纺织复合材料纤维预制体力学性能测试方法研究进展

纺织复合材料具有质量轻、强度高，可设计性强等诸多优势，在航空航天领域得到广泛应用。纺织预制体的纤维结构对复合材料的最终力学性能有着决定性影响。然而，预制体的纤维结构在织造过程中不可避免地会发生宏观尺寸和微细观结构的变形，甚至产生褶皱缺陷。纺织预制体作为一种柔性骨架，其变形机制十分复杂。采用试验测试来表征预制体的力学变形特性是最直接、最有效的方法，也是建立理论和数值分析模型的基础。本文对纺织复合材料预制体的拉伸、压缩、弯曲、剪切和成型试验等测试方法进行了综述，讨论了不同测试方法的优缺点及适用条件，对后续的研究工作进行了展望。本研究将为预制体力学测试技术的改进、测试标准的建立和成型过程中的准确控形提供理论指导，对纺织复合材料的结构设计和工程应用起到推动作用。     

水力空化改质对重质原油沥青质及性质的影响

利用水力空化过程产生局部的高温、高压、高射流以及强大的剪切力等极端化学物理条件改质处理沙特重质原油，试验结果表明：沙特重质原油经过水力空化改质后粘度由13.61降低至7.22mm2/s，残碳由7.16%降低至6.48%，实沸点蒸馏后减压渣油降低1个百分点。进一步采用APPI FT-IR MS、XRD、FT-IR、SEM和粒度分布等技术研究了水力空化改质对沙重原油分子组成，沥青质团聚体微晶结构、沥青质胶束粒径分布、沥青质官能团、沥青质形貌等方面的影响，从分子角度阐述空化改质重油的机理。研究结果表明：水力空化改质后沙重原油分子量分布、芳烃类化合物缔合作用变小；沥青质对低DBE化合物吸附性能降低；沥青质团聚体微晶结构更加松散；沥青质胶束粒度分布降低；沥青质分子相互团聚作用力减弱。进一步考察了水力空化改质前后减压渣油延迟焦化性能，改质处理后焦炭产率降低1.85个百分点，液体收率和气体产率分别增加1.52和0.33个百分点，水力空化改质对沥青质性质、结构特点的改善能够有效的提高其加工性能。     

Scallops as a new source of food protein: high-intensity ultrasonication improved stability of oil-in-water emulsion stabilised by myofibrillar protein

In this study, the effect of high-intensity ultrasound (HIUS) (200 and 400 W for 0, 5, 10 and 15 min respectively) on conformational changes, physicochemical, rheological and emulsifying properties of scallop (Patinopecten yessoensis) myofibrillar protein (SMP) was investigated. HIUS-treated SMP had lower α-helix content and higher β-sheet content compared with the native SMP. HIUS treatment induced the unfolding of SMP and increased the surface hydrophobicity. The particle size of SMP decreased and the absolute zeta-potential increased after ultrasonication, which in turn increased the solubility of SMP. The conformational changes and the improvement of physicochemical properties of SMP increased the ability for SMP to lower the interfacial tension at the oil–water interface and increased the percentage of adsorbed protein. As a result, the emulsifying properties, rheological properties of SMP and storage stability of emulsions were also improved. In conclusion, HIUS treatment has future potential for improving the emulsifying properties of SMP.     

Improving gelling properties of myofibrillar proteins incorporating with cellulose micro/nanofibres

The effects of cellulose microfibres (CMFs, Average size: 100 ± 5 μm) and cellulose nanofibres (CNFs, Average size: 60 ± 3 nm) on the properties of myofibrillar protein (MP) gels from duck breast meat were studied. The results demonstrated that CMFs and CNFs were mostly connected to MP by non-covalent bonds, the diffusion and cross-linking of MP molecules was promoted, and a denser and more complete gel network was formed. With the increases of CMFs and CNFs concentration (0–10%), the hardness was increased by 13.15% and 19.78% for CMFs10% and CNFs10% gels, respectively, and the elasticity was increased by 40% and 80%, respectively. At the same concentration (0–10%), the increase in gel hardness, viscoelasticity and immobilised water content was greater in the CNFs-MP group than in the CMFs-MP group. The CNFs-MP group had a tighter gel network, and CNFs had a better potential to improve the gelation performance of MP.     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

Self-healing,elastic and deformable novel composite phase change polymer based on thermoplastic elastomer SEBS for wearable devices

Journal of Materials Science - Form-stable composite phase change materials (C-PCMs) prepared by microencapsulation method and porous matrix adsorption method need for compression molding after...     

基于大数据深度学习的输电塔结构抗风易损性评估

为了有效预测输电网系统在强风作用下的响应，并开展高效精准的性能评估，文章提出了基于深度学习模型的风致易损性评估框架。以某具备健康监测系统的输电塔结构为例，首先对监测数据进行清洗和重构，通过大数据深度学习建立荷载输入和响应输出的等效映射模型，然后通过数值模拟生成灾害强度均匀的风场数据并由深度学习模型预测输电塔关键杆件响应，计算不同性能水准的易损性曲线。研究结果表明，经训练的深度学习模型可以涵盖实际工程中存在的各类不确定性因素，有效映射复杂风环境下输电塔结构的动力响应。提出的框架方法可以避免单纯通过数值模型制备大量动态响应数据，更高效地进行输电网系统风致易损性评估。