Effects of injection parameters on propagation patterns of hydrogen-fueled rotating detonation waves

Two-dimensional rotating detonation waves (RDWs) with separate injections of hydrogen and air are simulated using the Navier–Stokes equations together with a detailed chemical mechanism. The effects of injection stagnation temperature and slot width on the detonation propagation patterns are investigated. Results find that extremely high temperatures can lead to a chaotic mode in which detonation waves are generated and extinguished randomly. Increasing the slot width can reduce the number of detonation waves and finally trigger detonation quenching at a low injection stagnation temperature. But increasing the slot width can change the RDW propagation pattern from a chaotic to a stable mode under high injection temperature. Furthermore, the kinetic parameter τ (representing the chemical reactivity of the mixture) and the kinematic parameter α (representing the mixing efficiency of hydrogen and oxygen) are introduced to distinguish the RDW propagation patterns.     

Optimization of structural,electrical, and magnetic properties of ytterbium substituted W-type hexaferrite for multi-layer chip inductors

The rare earth (Yb³⁺) substituted W-type hexagonal ferrites with composition CaPb_2-xYb_xFe₁₆O₂₇ (x = 0.0, 0.5, 1.0, 1.5, 2.0) were synthesized by a facile and cost-effective sol-gel auto combustion method with post heat treatment. The synthesized hexagonal ferrites were characterized by a variety of analytical techniques, and an impedance analyzer was used to investigate the effects of Ytterbium on structural, magnetic, spectral and dielectric properties. The relationship between their impedance, structure and dielectric properties was investigated. The X-ray diffraction patterns verify the presence of single-phase W-type hexagonal ferrites. Physical properties such as D_bulk (bulk density), D_xrd (X-ray density), and P (porosity) of the CaPb_2-xYb_xFe₁₆O₂₇ W-type hexagonal ferrites were calculated. The bulk density of all the samples was decreased, and X-ray intensity was increased with the Ytterbium replacement in the W-type hexaferrite. By adding Yb³⁺ ions, the lattice parameters, cell volume and X-ray density were reduced due to the substitution of ytterbium with smaller ionic radii compared to the lead ion with large ionic radii. The AC-conductivity was increased from (1.523 × 10^?5 to 6.699 × 10^?5) Ωcm^?1. The dielectric constant and tangent loss was found to decrease substantially. The magnetic properties were found to enhance by the substitution of Yb³⁺. The low coercivity value of Yb³⁺ substituted W-type hexagonal ferrites are suitable for magnetic recording media operated at a high-frequency regime. The enhancement of electrical, dielectric and magnetic characteristics suggests these materials as promising for multi-layer chip inductors (MLCIs) circuit applications.     

Hybrid hydrate processes for CO2/H2 mixture purification: A techno-economic analysis

In this work, hydrate based separation technique was combined with membrane separation and amine-absorption separation technologies to design hybrid processes for separation of CO₂/H₂ mixture. Hybrid processes are designed in the presence of different types of hydrate promoters. The conceptual processes have been developed using Aspen HYSYS. Proposed processes were simulated at different flow rates for the feed stream. A comprehensive cost model was developed for economic analysis of novel processes proposed in this study. Based on the results from process simulation and equipment sizing, the amount of total energy consumption, fixed cost, variable cost, and total cost were calculated per unit weight of captured CO₂ for various flow rates of feed stream and in the presence of different hydrate promoters. Results showed that combination of hydrate formation separation technique with membrane separation technology results in a CO₂ capture process with lowest energy consumption and total cost per unit weight of captured CO₂. As split fraction and heat of hydrate formation increases, the share of hydrate formation section in total energy consumption increases. When TBAB is applied as hydrate promoter, due to its higher hydrate separation efficiency, more amount of CO₂ is captured in hydrate formation section and consequently the total cost for process decreases considerably. Hybrid hydrate-membrane process in the presence of TBAB as hydrate promoter with 29.47 US$/ton CO₂ total cost is the best scheme for hybrid hydrate CO₂ capture process. Total cost for this process is lower than total cost for single MDEA-based absorption process as the mature technology for CO₂ capture.     

Influencing parameters in the electrochemical anodization of TiO2 nanotubes: Systematic review and meta-analysis

Comprehensive control of processing techniques is primordial when fine-tuning the morphological features of titanium dioxide nanotube arrays (TNTs). This systematic review and meta-analysis compiled articles published from 2007 to date on the synthesis and growth mechanism of nanotubes fabricated via electrochemical anodization and evaluated the potential relationships between anodizing conditions and the resulting structures. Studies were gathered from the Science Direct online database, screened according to predefined criteria, and evaluated for their eligibility. Ninety-nine studies were assessed in the meta-analysis, 87 of them on tube length, 80 on tube diameter, and 33 on wall thickness. Multiple linear regression was performed to test if anodization parameters significantly predicted the resulting morphology of TiO₂ nanotubular structures. Overall regression for the three responses was statistically significant (length: R² = 0.487, p < 0.001; diameter: R² = 0.899, p < 0.001; wall thickness: R² = 0.792, p < 0.001). Applied potential was one of the main effects predicting all three responses (p < 0.001 in every model). Other important main predictors were anodizing time for tube length (p < 0.001), water percentage for tube diameter (p < 0.001) and ammonium fluoride (NH₄F) concentration for wall thickness (p < 0.001).     

5万m~2地下特大型支撑爆破拆除

介绍了在复杂环境下爆破拆除一地下特大钢筋混凝土支撑的技术难点。由于合理选取爆破参数,采取孔内高段、孔外低段毫秒微差起爆网路,安全防护采取覆盖、近体、保护性三种措施,有效地阻止了飞石对周围建筑物的损害,并对爆破可能产生的危害进行了科学验算,最后分多次爆破圆满完成拆除任务。     

注射模生产中智能化控制及应用

分析了注射模生产的现状，针对其生产中智能化调控应用方面的不足，提出模内参数的自适应调节方案，还介绍了自适应工作的原理、可调参数种类、逻辑推理等，并实际验证了基于注塑设备联网集成工艺数据下注射模成型工艺自适应调节的可行性。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Molecularly imprinted macroporous polymer monolithic layers for L-phenylalanine recognition in complex biological fluids

In present work, the development of macroporous monolithic layers bearing the artificial recognition sites toward L-phenylalanine has been carried out. The set of macroporous poly(2-aminoethyl methacrylate-co-2-hydroxyethyl methacrylate-co-ethylene glycol dimethacrylate) materials with average pore size ranged in 340–1200 nm was synthesized. The applicability of Hildebrand's and Hansen's theories for the prediction of polymer compatibility with porogenic solvents was evaluated. The dependences of average pore size on theoretically calculated parameters were plotted. The linear trend detected for Hansen's theory has indicated the high suitability of this approach to select appropriate porogens. The synthesized monolithic MIP layers were tested toward the ability to rebind phenylalanine-derivative in microarray format. The influence of such factors as average pore size of the material, the concentration of template molecule in polymerization mixture, interaction time of analyte with its imprinted sites on binding efficiency were studied. The developed materials demonstrated good analyte rebinding from buffer solution with recognition factors 2.5–3.4 depending on the MIP sample. The comparable rebinding efficiency was also detected when the analysis was carried using complex biological media. The selectivity of phenylalanine binding from the equimolar mixture of structural analogues was 81.9% for free amino acid and 91.2% for labeled one.     

稀土灼烧过程温湿度场仿真分析

影响稀土灼烧工艺的因素十分复杂,关系产品质量稳定及能耗,现行工艺存在优化空间。通过剖析灼烧窑中温度和湿度分布状况,运用κ-ε双方程湍流模型、流体传热、多孔介质传热等理论,按特定组分运输模式,建立灼烧过程质量、动量和能量耦合传递数学模型。设置不同边界导入Fluent环境对数学模型进行仿真试验,完成数据处理实现工艺参数优化。结果表明所建模型能准确反映灼烧窑中温湿度场分布及变化,且最终仿真结果与实际灼烧后的产品湿度含量相符合。     

Multiple material domain‐based computational tools for ease assessment of modal parameter in photonic crystal fibers

We report here the development of two computational tools PCFPS (Photonic Crystal Fiber Parameter Study) and PCFPA (Photonic Crystal Fiber Parameter Analysis), equipped with graphical user interface (GUI) for modeling of photonic crystal fiber. The tools are based on different structural parameters, and they provide characteristic analysis of the modal parameters from the structural parameters. The main feature of PCFPS is that it enables the user to find out the values of each defining modal parameter that has an immense contribution towards the manufacture of photonic crystal fiber. Additionally, PCFPA allows the user to observe the variation in the modal parameters with respect to the changes in structural parameters (such as d, Λ, d/Λ, and λ/>Λ). Besides their ease of use, these two schemes have high computational precision and adaptability, giving a novel platform to optical engineers to modulate the microstructured fibers according to their requirement.