Revealing cracking and breakage behaviours of gibbsite particles

Mitigating gibbsite particle cracking and breakage during industrial alumina production can increase the quality of smelter grade alumina product by reducing the ultrafine particle content. Therefore, it is essential to investigate the particle cracking during static calcination and the breakage of calcined gibbsite particles under external force. In this work, we investigated the impact of the calcination ramping rate and the crystallite size on gibbsite particle cracking during static calcination. A slow ramping rate and a large pristine crystallite size tend to increase particle cracking. Apart from the study of particle cracking behaviour, we also investigated the breakage of calcined gibbsite particle under external force. Cracks on the particle surface can initiate breakage within the crystallite and along the grain boundary under external force. The breakage within crystallite occurs as the cleavage of the crystallite, while the breakage along the grain boundary leads to the shedding of a whole crystallite. We further explored the factors influencing the strength of calcined gibbsite particles. With increasing calcination temperature, the strength of particle increases when gibbsite converts to boehmite, and then decreases when boehmite converts into amorphous alumina. Particles containing smaller crystallites and calcined with fast ramping rates exhibit higher resistance to breakage.     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

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。     

Gluten–starch interface characteristics and wheat dough rheology—Insights from hybrid artificial systems

Referring to the total surface existing in wheat dough, gluten–starch interfaces are a major component. However, their impact on dough rheology is largely unclear. Common viewpoints, based on starch surface modifications or reconstitution experiments, failed to show unambiguous relations of interface characteristics and dough rheology. Observing hybrid artificial dough systems with defined particle surface functionalization gives a new perspective. Since surface functionalization standardizes particle–polymer interfaces, the impact on rheology becomes clearly transferable and thus, contributes to a better understanding of gluten–starch interfaces. Based on this perspective, the effect of particle/starch surface functionality is discussed in relation to the rheological properties of natural wheat dough and modified gluten–starch systems. A competitive relation of starch and gluten for intermolecular interactions with the network-forming polymer becomes apparent during network development by adsorption phenomena. This gluten–starch adhesiveness delays the beginning of non-linearity under large deformations, thus contributing to a high deformability of dough. Consequently, starch surface functionality affects the mechanical properties, starting from network formation and ending with the thermal fixation of structure.     

Microstructure and mechanical properties of 3D-printed nano-silica reinforced alumina cores

Ceramic cores are an important component in the preparation of hollow turbine blades for aero-engines. Compared with traditional hot injection technology, 3D printing technology overcomes the disadvantages of a long production cycle and the difficulty in producing highly complex ceramic cores. The ceramic cores of hollow turbine blades require a high bending strength at high temperatures, and nano-mineralizers greatly improve their strength. In this study, nano-silica-reinforced alumina-based ceramic cores were prepared, and the effects of nanopowder content on the microstructure and properties of the ceramic cores were investigated. Alumina-based ceramic cores contained with nano-silica were prepared using the vat photopolymerization 3D printing technique and sintered at 1500 °C. The results showed that the linear shrinkage of ceramic cores first increased and then decreased as the nano-silica powder content increased, and the bending strength showed the same trend. The fracture mode changed from intergranular to transgranular. The open porosity and bulk density fluctuated slightly. The weight loss rate was approximately 20%. When the nano-silica content was 3%, the bending strength reached a maximum of 46.2 MPa and 26.1 MPa at 25 °C and 1500 °C, respectively. The precipitation of the glass phase, change in the fracture mode of the material, pinning crack of nanoparticles, and reduction of fracture energy due to the interlocking of cracks, were the main reasons for material strengthening. The successful preparation of 3D printed nano-silica reinforced alumina-based ceramic cores is expected to promote the preparation of high-performance ceramic cores with complex structures of hollow turbine blades.     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.