一种酰胺类沥青乳化剂的制备及性能研究

首先通过脂肪酸与酸酐反应，得到多元羧酸中间体，再将其与改性多乙烯多胺经过一系列反应合成一种酰胺类沥青乳化剂，采用傅里叶红外光谱对乳化剂结构进行表征。实验室对比测试了自制乳化剂与进口乳化剂的表面活性及其所制乳化沥青的性能。结果表明：两种乳化剂制备乳化沥青的各项性能指标均可满足微表处施工技术要求，且自制乳化剂具有较高的固含量和较好的储存稳定性，乳化沥青8 min即可破乳，用于微表处养护可快速开放交通；自制乳化剂价格仅为进口乳化剂的76.4%左右，应用前景良好。     

Stiffness and strength characteristics of demolition waste,glass and plastics in railway capping layers

The increased generation of demolition waste has led to the successful implementation of its utilization in civil engineering projects. The combination of recycled aggregates and supplementary materials can potentially improve the quality of geomaterials when constructing alternative railway capping layers. In this research, two types of demolition waste, namely, Recycled Concrete Aggregates (RCA) and Crushed Brick (CB), were studied in comparison to two Conventional Capping Materials (CCMs), which are currently used for railway track construction. Recycled Glass (RG) and Mixed Recovered Plastic (MRP) were also blended with RCA to assess their performance. All the materials and mixtures were evaluated in terms of both stiffness and strength. A new Repeated Load Triaxial (RLT) testing protocol was introduced based on the stress induced in the capping layers to determine the stiffness of the materials. A comparison was made among the current resilient modulus prediction models to find a model that would better fit the results for the demolition waste and mixtures. Multistage triaxial tests were also conducted to determine the strength, friction, stiffness and energy absorption capacity of the materials. It was found from this research study that RCA, CB and mixtures of RCA with RG and MRP have equivalent or higher levels of stiffness and strength than CCMs and are suitable alternatives for sustainable railway capping layer construction.     

弹性地基处周期性圆形钢筋混凝土管道振动特性分析

针对弹性地基处周期性钢筋混凝土管道的波动特性,基于声子晶体理论和Flugge壳体理论,建立了圆形管道环径向轴对称波动微分方程,利用传递矩阵法建立了相邻胞元间的传递矩阵,数值分析了周期性管道结构的能带特性。结果表明,振动波在传播过程中存在禁带域和通带域,弹性地基对弯曲波在特定频率范围内的传播具有抑制作用,长度比的变化对周期性圆形混凝土管道禁带的幅值、宽度和个数影响显著,因此可通过调整结构尺寸参数改变结构中波的传播特性。     

Impact of ZrO2 alloying on thermo-mechanical properties of Gd3NbO7

The ZrO₂ alloying effect is widely used to optimize the thermo-mechanical properties of potential thermal barrier coatings. In this study, dense x mol% ZrO₂-Gd₃NbO₇ with C222₁ space group were manufactured via a solid-state reaction. The crystalline structure was determined through X-ray diffraction and Raman spectroscopy, when the surface morphology was observed by scanning electron microscopy. ZrO₂-Gd₃NbO₇ had identical orthorhombic crystal structures, and there was no second phase. The crystalline structure of ZrO₂-Gd₃NbO₇ shrunk with the increasing ZrO₂ content as indicated by XRD and Raman results. The heat capacity and thermal diffusivity of ZrO₂-Gd₃NbO₇ were 0.31–0.43 J g⁻¹ K⁻¹ (25–900 °C) and 0.25–0.70 mm²/s (25–900 °C), respectively. It was found that ZrO₂-Gd₃NbO₇ had much lower thermal conductivity (1.21–1.82 W m⁻¹ K⁻¹, 25–900 °C) than YSZ (2.50–3.00 W m⁻¹ K⁻¹) and La₂Zr₂O₇ (1.50–2.00 W m⁻¹ K⁻¹). The thermal expansion coefficients (TECs) were higher than 10.60 × 10⁻⁶ K⁻¹ (1200 °C), which were better than that of YSZ (10.00 × 10⁻⁶ K⁻¹) and La₂Zr₂O₇ (9.00 × 10⁻⁶ K⁻¹). The mechanical properties of Gd₃NbO₇ change little with the increasing ZrO₂ content, Vickers hardness was about 10 GPa, and Young's modulus was about 190 GPa, which was lower than YSZ (240 GPa). Compared with previous work about alloying effects, much lower thermal conductivity was obtained. Due to the high melting point, high hardness, low Young's modulus, ultralow thermal conductivity and high TECs, it is believed that ZrO₂-Gd₃NbO₇ is promising TBCs candidate.     

The effect of barium titanate ceramic loading on the stress relaxation behavior of barium titanate-silicone elastomer composites

The stress relaxation behavior of barium titanate (BTO)-elastomer (Ecoflex) composites, as used in large strain sensors, is studied using the generalized Maxwell-Wiechert model. In this article, we examine the stress relaxation behavior of ceramic polymer composites by conducting stress relaxation tests on samples prepared with varying the particle loading by 0, 10, 20, 30, and 40 wt% of 100 and 200 nm BTO ceramic particles embedded in a Ecoflex silicone-based hyperelastic elastomer. The influence of BTO on the Maxwell-Wiechert model parameters was studied through the stress relaxation results. While a pristine Ecoflex silicone elastomer is predominantly a hyperelastic material, the addition of BTO made the composite behave as a visco-hyperelastic material. However, this behavior was shown to have a negligible effect on the electrical sensing performance of the large strain sensor.     

Microstructure and interface-adhesion of thermally sprayed continuous gradient elastic modulus FeCrAl-ceramic coatings

The bond strength between thermally sprayed metal bond-coats and ceramic top-coats is a key factor in determining their service life. However, most studies focus on interface modifications. In this research, based on FeCrAl bond-coats prepared by arc spraying, top-coats (Al₂O₃-40 wt% TiO₂) were prepared by plasma spraying, and heat treatment was carried out in a hypoxic atmosphere. Continuous gradient elastic modulus FeCrAl-ceramic coatings were successfully prepared, and the microstructural and mechanical properties from the substrate to the top-coats were systematically investigated. The Al₂O₃ content gradually decreased from the top-coats to the substrate, forming continuous gradient elastic modulus FeCrAl-ceramic coatings. The oxide formed during the heat treatment filled the defects in the bond-coats and greatly improved the mechanical properties of the coating. The bonding strength of the continuous gradient elastic modulus coating was 21.7% greater than that of the as-received coating.     

Dynamic mechanical response of polyetheretherketone (PEEK) exposed to cyclic loads in the high stress tensile regime

Tensile fatigue tests of PEEK at high load levels were carried out and analyzed regarding the dynamic mechanic behavior of the material during the tests. The experiments were conducted in the high stress tensile regime of PEEK. After a short period of unsteady temperature increase distinct material changes were observed during the tests at medium and high load levels. The storage modulus increased continuously while the loss modulus, loss factor (tan δ) and the dissipation energy rate decreased continuously. It was concluded that the accumulation of residual stresses because of partially irreversible deformation causes this effect.     

A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

A robust adaptive weighted constant modulus algorithm is proposed for blind equalization of wireless communication systems under impulsive noise environment. The influence of the impulsive noise is analyzed based on numerical analysis method. Then an adaptive weighted constant modulus algorithm is constructed to adaptively suppress impulsive noise. Theoretical analysis is provided to illustrate that the proposed algorithm has a robust equalization performance since the impulsive noise is adaptively suppressed. Moreover, the proposed algorithm has stable and quick convergence due to avoidance of large misadjuntment and adoption of large step size. Simulation results are presented to show the robust equalization performance and the fast convergence speed of the proposed algorithm under both impulsive noise and Gaussian noise environments.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.