基于动态模态分解的缸内流场演变及动能分析

采用高速粒子图像测速技术(particle image velocimetry,PIV)测量了一台直喷式光学发动机的缸内流场,利用动态模态分解(dynamic mode decomposition,DMD)算法,提取了发动机从进气冲程早期到压缩冲程后期中出现的多尺度涡团结构,量化了涡团的比动能在整个冲程阶段的衰减程度。结果表明:从进气冲程早期开始,缸内流场主要由低阶DMD模态表现的大尺度流场结构和高阶DMD模态表现的小尺度涡团结构组成;DMD模态的比动能变化可清楚地反映从大尺度流场结构到小尺度涡团的能量级联和耗散过程。同时还发现,与压缩冲程相比,进气冲程期间的流场可形成更多小尺度的涡团结构,并表现出更快的能量衰减特征,且该阶段流场能量衰减现象对发动机转速更加敏感。     

Production of hydrogen by catalytic methane decomposition using biochar and activated char produced from biosolids pyrolysis

Catalytic methane decomposition (CMD) was studied by employing biochar and activated char of biosolids’ origin under different reaction temperatures and methane concentrations. Higher reaction temperatures and lower inlet methane concentrations were found to be favourable for achieving higher methane conversion. A maximum initial methane conversion of 71.0 ± 2.5 and 65.2 ± 2.3% was observed for activated char and biochar, respectively at 900 °C and for 10% CH₄ in N₂ within the first 0.5 h of experiment. Active sites from oxygen containing carboxylic acid functional groups and smaller pore volume and pore diameter were attributed to assist in higher initial methane conversion for biochar and activated char respectively. However, rapid blockages of active sites and surfaces of biochar and activated char due to carbon formation have caused a rapid decline in methane conversion values in the first 0.5 h. Later on, crystalline nature of the newly formed carbon deposits due to their higher catalytic activity have stabilised methane conversion values for an extended experimental period of 6 h for both biochar and activated char. The final conversion values at the end of 6 h experiment with biochar and activated char at 900 °C and for 10% CH₄ in N₂, were found to be 40 ± 1.9 and 35 ± 1.6% respectively. Analysing carbon deposits in detail revealed that carbon nanofiber type structures were observed at 700 °C while nanospheres of carbon were found at 900 °C.     

Methane thermocatalytic decomposition to COx-free hydrogen and carbon nanomaterials over Ni–Mn–Ru/Al2O3 catalysts

Thermocatalytic decomposition of methane is proposed to be an economical and green method to produce CO_x-free hydrogen and carbon nanomaterials. In this work, the catalytic performance of Ni–Mn–Ru/Al₂O₃ catalyst under different reaction parameters (such as, pre-reduction temperature, reaction temperature, space velocity, etc.) were investigated to obtain optimum reaction conditions. The catalysts were characterized by N₂ adsorption/desorption, X-ray diffraction, inductively coupled plasma optical emission spectrometer and hydrogen temperature programmed reduction. For the 60 wt% Ni-5 wt% Mn-10 wt% Ru/Al₂O₃ catalyst using Ru(NO)(NO₃)_x(OH)_y(x + y = 3) as Ru precursor, the methane conversion rate obtained is high as 93.76% under optimum reaction conditions (reduction at 700 °C for 1 h, reaction at 750 °C, GSHV = 36,000 mL/g_cat h). Carbon nanomaterials formed during the process of methane thermocatalytic decomposition were characterized by scanning electron microscopy, thermal gravimetric analyzer and Raman spectroscopy. Carbon nanofibers were formed over all the Ni–Mn–Ru/Al₂O₃ catalysts.     

High-temperature degradation of butadiene-based model elastomers by reactive molecular dynamics simulation

Thermal degradation of butadiene-based model elastomers was analyzed via a novel reactive molecular dynamics simulation (ReaxFF) method. The molecular simulation was carried out on 40 monomer units connected together. Degradation pathways of both homopolymer and copolymer of butadiene-based model elastomers such as polybutadiene (BR) and poly (styrene-co-butadiene) (SBR) were studied. The evolution of different fragmented products was examined as a function of time and heating rate. The formation mechanisms of different degraded fragments were visualized via the simulation method. The major decomposition products obtained from these model compounds were the monomers and comonomers. Pyrolysis gas chromatography–mass spectrometry (py-GC–MS) analysis was performed on the commercial samples of BR and SBR to verify the simulation results. The results obtained from the reactive simulation were very consistent with the experimental results. The activation energy required for the thermal decomposition of butadiene-based model elastomers were calculated both from the ReaxFF simulation and thermogravimetric analysis (TGA). The results were also in good agreement. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48592.     

Concept design of a novel reformer producing hydrogen for internal combustion engines using fuel decomposition method: Performance evaluation of coated monolith suitable for on-board applications

Hydrogen addition effectively reduces the fuel consumption of spark ignition engines. We propose a new on-board reformer that produces hydrogen at high concentrations and enables multi-mode operations. For the proposed reformer, we employ a catalytic fuel decomposition reaction via a commercial NiO–CaAl₂O₄ catalyst. We explore the physical and chemical aspects of the reforming process using a fixed bed micro-reactor operating at temperatures of 550–700 °C. During reduction, methane is decomposed to form hydrogen and carbon. Carbon formation is critical to hydrogen production, and free space for carbon growth is essential at low temperatures (≤600 °C). We define a new accumulated conversion ratio that quantitatively measures highly transient catalytic decomposition. The free space of the coated monolith clearly aided low-temperature decomposition with negligible pressure drop. The coated substrate is therefore suitable for on-board applications considering that our reformer concept also utilizes the catalytic fuel decomposition reaction.     

Correlation of hydrogen generation and optical emission properties of plasma in water photolysis on perovskite photocatalysts

Photocatalytic decomposition of organic materials-contained aqueous solution is assessed using a plasma discharged into the liquid directly. The correlation of H₂ generation and optical emission spectroscopy is discussed in terms of photocatalytic H₂ production using plasma and photocatalysts. Variations of the active species are evaluated according to the conditions of the plasma in the liquid phase. The optical emission spectra vary according to the plasma discharging conditions in the liquid phase. The intensities of the OH· peaks at 309 nm increase with the addition of ethanol or acetaldehyde in water. The highest intensities and rate of H₂ evolution are observed at a 10% acetaldehyde concentration in the aqueous solution. The rates of H₂ evolution in the ethanol or acetaldehyde solution correspond to the concentration of OH· in the solution. The photocatalytic reaction using liquid plasma generates hydrogen at the same time as the decomposition of the organic chemicals. The rate of hydrogen evolution in aqueous solutions containing the organic chemicals is higher than that in pure water. This is because hydrogen is further generated due to hydrogen generation by photolysis of the organic chemicals. CaTiO₃ perovskite photocatalyst shows better photocatalytic activity than TiO₂. Ni loading on the photocatalyst lead to an increase in H₂ production.     

铝空气电池废电解液生产超细氢氧化铝工艺条件研究

以铝空气电池的废电解液为原料, 采用种分法生产氢氧化铝。结果表明, 当种分时间为24 h、晶种系数为2%~4%时, 可生产出超细氢氧化铝, 且粒径分布宽度窄, 阻燃性能优良, 达到HG/T4530-2013氢氧化铝阻燃剂ATH-1一等品的要求。     

两类复合无机相变储热材料高温热稳定性和安全性研究

以适合工业储热的复合无机相变储热材料硝酸盐（KNO₃、NaNO₃）和碳酸盐（Li₂CO₃、K₂CO₃、Na₂CO₃和CaCO₃）为相变组分，研究了4种不同配比硝酸盐相变组分和6种不同配比碳酸盐相变组分的热性能（熔点、潜热）差异，分别优选出一种熔融盐相变组分配比。利用多孔载体吸附原理，制备出两种最佳配比的复合熔融盐类储热材料，分析了储热材料在不同介质气氛中（Ar和air）分解难易程度，TG-DSC-MS联用测试的高温热分解产物表明，复合硝酸盐类储热材料在中温（300℃）储热时，物理化学性能稳定，安全性较好，但在高温（500℃以上）时易分解成NO和NO₂，且air气氛中更易生成有毒气体；而复合碳酸盐类储热材料在air中比在Ar中更易生成CO而影响储热过程中的安全性。     

基于VMD与时间序列分析的滚动轴承故障特征提取方法

为了在非线性、非平稳的滚动轴承故障振动信号中有效提取出敏感的故障特征,提出了基于变分模态分解(VMD)与时间序列分析相结合的特征提取方法。首先通过VMD将原始信号分解为不同预设尺度的本征模态分量(IMF),对各个IMF分量建立时间序列预测模型,通过叠加重构得到最终的预测模型,比较评价指标确定最优参数的选取。最后,通过仿真信号与滚动轴承实际故障数据分析,并与经验模式分解(EMD)进行对比,结果表明该方法能够有效的提取到故障特征频率。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。