On the thermal conductivity of bimodal SiC/A356 composites fabricated via powder metallurgy route

The present study investigates the thermal conductivity of bimodal SiC particulate distribution in aluminum matrix composites fabricated via powder metallurgy route. The effects of the SiC_p reinforcement size distribution and processing parameters such as sintering time and temperature on the thermal conductivity have been examined. The Box–Behnken experimental array was employed to identify the effects of selected variables on the thermal conductivity of the composite. A reasonable augmentation in the thermal conductivity was observed with an increase in sintering time and %volume fraction of fine SiC particulates. It has been demonstrated that the matrix doped with fine SiC particulates (37?µm) occupied interstitial positions and formed continuous SiC–matrix network resulting in minimizing the micropores that contributed for good thermal conductivity, that is, 235?W/mK. Scanning electron microscopy (SEM) and x-ray diffraction (XRD) were conducted to evaluate the microstructure architecture and interfacial phase formation.     

基于弥散燃料颗粒开裂的金属基体裂纹特征模型

金属基弥散燃料元件在特殊工况下会发生表面起泡失效。燃料颗粒开裂是金属基体开裂的前提条件，只有当金属基体开裂后元件才会发生表面起泡。燃料颗粒开裂后，裂纹宽度和塑性区长度等裂纹特征决定了金属基体开裂行为。基于弹塑性断裂力学和应力平衡条件，建立了基于弥散燃料颗粒开裂的金属基体裂纹特征模型。计算结果表明：裂纹张开位移随退火温度和燃耗深度的升高而增加；裂纹尖端塑性区长度主要与退火温度相关。裂纹张开位移和塑性区长度的计算结果与实验数据均符合较好，验证了金属基体裂纹特征模型的有效性。     

Robust predictive extended state observer for a class of nonlinear systems with time-varying input delay

ABSTRACT

This paper deals with asymptotic stabilisation of a class of nonlinear input-delayed systems via dynamic output feedback in the presence of disturbances. The proposed strategy has the structure of an observer-based control law, in which the observer estimates and predicts both the plant state and the external disturbance. A nominal delay value is assumed to be known and stability conditions in terms of linear matrix inequalities are derived for fast-varying delay uncertainties. Asymptotic stability is achieved if the disturbance or the time delay is constant. The controller design problem is also addressed and a numerical example with an unstable system is provided to illustrate the usefulness of the proposed strategy.     

Evaluation of dynamic modulus measurement for C/C-SiC composites at different temperatures

The determination of elastic properties at application temperature is fundamental for the design of fibre reinforced ceramic composite components. An attractive method to characterize the flexural modulus at room and high temperature under specific atmosphere is the nondestructive Resonant Frequency Damping Analysis (RFDA). The objective of this paper was to evaluate and validate the modulus measurement via RFDA for orthotropic C/C-SiC composites at the application temperature. At room temperature flexural moduli of C/C-SiC with 0/90° reinforcement were measured under quasi-static 4-point bending loads and compared with dynamic moduli measured via RFDA longitudinally to fibre direction. The dynamic modulus of C/C-SiC was then measured via RFDA up to 1250°C under flowing inert gas and showed an increase with temperature which fitted with literature values. The measured fundamental frequencies were finally compared to those resulting from numerical modal analyses. Dynamic and quasi-static flexural moduli are comparable and the numerical analyses proved that bending modes are correctly modeled by means of dynamic modulus measured via RFDA. The nondestructive RFDA as well as the numerical modeling approach are suitable for evaluation of C/C-SiC and may be transferred to other fibre reinforced ceramic composite materials.     

石墨烯增强钛基复合材料制备工艺与性能研究

以多层石墨烯为增强体,通过熔炼锻造(MF)和粉末冶金(PM) 2种工艺分别制备出规格为Φ10 mm的石墨烯增强钛基复合材料棒材。石墨烯在凝固过程中以TiC枝晶形态析出,变形后呈细小颗粒,其中Ti和C原子比约为2∶1。石墨烯和球形钛粉经过机械合金化和变形加工,在基体中反应形成薄片层。MF工艺对应的棒材拉伸强度可达476 MPa,延伸率保持在28%; PM工艺对应的棒材拉伸强度可达487 MPa,延伸率保持在30%。PM工艺可形成尺寸较小的薄片状石墨烯增强体,强化作用提升,同时塑性没有显著下降。     

Nonreciprocal properties of 1D magnetized plasma photonic crystals with the Fibonacci sequence

In this paper, the nonreciprocal properties of a novel kind of 1D magnetized plasma photonic crystals(MPPCs) with the Fibonacci sequence are investigated. The isolation of the proposed 1D MPPCs is also used to analyze the nonreciprocal properties. Compared to the conventional 1D MPPCs with periodic structure, the nonreciprocal performance can be significantly improved.The effects of several parameters of the proposed 1D MPPCs on the nonreciprocal properties are studied by the transfer matrix method, which includes the incident angle, order of the Fibonacci sequence, plasma frequency, plasma cyclotron frequency and plasma filling factor. The obtained results show that the nonreciprocal propagation properties can be improved by increasing the values of the plasma cyclotron frequency and incident angle, but they will worsen by blindly increasing the order of the Fibonacci sequence, plasma frequency and filling factor of plasma.The peaks of transmittance also are obviously reduced. In addition, the value of isolation will increase with increasing the incident angle, order of Fibonacci sequence, plasma frequency and plasma filling factor. However, when the plasma cyclotron frequency is increased, the value of isolation will be increased at lower frequencies, but is almost unchanged at higher frequencies.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.