Fe₂O₃ nanotube gas sensors exhibit a markedly enhanced gas sensing performance compared with hollow α-Fe₂O₃ nanotube gas sensors, which was about three times higher to 100 ppm acetone at 240 °C. Interestingly, hollow and porous α-Fe₂O₃ nanotube gas sensors both showed fast response–recovery time and good selectivity, but the porous ones possessed the shorter recovery time. The improved properties could be attributed to the unique morphology of porous nanotubes. Thus, further improvement of performance in metal-oxide-semiconductors materials could be realized by preparation the unique porous structures of nanotubes. Moreover, it is expected that porous metal-oxide-semiconductors nanotubes could be further design as promising candidates for gas sensing materials.     

Graphene oxide-decorated Fe<Subscript>2</Subscript>(MoO<Subscript>4</Subscript>)<Subscript>3</Subscript> microflowers as a promising anode for lithium and sodium storage

Mixed transition metal oxides (MTMOs) have received intensive attention as promising anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). In this work, we demonstrate a facile one-step water-bath method for the preparation of graphene oxide (GO) decorated Fe₂(MoO₄)₃ (FMO) microflower composite (FMO/GO), in which the FMO is constructed by numerous nanosheets. The resulting FMO/GO exhibits excellent electrochemical performances in both LIBs and SIBs. As the anode material for LIBs, the FMO/GO delivers a high capacity of 1,220 mAh·g^–1 at 200 mA·g^–1 after 50 cycles and a capacity of 685 mAh·g^–1 at a high current density of 10 A·g^–1. As the anode material for SIBs, the FMO/GO shows an initial discharge capacity of 571 mAh·g^–1 at 100 mA·g^–1, maintaining a discharge capacity of 307 mAh·g^–1 after 100 cycles. The promising performance is attributed to the good electrical transport from the intimate contact between FMO and graphene oxide. This work indicates that the FMO/GO composite is a promising anode for high-performance lithium and sodium storage.

Open image in new window

     

一款基于APA300的创新型FPGA实验板

基于Actel公司Flash型FPGA芯片APA300设计并实现一款创新型FPGA实验板.该实验板包括2片互通的APA300、丰富的外围设备和常用外部设备端口,还提供与外部电路连接的扩展接口,能够开展多种创新性实验,并可作为科学预研的平台.在详细介绍了实验板各部分的实现原理和电路连接图后,给出了实验板的两个典型创新性实验示例:协处理器密码机和MP3播放器.     

Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.     

Content-based image authentication: current status, issues, and challenges

With today’s global digital environment, the Internet is readily accessible anytime from everywhere, so does the digital image manipulation software; thus, digital data is easy to be tampered without notice. Under this circumstance, integrity verification has become an important issue in the digital world. The aim of this paper is to present an in-depth review and analysis on the methods of detecting image tampering. We introduce the notion of content-based image authentication and the features required to design an effective authentication scheme. We review major algorithms and frequently used security mechanisms found in the open literature. We also analyze and discuss the performance trade-offs and related security issues among existing technologies.