Packing Method Research Articles

Comprehensive study on parabolic trough solar receiver-reactors via a novel efficient optical-thermal-chemical model of catalyst packed bed characteristics

The catalyst packed bed structure plays an important role in the promising solar-driven catalyst packed bed based thermochemical hydrogen production systems. However, corresponding accurate yet efficient numerical methods for characterizing and optimizing the catalyst packed bed structure as well as the whole complex solar-driven thermochemical process is still urgently needed. In this paper, a novel three-dimensional comprehensive model is proposed for the hydrogen production of methanol steam reforming reaction (MSRR) in parabolic trough solar receiver-reactors (PTSRR). A relatively realistic porosity distribution obtained by the discrete element method was combined with the optical-thermal-chemical coupling model. The proposed model was validated, proved to improve the accuracy of numerical predictions in a more efficient way. Moreover, it was effectively applied to tune the overall energy flow of the complex optical-thermal-chemical process of PTSRR-MSRRs for better performance, by optimizing the catalyst packed bed structure in aspects of catalyst sizes and packing methods. The results revealed that single packed beds of larger catalyst particles show better performance within the selected catalyst size range. The radial layered packing method of larger particle-to-particle diameter ratios can bring higher energy conversion efficiency and lower flow resistance, as compared to the single packing method and fully mixed packing method. This study could provide an efficient numerical modelling method and significant guidance for other similar solar-driven thermochemical applications of catalyst packed beds.

Tribological Evaluation of Boride Layers Formed on an AISI M2 Steel Substrate by the Powder Packing Method.

Tribological Evaluation of Boride Layers Formed on an AISI M2 Steel Substrate by the Powder Packing Method.

Matrix design and performance development of eco-friendly high-strength, lightweight engineered cementitious composites

This study develops a novel eco-friendly high-strength, lightweight engineered cementitious composite (EHLECC) using lightweight sand fines (LSF) and fly ash cenospheres (FAC) based on the theory of micromechanics, dense packing theory and method of performance driven design. The mechanical properties, microstructures, and micromechanism of EHLECC are examined. The results indicate that the accumulation compactness between different particles in EHLECCs is optimized due to the appropriate particle size range brought by LSF and FAC. LSF and FAC slightly reduced the mechanical strength of EHLECC matrix but had a significant weight reduction effect for EHLECC. All EHLECCs exhibited remarkable mechanical properties with the tensile strain exceeding 6%, tensile stress more than 10 MPa, and compressive strength surpassing 80 MPa. Moreover, the incorporation of LSF enhanced the degree of saturated cracking behavior in EHLECC, resulting in better ductility. Furthermore, LSF substitution rate of 75% led to the optimum matrix design. This study provides a new approach for designing and producing an EHLECC, which would be a promising building material for load-bearing structures and construction reinforcement.

An Examination of Packing Methods for Grafts to Prevent Freezing Injury during Transportation for Liver Transplantation.

University of Wisconsin solution (UW) may freeze at temperatures below -0.7 °C, damaging the graft. The present study assessed the effectiveness of the liver graft package protocol, which recommends filling a package with sufficient liquid to prevent grafts from sustaining freezing injury. We filled ice cubes at two temperatures (-80 and -20 °C) around packages and performed a comparative study with four groups based on the temperature and filling of the second layer with lactated Ringer's solution (LR) (A: -80 °C, LR-; B: -80 °C, LR+; C: -20 °C, LR-; D: -20 °C, LR+). The bovine liver was used as a graft and preserved for 6 h in the first isolation bag filled with UW. While temperatures dropped below -0.7 °C at some points for 6 h in groups A, B, C, they never dropped to -0.7 °C in group D. The macroscopic findings in groups A, B, C showed freezing of the UW and grafts, but no such results in group D. A pathological study including electron microscopy showed freezing injury in groups A, B, and C but no significant changes in group D. The graft package protocol prevents freezing of the UW and liver grafts.

Efficacy of Balloon Tamponade in Postpartum Hemorrhage

Aim: An estimated loss of more than 500 mL of blood after vaginal delivery and more than 1000 mL of blood following caesarean delivery from the birth canal within 24 hours is considered primary postpartum haemorrhage (PPH). Rightfully termed the "obstetrician's nightmare," primary postpartum haemorrhage remains a major contributor to maternal mortality and morbidity in underdeveloped countries like Pakistan, where it accounts for 21-31% of maternal deaths. As a result, investigating PPH management strategies is vital. Objectives: To determine the efficacy of intrauterine balloon tamponade in the management of post-partum hemorrhage Study Design: Descriptive study Study Setting: Department of Obstetrics and Gynecology, Sheikh Zayed Medical from 21st May 2021 till 20th November 2021. Methods: Total 126 women aged 18-35 years, parity 1-6 and 31-41 weeks of gestation, who develop atonic post-partum hemorrhage not responding to uterotonics following normal vaginal delivery or caesarean section were enrolled using non-probability consecutive sampling technique. Women having postpartum hemorrhage were treated with balloon tamponade to stop bleeding. Efficacy was noted after 06 hours of procedure. Data was entered and analyzed using SPSS 25. Results: In this study total 126 patients were enrolled with mean age of 25.6±5.3 years. Mean gestational age was 38.2±3.2 weeks. Mean blood loss was 1032.1±379.6 ml. Mean parity was4.1±1.3. Most of patients had middle education i.e. 33.3%. Majority of patients belonged to middle socioeconomic status i.e. 47.6%. There were 41.3% patients who were booked and 2 had regular antenatal visits. Vaginal delivery was done in 55.6% patients. Balloon tamponade was effective in 81% patients. Practical Implication: Since balloon tamponade is a relatively new procedure, there is little information about its efficacy compared to the more traditional method of uterovaginal packing in Pakistan. Conclusion: Balloon tamponade is effective in stopping postpartum hemorrhage. The success rate (81%) of balloon tamponade in preventing PPH is high. It's convenient for use in situations with a higher risk of PPH. Keywords: Postpartum Hemorrhage, Balloon Tamponade, Delivery, Efficacy, Blood Loss, Management

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Surface relaxivity estimation of coals using the cutting grain packing method for coalbed methane reservoirs

Low-field nuclear magnetic resonance (NMR) has been widely used to express the pore-size distributions of conventional and unconventional reservoirs. Based on the equation 1T2=ρFs1r, the determination of the surface relaxivity ρ can help to quantitatively convert the transverse relaxation time T2 to pore radius (r). The existing calculation of ρ using NMR requires supplementary and expensive destructive or non-destructive methods, and consolidated cylinders are required as samples to be well tested. However, the operation of multiple drilling projects and the production of many drilling cuttings are barriers to making rapid measurement of the ρ value of the unconsolidated coal cuttings challenging. In this study, an NMR grain packing method is proposed using different coal grains. The surface-to-volume ratio was transformed into the ratio of the derivative of an arbitrary pore volume to its volume, and the governing NMR equation was established by considering only the NMR porosity and T2 distribution. Sphere and cubic-like models were also constructed. The results showed that the coal with different grain sizes had different relaxation responses, and the different assemblies with the same wgrain/wgrain,c also exhibited some relaxation differences, which might be affected by the diversity in grain packing patterns and grain morphology, resulting in the variation in porosity. In addition, the water film distribution on the grain surface and the soluble minerals in the coal contribute to the variation in ρ values by influencing the relaxation properties of the voids between grains and the pores in the grains. The T2 spectrum indicated that the macropores contributed the most to the porosity, owing to the loose grain packing, and the larger grains might not strictly obey the fast diffusion limit. For the sphere-like model, the ρ values of the Ji15 and Ji17 coals were in the range of 19.47–44.04 μm/s and 36.50–46.66 μm/s, respectively; whereas for the cubic-like model, the corresponding ρ values of these two coals ranged from 24.16 μm/s to 54.64 μm/s and from 45.29 μm/s to 57.90 μm/s, respectively. Finally, different methods of ρ calculation are compared, and its potential field application is discussed.

Analysis of Diffusion Coefficients of Iron Monoboride and Diiron Boride Coating Formed on the Surface of AISI 420 Steel by Two Different Models: Experiments and Modelling.

In the present work, two mathematical diffusion models have been used to estimate the growth of the iron monoboride and diiron boride coating formed on AISI 420 steel. The boronizing of the steel was carried out with the solid diffusion packing method at a boronizing temperature of 1123 K-1273 K. Experimental results show the two-coating system consists of an outer monoboride and an inner diiron boride coating with a predominantly planar structure at the propagation front. The depth of the boride coating increases according to temperature and treatment time. A parabolic curve characterizes the propagation of the boride coatings. The two proposed mathematical models of mass transfer diffusion are founded on the solution corresponding to Fick's second fundamental law. The first is based on a linear boron concentration-penetration profile without time dependence, and the second model with time dependence (exact solution). For both models, the theoretical law of parabolic propagation and the average flux of boron atoms (Fick's first fundamental law) at the growth interfaces (monoboride/diiron boride and diiron boride/substrate) are considered to estimate the propagation of the boride coatings (monoboride and diiron boride). To validate the mathematical models, a programming code is written in the MATLAB program (adaptation 7.5) designed to simulate the growth of the boride coatings (monoboride and diiron boride). The following parameters are used as input data for this computer code: (the layer thicknesses of the FeB and Fe2B phases, the operating temperature, the boronizing time, initial formation time of the boride coating, the surface boron concentration limits, FeB/Fe2B and Fe2B/Fe growth interfaces, and the mass transfer diffusion coefficient of boron in the iron monoboride and diiron boride phases). The outputs of the computer code are the constants εFeB and εFe2B. The assessment of activation energies of AISI 420 steel for the two mathematical models of mass transfer is coincident (QFeB=221.9 kJ∙mol-1 and QFe2B=209.1 kJ∙mol-1). A numerical analysis was performed using a standard Taylor series for clarification of the proximity between the two models. SEM micrographs exhibited a strong propensity toward a flat-fronted composition at expansion interfaces of the iron monoboride and diiron boride coating, confirmed by XRD analysis. Tribological characterizations included the Vickers hardness test method, pin-on-disc, and Daimler-Benz Rockwell-C indentation adhesion tests. After thorough analysis, the energies were compared to the existing literature to validate our experiment. We found that our models and experimental results agreed. The diffusion models we utilized were crucial in gaining a deeper understanding of the boronizing behavior of AISI 420 steel, and they also allowed us to predict the thicknesses of the iron monoboride and diiron boride coating. These models provide helpful approaches for predicting the behavior of these steels.

Transport conditions in the planktonic phase for seahorses Hippocampus reidi Ginsburg, 1933 (Teleostei: Syngnathidae)

Thousands of seahorses are traded every year as marine ornamental species. Although packing methods for live adult seahorses have already been addressed, there is no information on the transportation of juvenile seahorses. Thus, this study aimed to evaluate the possibility of packing and transportation for newly-birthed seahorses. Three batches of newborn long-snout seahorses Hippocampus reidi were transported in three independent events from Ubatuba, north coast of São Paulo, to the city of São Paulo, covering 248 km in each trip, with an average duration of 3.5 h. A mean of 350 offspring of one day old was distributed in 1 L containers with round borders at equal densities, provided with constant aeration by portable pumps. Aeration was kept so that large bubbles would create an ascending flow keeping juveniles in suspension throughout transportation. Values of survival achieved in this study, even after 10 days post-transportation, were surprisingly high, reaching over 70%. No significant shift in water temperature, pH, or salinity was observed for any transportation events. For oxygen levels, although portable pumps were used, a drop of around 15% in DO level was recorded. In view of the trending ornamental fish market, considering the limited swimming capacity of newly born seahorses and that a great number of marine fish undergo a planktonic phase, this study provides important directions to a better understanding on seahorse transportation and yet, directions for the design of new transport methodology for an efficient shipping protocol for other species.

Convolutions in Overdrive: Maliciously Secure Convolutions for MPC

Machine learning (ML) has seen a strong rise in popularity in recent years and has become an essential tool for research and industrial applications. Given the large amount of high quality data needed and the often sensitive nature of ML data, privacy-preserving collaborative ML is of increasing importance. In this paper, we introduce new actively secure multiparty computation (MPC) protocols which are specially optimized for privacy-preserving machine learning applications. We concentrate on the optimization of (tensor) convolutions which belong to the most commonly used components in ML architectures, especially in convolutional neural networks but also in recurrent neural networks or transformers, and therefore have a major impact on the overall performance. Our approach is based on a generalized form of structured randomness that speeds up convolutions in a fast online phase. The structured randomness is generated with homomorphic encryption using adapted and newly constructed packing methods for convolutions, which might be of independent interest. Overall our protocols extend the state-of-the-art Overdrive family of protocols (Keller et al., EUROCRYPT 2018). We implemented our protocols on-top of MP-SPDZ (Keller, CCS 2020) resulting in a full-featured implementation with support for faster convolutions. Our evaluation shows that our protocols outperform state-of-the-art actively secure MPC protocols on ML tasks like evaluating ResNet50 by a factor of 3 or more. Benchmarks for depthwise convolutions show order-of-magnitude speed-ups compared to existing approaches.

Cushioning Performance of the Biomimetic Cobweb Cushioning Silicone Pad.

At present, the packing method of "plastic bag-buffer packing-packing paper box" is adopted for bearing packaging. However, the common packing method has a poor packing effect and poor versatility. In this study, a new biomimetic cobweb cushion is proposed to solve the problem of insufficient cushioning capacity of high-precision bearing cushion packaging pads. First, according to the nature of cobweb form, the cobweb cushion structure configuration is determined. Next, based on the structure of the cushion and the relationship between the parameters of radial thread and spiral thread, a mechanical and target optimization model is established. The stress nephogram of bearing and the cobweb cushion are analyzed under three drop heights of 381, 610, and 700 mm, in the finite element simulation software to ensure that the maximum bearings stress is not beyond the material yield strength. Via the 3D printing technology, a cobweb cushion shell cast is made. Drop tests of the bearing were performed, and the results were verified with the finite element simulation analysis. This research can provide technical support for the protection of high-precision bearings from accidental drops during transportation.

A hybrid soft material robotic end-effector for reversible in-space assembly of strut components.

Based on the NASA in-Space Assembled Telescope (iSAT) study (Bulletin of the American Astronomical Society, 2019, 51, 50) which details the design and requirements for a 20-m parabolic in-space telescope, NASA Langley Research Center (LaRC) has been developing structural and robotic solutions to address the needs of building larger in-space assets. One of the structural methods studied involves stackable and collapsible modular solutions to address launch vehicle volume constraints. This solution uses a packing method that stacks struts in a dixie-cup like manner and a chemical composite bonding technique that reduces weight of the structure, adds strength, and offers the ability to de-bond the components for structural modifications. We present in this paper work towards a soft material robot end-effector, capable of suppling the manipulability, pressure, and temperature requirements for the bonding/de-bonding of these conical structural components. This work is done to investigate the feasibility of a hybrid soft robotic end-effector actuated by Twisted and Coiled Artificial Muscles (TCAMs) for in-space assembly tasks. TCAMs are a class of actuator which have garnered significant recent research interest due to their allowance for high force to weight ratio when compared to other popular methods of actuation within the field of soft robotics, and a muscle-tendon actuation design using TCAMs leads to a compact and lightweight system with controllable and tunable behavior. In addition to the muscle-tendon design, this paper also details the early investigation of an induction system for adhesive bonding/de-bonding and the sensors used for benchtop design and testing. Additionally, we discuss the viability of Robotic Operating System 2 (ROS2) and Gazebo modeling environments for soft robotics as they pertain to larger simulation efforts at LaRC. We show real world test results against simulation results for a method which divides the soft, continuous material of the end-effector into discrete links connected by spring-like joints.

Improving Seed-Based FPGA Packing with Indirect Connection for Realization of Neural Networks

FPGAs are gaining favor among researchers in fields including artificial intelligence and big data due to their configurability and high level of parallelism. As the packing methods indisputably affect the implementation performance of FPGA chips, packing techniques play an important role in the design automation flow of FPGAs. In this paper, we propose a quantitative rule for packing priority of neural network circuits, and optimize the traditional seed-based packing methods with special primitives. The experiment result indicates that the proposed packing method achieves an average decrease of 8.45% in critical path delay compared to the VTR8.0 on Koios deep learning benchmarks.

Study on liquid dispersion characteristics in trickle bed reactor based on image processing

The liquid flow and distribution are key factors in determining the performance of a trickle bed reactor. It is highly challenging to examine the characteristics of liquid dispersion due to the complexity of the liquid flow mechanism in the trickle bed reactor. In this study, experimental research was performed to determine the impacts of gas/liquid velocity, particle packing method, and liquid properties on the characteristics of liquid dispersion in a pseudo-two-dimensional trickle bed reactor. A high-speed camera system was utilized to record the liquid flow and distribution in the reactor. An image processing method was proposed to obtain the particle surface wetting efficiency for quantifying the liquid distribution in the reactor. By combining liquid distribution images (qualitative information) and particle surface wetting efficiency (quantitative information) in the reactor under different conditions, it is feasible to evaluate the liquid dispersion in the trickle bed reactor in great detail.

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Mesomechanical Aspects of the Strain-Rate Sensitivity of Armco-Iron Pulled in Tension

The phenomenon of the strain-rate sensitivity of metallic materials has been a topic of interest since the first mechanical tests at different strain rates were performed. The problem of its theoretical description appeared simultaneously. Despite the significant number of studies covering this issue, it is necessary to rule out a few drawbacks of previously reported models, which is the goal of this work. Herein, an extension of the elastic–viscoplastic model to a generalized state of stress is proposed while aiming to describe the strain rate sensitivity of Armco-iron samples that were pulled in tension within the framework of the finite-difference method. A mathematical model was formulated using equivalent stress and strain, which alleviated the complexity of the relaxation-type constitutive equations. The critical resolved shear stress (CRSS) function describes S-type instability with a single equation. The plastic strain rate was calculated based on the well-known Orowan equation, which is related to dislocation dynamics. In addition, the model took the material’s microstructure into account based on the design of a representative volume element (RVE) using the step-by-step packing (SSP) method. The results of the modeling were compared with the available experimental data and were found to satisfactorily correlate with them. The results suggest that the misfit error between the model and experimental data did not exceed 10% in the range of strain rates under study, which is a reliable outcome.

Mix design method of granular packing for high performance concrete with natural pozzolans

This study presents an enhanced mix design method for High performance concrete (HPC) with Natural Pozzolans (NP) content that considers material packing theory. Seven HPC mixes with different NP replacement levels of 0, 5, 10, 15, 20, 25, 30% by volume were conducted to investigate the effect of NP on the rheological and mechanical properties of HPC. A basic water demand experiment was designed to obtain the granular packing and packing density of mortar. Furthermore, the equivalent packing density was defined and calibrated to represent the effect of NP, And the bilinear interpolation method was used to analyses the HPC properties. The results show that the relative accuracies of the improved method are higher than 15% to the initial method. The HPC mixtures with NP was also assessed and shown to be within the optimum area.

Economical Olive Oil Production by Reduction of Post-Harvest Losses in Olive Fruit

The higher cost of oil extraction units and their shortage at the district level results in huge economic losses for olive farmers. Olive fruit harvesting and packing method for temporary storage and transportation cause major losses to the growers due to the reduction in the quality of olive oil. Poor economic returns from lower-quality olive oil result in financial losses to the growers. Fruit harvesting and packing method are major sources of fruit losses; hence the studies were designed to standardize commercial fruit harvesting and packing method to minimize the losses. The study was conducted at Buzdar Agricultural Farm, DG Khan. The fruit was packed in plastic bags (Conventional method) and perforated fruit baskets (Non-conventional method) for transportation from the olive orchard to the Oil Mill (Barani Agricultural Research Institute, Chakwal) for extraction of olive oil. Olive oil extraction machine Model (Pieralisi, Italy) and the first cold extraction were used for data collection. Minimum fruit damage of 1.14% was recorded in hand picking, and maximum fruit damage of 10.65% was recorded in stick beating. Maximum fruit rotting of 4.48% was recorded in stick beating, and minimum damage of 1.10% was recorded in the hand picking method. Oil recovery percentage was not affected by harvesting and packing methods; however, oil quality was highly affected by harvesting and packing methods, which resulted in economic returns from produced olive oil. Poor quality oil fetched minimum price as compared with premium quality olive oil, which resulted in financial losses to the grower.

A hybrid biogeography-based optimization algorithm for three-dimensional bin size designing and packing problem

The three-dimensional bin packing problem (3D-BPP) plays a critical role in logistics activities. Prior studies focus on 3D-BPP in cases where bin size is predetermined. However, bin volume utilization can be improved significantly by optimizing bin size. In this study, we propose a novel three-dimensional bin size designing and packing problem (3D-BSDPP) and a hybrid biogeography-based optimization (HBBO) algorithm to address the problem by maximizing bin volume utilization by optimizing the bin size. The HBBO combines a differential evolution algorithm with basic biogeography-based optimization to improve exploration ability, and a novel local search strategy involving three specific local search operators is proposed to accelerate the HBBO convergence. Finally, a novel heuristic packing method is employed to find a high-quality packing solution. The proposed algorithm is evaluated through experiments carried out on a practical bin packing application. The results indicate that the proposed algorithm outperforms existing algorithms in solving the 3D-BSDPP.

Pore-scale numerical investigation on the thermal storage properties of packed-bed systems with phase change material microcapsules

Phase change material (PCM) microcapsules are gaining increasing attention because it can overcome the shortage of PCM macrocapsules and increase the heat transfer area, while the pore-scale thermal performance of the PCM microcapsule packed-bed systems, as well as the influence of packing methods and microcapsule sizes on thermal storage, is still poorly understood. This paper introduces various spherical PCM microcapsule pack models, including single-layered, diameter-changed two-layered, and three-layered packing models, to investigate the charging process at pore scale. The indices of thermal storage properties such as charging time, temperature distribution, phase change fraction, pressure drop and thermocline degradation are studied with the finite element method. In the single-layered thermal storage system, the results show that the charging process can be improved by reducing the capsule size and using cross-packing methods. When the capsule size is reduced from 2 to 0.5 mm with cross packing, the charging time can be shortened by 72.97%, but remarkable pressure drop is created. The results indicate that the overall thermal storage performance of multi-layered packed-bed systems is better than that of single-layered packed-bed systems. The charging time of the diameter-changed two-layered and three-layered thermal storage systems can be reduced up to 16.21% and 24.32%, respectively, compared to the single-layered in-line pack with a d = 2 mm. The filling sequence plays a key role in multi-layered thermal systems. A packed-bed system with a diameter decreasing along the heat transfer fluid (HTF) flow direction has a better capacity to reduce thermocline degradation than other packed-bed systems.