Packing Ratio Research Articles

Yttria substitution effect of tantalate aluminosilicate glasses with ultra‐high Young's modulus

AbstractWe report novel oxide glasses that are compatible with ultra‐high Young's modulus (∼150 GPa) and a small coefficient of thermal expansion (∼5.0 ppm/K). These glasses were prepared using a conventional melt‐quenching technique. The viscosity–temperature relation provides the reshaping temperature for the stiff and less dilated oxide glasses. The origin of the ultra‐high Young's modulus is the inclusion of Y2O3 as well as five‐ and six‐coordinated alumina oxide and Ta2O5, which has a large ionic packing ratio due to higher oxygen coordination numbers. On the other hand, the small thermal expansion coefficient originates from including Ta2O5 and SiO2 with large dissociation energy due to strong bonds.

The performance of hollow fiber direct contact membrane distillation modules

Computational fluid dynamics simulations were conducted in hollow fiber direct contact membrane distillation modules. The objective is to understand the impact of packing densities and incorporating flow mixers on the feed side of the system. The feed flow rate varies while the feed inlet and permeate inlet temperatures are fixed. Furthermore, finned hollow fibers are used as flow disruptors in the feed channel to mitigate temperature and concentration polarization and to enhance vapor flux. The laminar flow model is used to characterize velocity, temperature, and concentration fields. It is demonstrated that the packing ratio significantly influences when the flow rate is low. The tightly packed hollow fibers in the shell resulted in decreased permeating flux due to a lack of mixing in flow in the narrowed regions around hollow fibers. The data shows that the permeate flux was 14.5 % higher for a 25 % packing ratio compared to a 75 % packing ratio at a lower flow rate. In contrast, the flux decreases only 3 % at a higher flow rate with an increasing packing ratio. It is demonstrated that the presence of fins breaks the parallel flow and enhances the mixing performance by approximately 10 % While fin modifications can enhance flux performance and mitigate polarization issues, the decrease in the merit criterion suggests that the benefits must be weighed against the potential drawbacks, such as elevated energy consumptions due to the increase in pressure drops. This underscores the importance of a balanced approach in optimizing membrane module performance.

Origami of multi-layered spaced sheets

Two-dimensional (2D) origami tessellations such as the Miura-ori are often generalized to build three-dimensional (3D) architected materials with sandwich or cellular structures. However, such 3D blocks are densely packed with continuity of the internal material, while for many engineering structures with multi-physical functionality, it is necessary to have thin sheets that are separately spaced and sparsely connected. This work presents a framework for the design and analysis of multi-layered spaced origami, which provides an origami solution for 3D structures where multiple flat sheets are intentionally spaced apart. We connect Miura-ori sheets with sparsely installed thin-sheet parallelogram-like linkages. To explore how this connectivity approach affects the behavior of the origami system, we model the rigid-folding kinematics using analytic trigonometry and rigid-body transformations, and we characterize the elastic-folding mechanics by generalizing a reduced order bar and hinge model for these 3D assemblies. The orientation of the linkages in the multi-layered spaced origami determines which of three folding paths the system will follow including a flat foldable type, a self-locking type, and a double-branch type. When the origami is flat foldable, a maximized packing ratio and a uniform in-plane shear stiffness can be achieved by strategically choosing the link orientation. We show possible applications by demonstrating how the multi-layered spaced origami can be used to build deployable acoustic cloaks and heat shields.

Container technology for transporting rock masses in quarries

Container technology for transporting rock masses in quarries

Optimization of Fischer–Tropsch microchannel reactor using computational fluid dynamics and enveloped Bayesian optimization

We propose computational fluid dynamics (CFD)-enveloped Bayesian optimization (EBO), a novel optimizer that integrates EBO with CFD to reduce the required CFD simulations by utilizing previous optimization data. The proposed optimizer was applied to determine the optimal catalyst packing ratio of the Fischer–Tropsch microchannel reactor that minimizes the maximum temperature and maximizes the productivity of long-chain hydrocarbons by utilizing the CFD model. The obtained results indicate that the number of iterations required to reach the optimal points is lower than that of BO, and the optimal result exhibits a 5% improvement from the initial condition. The optimizer was evaluated across various catalyst packing cases to assess its robustness. Nevertheless, the proposed optimizer was consistently able to reach optimal points that BO could not achieve. We anticipate that this optimizer can be widely applied to optimize the operating condition of a chemical reactor in the presence of previous optimization data.

Ignition of Forest Fires by Cigarette Butts: Using Pinus massoniana Needles as an Example

As a cigarette butt falls onto the forest surface fuel, it first smolders the fuel, then ignites into flames, and spreads as forest fire under certain conditions. In this study, the needles under a typical stand of P. massoniana were used as the research object. Needle beds with different moisture content and packing ratios were constructed indoors. Cigarette butt-ignition experiments were conducted under different wind velocities, and 30 experiment cycles were conducted under different conditions. There was a total of 5 (packing ratio) × 4 (moisture content) × 6 (wind velocity) = 120 sets of conditions, and a total of 3600 ignition experiments were conducted. The results showed that (1) the total ignition probability of the cigarette butts was 2.36%, which only occurred when the fuelbed moisture content was <10% and the wind velocity was >1 m/s. The ignition time of cigarette butts ranged from 2.73 to 7.25 min. (2) The fuelbed moisture content and wind velocity significantly influenced the ignition probability and time. With an increase in moisture content, the ignition probability of cigarette butts decreased, while the time required for ignition showed an increasing trend. Wind velocity had a dual effect on ignition. The ignition effect was optimal at a wind velocity of 4 m/s. With an increase in wind velocity, the ignition probability first increased and then decreased, and the ignition time first decreased and then increased. (3) The packing ratio had no significant effect on the ignition probability; however, the ignition time significantly decreased as the packing ratio increased. (4) The logistic regression method (LRM), general linear method (GLM), and nonlinear regression method (NLM) were used to establish a prediction model of ignition probability. The prediction effect of GLM was the worst, followed by LRM, and the NLM had the best prediction effect. The GLM was selected to establish the ignition time model, and the error was also within the allowance range. This study elucidated the underlying mechanism of factors affecting cigarette butt-based fuel ignition. In addition, the established prediction model provides a reference for human-caused forest fires and is highly significant for forest fire prevention.

Rapid Solid‐State Gas Sensing Realized via Fast K+ Transport Kinetics in Earth Abundant Rock‐Silicates

Here we report on the discovery of a novel fast potassium super stoichiometric silicate, with fully earth‐abundant nominal chemical composition of K2+xMg1−x/2SiO4, which exhibits near superionic K+ conductivity, up to 5 × 10−5 S cm−1 at room temperature. Fast K+ conduction is attributed to a high Continuous Symmetry Measure value in K‐polyhedrons, coupled with a low packing ratio of Corner‐Sharing‐framework. This is the first time that such a high conductivity is measured by a rock‐silicate formed only by abundant metal ions. K2+xMg1−x/2SiO4 displays excellent stability under air and humidity, which renders it a very promising candidate for economical fabrication of electrochemical devices such as potentiometric gas sensors. We demonstrated this by fabricating a gas sensor for SO2 detection, as the first demonstration of type III potentiometric gas sensors using K+ conductors. At 500 °C and SO2 concentrations in the range of 0–10 ppm, the sensor exhibited high sensitivities (69–72 mV dec−1), robust signal output (220 mV for 2 ppm of SO2), fast response times (1–6 min), and excellent stability in ambient condition.

Experimental study and discrete element method analysis of impact of vibration on pebble packing

To develop tritium breeding technology for fusion reactors, Korea actively participated in the ITER Test Blanket Module (TBM) program, focusing on the development of a ceramic breeder-type TBM. The challenge was to introduce pebbles into the TBM without causing any damage to them. This study conducted experiments and Discrete Element Method (DEM) analyses to optimize the pebble-filling process for breeding blanket development. Even when facing obstacles such as grooved shapes on the walls of the pebble-filled space, these grooves did not significantly reduce the packing ratio. Vibrating the pebbles proved essential to increase the pebble packing ratio. Both experiments and DEM analyses confirmed an increase in pebble packing when vibration is applied. The study also investigated the impact of the timing of vibration application. It is better to vibrate after the pebble injection is done during injection to increase the pebble packing ratio. This approach increases the pebble packing ratio, but it also makes additional empty space. Consequently, maintaining continuous vibration from the initiation to the completion of the injection process can increase the packing ratio without the need to repeat the injection process to fill the empty space.

Learning Based 2D Irregular Shape Packing

2D irregular shape packing is a necessary step to arrange UV patches of a 3D model within a texture atlas for memory-efficient appearance rendering in computer graphics. Being a joint, combinatorial decision-making problem involving all patch positions and orientations, this problem has well-known NP-hard complexity. Prior solutions either assume a heuristic packing order or modify the upstream mesh cut and UV mapping to simplify the problem, which either limits the packing ratio or incurs robustness or generality issues. Instead, we introduce a learning-assisted 2D irregular shape packing method that achieves a high packing quality with minimal requirements from the input. Our method iteratively selects and groups subsets of UV patches into near-rectangular super patches, essentially reducing the problem to bin-packing, based on which a joint optimization is employed to further improve the packing ratio. In order to efficiently deal with large problem instances with hundreds of patches, we train deep neural policies to predict nearly rectangular patch subsets and determine their relative poses, leading to linear time scaling with the number of patches. We demonstrate the effectiveness of our method on three datasets for UV packing, where our method achieves a higher packing ratio over several widely used baselines with competitive computational speed.

High-bandwidth self-powered vibration sensors based on triboelectric particle-surface interactions

Vibrating triboelectric nanogenerators have the potential as self-powered vibration sensors if they can be designed to operate over a wide frequency band. Herein, we present a high-bandwidth self-powered triboelectric particle-surface vibration sensor. The sensor consists of a flat round capsule, filling with dielectric particles. The inner surfaces of the capsule are made of triboelectric materials backed by electrodes. When attached to a vibrating structure, the impact of the particles on the inner surfaces of the capsule causes them to be oppositely charged, and the dynamic motion of the charged particles in turn generates an alternating voltage across the two electrodes. A systematic investigation of the effects of particle properties, including material type, diameter, and packing ratio on the sensor’s output performance revealed that, among several designs, the combination of nylon particles and PTFE triboelectric layers yielded the highest output performance. The sensor’s flat frequency response has been found to span between 500 Hz and 7000 Hz, which is significantly higher than the recently reported values for triboelectric vibration sensors. Furthermore, the novel vibration sensor offers a frequency resolution of 0.19% at frequencies up to 8 kHz. The potentials of this new type of vibration sensor have been demonstrated for speech recognition applications and to detect structural vibrations due to drop-weight impact at different distances and heights.

For Security and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-Than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes the variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity than the conventional Nyquist-criterion transmission with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). More importantly, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

Effect of B2O3 substitution for P2O5 on the refractive index and optical absorption in pyro SnO−P2O5−B2O3 glasses

We investigated the impact of substituting B2O3 for P2O5 on a refractive index and optical absorption of pyro SnO−P2O5−B2O3 glasses. To date, we have reported that thermal properties and glass structure have a characteristic composition dependence when B2O3 replaces P2O5. In this paper, we report that optical properties such as optical absorption and refractive index also have a characteristic composition dependence. We observed a consistent increase in the refractive index with increasing B2O3 substitution, categorized into three regions, which is consistent with the trend in density. In addition, we observed that the optical absorption edge of the glass is shifted to a longer wavelength with increasing B2O3 concentration. To gain further insight into the optical properties of the glasses, we utilized Kramers–Kronig analysis to experimentally correlate the observed changes in refractive index and optical absorption. These changes in density and refractive index were predicted by elucidating the short-range structures of the glass, which were quantitatively provided by determining the proportions of phosphate and borate units. This information was then used to calculate the ion packing ratio.

Enhanced Performance of Ru-Based Infrared Imaging Sensor Array With Electrospun Thermal Isolation Structure

Predicting and optimizing thermal conductivity of materials used in infrared thermal imaging sensors is an effective method to improve thermal isolation performance, which can substantially reduce the thermal crosstalk among pixels. This paper reports an electrospun P(VDF-TrFE) nanofiber film used as adiabatic support structure in thermal sensor array, and investigates its effect on imaging performance of thermal sensor. Based on solid heat transfer simulation, the thermal conductivities of electrospun P(VDF-TrFE) films with different packing ratios were theoretically calculated, showing a significant decrease compared with the spin-coated counterpart. The temperature increment of thermal sensor using nanofiber film with 43.71% packing ratio is calculated as 1.68 times higher than that using spin-coated film. An average temperature rise of 1.73 times was experimentally obtained, and the thermal response time was also reduced to half, compared with the sensor using spin-coated film. The method of improving thermal isolation performance using electrospun fibrous film could be of great advantage to high sensitivity infrared imaging sensor.

Effect of Cavity Size on Performance of Particle Damper

Abstract: Particle damping is a damping technique that has found wide applications in engineering systems. It is a form of damping where the energy dissipation is achieved through the motion of small particles within a cavity. This technique has several unique features that make it an attractive option for many engineering applications. Cantilever beam with FFT analyzer is used to find the response parameters. The readings are taken with varying cavity dimensions, size of ball, packing ratio and ball material. The readings in the form of acceleration are noted by varying one experimental parameter. From the experimentation it is found that 24 mm diameter and height 37 mm cavity with 50% Packing ratio is effective for the applied experimental set-up.

Multivariate roles of litter traits on moisture and flammability of temperate northeastern North American tree species

BackgroundTree litter is the primary fuel affecting surface fire behavior in most fire-prone forest and woodland ecosystems in northeastern North America. Fire exclusion and land use changes have dramatically altered fire regimes in many of these ecosystems, shifting toward species that reinforce the continued diminution of fire. Comparative litter flammability studies provide a useful approach to identify the corresponding leaf and litter bed traits that drive the positive feedback termed “mesophication” in these ecosystems.ResultsIn this study, we measured laboratory flammability and moisture characteristics of leaf litter from 17 common tree species in temperate forests of northeastern North America. Principal components analysis of flammability and moisture data resulted in species separating along a pyrophyte-to-mesophyte gradient. Pyrophytic Quercus litter was the most flammable of all species, and Pinus litter dried the most rapidly and flamed for the longest duration. The remaining mesophytic species grouped into either a high moisture retention-poor burning cluster (Acer rubrum, Betula alleghaniensis, Nyssa sylvatica, Carya glabra, C. tomentosa) or an intermediate flammability cluster (Acer saccharum, C. ovata, Tilia americana). Several litter traits were associated with multivariate factors. Leaf length, density, curling, surface area-to-volume, and litter bed bulk density were associated with the factor comprising saturation moisture content, flaming time, and consumption. Specific leaf area was related to moisture time-lag and flame height. Litter bed packing ratio was most related to smoldering duration.ConclusionsInterspecific variability in leaf and litter bed traits explained substantial differences in moisture dynamics and flammability among the species examined. These findings extend our understanding of litter traits’ influence on the combustion environment and affirm the potential effects of pyrophytic and mesophytic tree species on contemporary fire regimes in northeastern North American forests.

Modeling pressure drop values across ultra-thin nanofiber filters with various ranges of filtration parameters under an aerodynamic slip effect

Computational fluid dynamics simulations of fibrous filters with 56 combinations of different fiber sizes, packing densities, face velocities, and thicknesses were conducted for developing models that predict pressure drops across nanofiber filters. The accuracy of the simulation method was confirmed by comparing the numerical pressure drops to the experimental data obtained for polyacrylonitrile electrospun nanofiber filters. In the simulations, an aerodynamic slip effect around the surface of the small nanofibers was considered. The results showed that, unlike in the case of conventional filtration theory, pressure drops across the thin layers of electrospun nanofiber filters are not proportional to the thickness. This might be a critical factor for obtaining precise pressure drops across the electrospun nanofiber filters with extremely thin layers. Finally, we derived the product of drag coefficient and Reynolds number as a function of packing density, Knudsen number, and ratio of thickness to fiber diameter to get the correlation equation for pressure drop prediction. The obtained equation predicted the pressure drops across the nanofiber filters with the maximum relative difference of less than 15%.

Effect of Ball Size on Performance of Particle Damper

Abstract: The study of particle damping involves the understanding of the various forces and mechanisms that cause energy to be lost from vibrating systems, leading to the reduction of their amplitudes over time. The cantilever beam is used for experimentation. FFT analyzer is used to find the response parameters. The readings are taken with varying cavity dimensions, size of ball, packing ratio and ball material. The response parameters in the form of acceleration are noted by varying one experimental parameter. From the experimentation it is found that 5 mm ball with sand material is effective for the applied experimental set-up.

Modeling and experimental investigation for development of Combined Electrolysis and Catalytic Exchange process for hydrogen isotope separation

A process based on the Combined Electrolysis and Catalytic Exchange (CECE) has been developed for separation of hydrogen isotopes. Indigenously developed Platinum supported on Carbon Aerogel based hydrophobic catalyst was used for hydrogen-water isotope exchange reaction combined with hydrophilic packing in a catalytic exchange column. The exchange column has been studied in detail for various operating conditions in this study. The intrinsic kinetics for the catalytic reaction was determined through spinning basket reactor experiments, and activation energy was estimated to be 29.7 kJ/mol. The overall mass transfer co-efficient in a mixture of hydrophilic packing and hydrophobic catalyst packed bed was determined experimentally in a pilot scale counter current trickle bed reactor to be in the range of 0.15–0.25 s-1 for the proposed range of operating conditions. Individual mass transfer resistances for vapour-liquid mass transfer, external gas mass transfer and gas-solid mass transfer were estimated using widely accepted empirical correlations. Under benchmark operating conditions, the Damkohler number for the exchange column was found to be between 0.10 and 0.16, indicating a significant contribution of surface reaction to overall mass transfer resistance. The vapour liquid mass transfer was found to be fast compared to the gas-vapour reaction. Therefore, increasing hydrophobic catalyst proportion relative to hydrophilic packing was found to increase the overall mass transfer. A steady state process model has been developed to predict the performance of overall exchange process using the experimentally determined parameters. Validation of the process model was carried out using the experimental data generated on the pilot scale trickle bed reactor. Optimum operating conditions of temperature, hydrophobic catalyst to hydrophilic packing ratio, gas and liquid superficial velocities were determined.

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

AbstractDensity is one of the most commonly measured or estimated material properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science, and sustainable cements. Here, three types of machine learning models (i.e., random forest [RF], artificial neural network [ANN], and Gaussian process regression [GPR]) have been developed to predict the room‐temperature density of glasses in the compositional space of CaO–MgO–Al2O3–SiO2–TiO2–FeO–Fe2O3–Na2O–K2O–MnO (CMASTFNKM), based on ∼2100 data points mined from ∼140 literature studies. The results show that the RF, ANN, and GPR models give accurate predictions of glass density with R2 values, root mean square error (RMSE), and mean absolute percentage error (MAPE) of ∼0.95–0.97, ∼0.03–0.04 g/cm3 and ∼0.63%–0.83%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ∼0.28–0.91, ∼0.05–0.15 g/cm3, and ∼1.40%–4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO–Al2O3–SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ∼0.90) with the average metal–oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density–composition relationships from these models (for selected compositional subspaces) suggests that the single‐layer ANN and GPR models exhibit a certain level of transferability (i.e., ability to extrapolate to compositional spaces not [or less] covered in the database) and capture some known features, including the mixed alkaline earth (or alkali) effects.

Improving the anodic packing and harmonizing the proton exchange membrane of bioelectrochemical systems for treating waste gases and generating electricity

Conductive (carbonized porous ceramic ring, CPCR or Cp) and non-conductive filters (porous ceramic ring, PCR or P) were used as anode packing materials in a biotrickling filter microbial fuel cell (BTF-MFC). The optimal packing ratio (2:1 for CPCR: PCR, expressed as Cp2P1) yielded 1.1 times and 1.5 times higher rates of decomposition of acetone than did Cp1P2. Nano zero-valent iron (nZVI) in the proton exchange membrane (PEM) not only reduces the oxygen diffusion rate, but also improves the proton transfer capacity. The tensile strength of the PEM to which was added nZVI (5 mg ml−1) was 1.29 times higher than that of conductive carbon black (CCB) and polyvinyl alcohol (PVA) hydrogel (PVA/CCB-H) before drying and 1.23 times higher after drying. A combination of this composite material and BTF-MFC can be used to treat gaseous pollutants, increasing both removal efficiency and power generation. The microbial population could be increased by controlling the characteristics of the packing materials. On the non-conductive materials near the PEM, the dominant species of bacteria were aerobic, while on the conductive materials far from the PEM, more bacteria were anaerobic, so the filter materials could be distributed to improve the acetone removal capacity and power output of the BTF-MFC.