Compact Method Research Articles

A linearized fourth-order compact ADI method for phytoplankton–zooplankton model arising in marine ecosystem

A linearized fourth-order compact ADI method for phytoplankton–zooplankton model arising in marine ecosystem

Coffee Skin Fiber Biocomposite for Brake Pad Applications

In this research, a biocomposite made from brass, magnesium oxide and polyester is filled with coffee skin powder fibers using a compaction method using a hydraulic press machine as a specimen maker. Biocomposites have variations in coffee skin powder of 10%, 20%, 30%, 40%, 50% by weight and were investigated for the hardness and surface morphology of brake lining. The hardness strength increased with the addition of coffee skin powder and was found to be higher than the matrix. Microscopic observation shows that the biocomposite bonds are not homogeneous. This is indicated by the presence of voids in the biocomposite specimen.

GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes

Matrix–matrix multiplication is of singular importance in linear algebra operations with a multitude of applications in scientific and engineering computing. Data structures for storing matrix elements are designed to minimize overhead information as well as to optimize the operation count. In this study, we utilize the notion of the compact diagonal storage method (CDM), which builds upon the previously developed diagonal storage—an orientation-independent uniform scheme to store the nonzero elements of a range of matrices. This study exploits both these storage schemes and presents efficient GPU-accelerated parallel implementations of matrix multiplication when the input matrices are banded and/or structured sparse. We exploit the data layouts in the diagonal storage schemes to expose a substantial amount of fine-grained parallelism and effectively utilize the GPU shared memory to improve the locality of data access for numerical calculations. Results from an extensive set of numerical experiments with the aforementioned types of matrices demonstrate orders-of-magnitude speedups compared with the sequential performance.

Neural Compact Modeling Framework for Flexible Model Parameter Selection with High Accuracy and Fast SPICE Simulation

Neural compact models are proposed to simplify device‐modeling processes without requiring domain expertise. However, the existing models have certain limitations. Specifically, some models are not parameterized, while others compromise accuracy and speed, which limits their usefulness in multi‐device applications and reduces the quality of circuit simulations. To address these drawbacks, a neural compact modeling framework with a flexible selection of technology‐based model parameters using a two‐stage neural network (NN) architecture is proposed. The proposed neural compact model comprises two NN components: one utilizes model parameters to program the other, which can then describe the current–voltage (I–V) characteristics of the device. Unlike previous neural compact models, this two‐stage network structure enables high accuracy and fast simulation program with integrated circuit emphasis (SPICE) simulation without any trade‐off. The I–V characteristics of 1000 amorphous indium–gallium–zinc‐oxide thin‐film transistor devices with different properties obtained through fully calibrated technology computer‐aided design simulations are utilized to train and test the model and a highly precise neural compact model with an average IDS error of 0.27% and R2 DC characteristic values above 0.995 is acquired. Moreover, the proposed framework outperforms the previous neural compact modeling methods in terms of SPICE simulation speed, training speed, and accuracy.

Machine learning optimization of compact finite volume methods on unstructured grids

Fourier analysis based optimization techniques have been developed for numerical schemes on structured grids and result in significant performance improvements. However, there lacks an effective optimization technique for numerical schemes on general unstructured grids, due to the mesh geometry complexity that makes Fourier transformation infeasible. To address this issue, an optimization framework based on machine learning is developed in this paper for numerical schemes on unstructured grids. The optimization of a compact high-order variational reconstruction on triangular grids is performed to illustrate the framework. An artificial neural network (ANN) is used to predict the optimal values of the derivative weights on cell interfaces that are the free parameters of the variational reconstruction, given the local geometry as input. The ANN is trained by minimizing the solution errors of a linear advection equation on a set of generated compact stencils, with a set of sampled sine waves as initial conditions. The developed optimization framework is applicable to general numerical schemes on unstructured grids as the training does not require explicit computation of dispersion or dissipation. Numerical results for inviscid flow problems show that the optimized variational finite volume scheme is remarkably more accurate and efficient than its non-optimized counterpart, demonstrating the effectiveness of the machine learning optimization.

A higher order compact numerical approach for singularly perturbed parabolic problem with retarded term

In this work, a compact finite difference approach is constructed for singularly perturbed parabolic reaction diffusion problems with a retarded term. The time and space derivatives have been discretized using the θ-method and a compact fourth-order finite difference method on a Shishkin mesh, respectively. Parameter uniform error estimates have been calculated in the L ∞ norm. Some numerical examples have been considered to corroborate the theoretical results and compare the numerical results with existing methods in the literature. It is shown that the present approach provides improved results till date for the problem considered in this paper.

Обзор современных методов уплотнения композиционных материалов

The article discusses various methods of compaction of composite materials: static, vibration, shock, as well as combined action. The designs of mechanisms for compaction are given: a vibrating press, a vibrating plate, a mechanism with a ramming bar, a continuous press for molding products from powdery masses with a connecting rod-rocker impact mechanism, a machine for compacting rigid concrete mixtures, and others. Their principle of operation is considered, advantages and disadvantages are indicated. The analysis of existing compacting methods allows synthesizing the accumulated knowledge in this field, combining them into a single system, finding shortcomings and ways to improve the applied compacting methods in order to increase the productivity of the process, reduce the required drive power of the mechanism, improve the operational characteristics of products. The works of Russian researchers are considered: E.A. Shishkin, A.A. Smolyakov, O.G. Klevtsova, V.I. Ivanchura, A.P. Prokopyev and others. The works of foreign researchers are also considered: Donghai Liu, Ning Wang, Erbo Song, Carl Wersall, K. Rainer Massarsch, Johan Spross and others. The research examines modern methods of compaction, including practical experiments aimed at identifying the most optimal parameters of equipment operation and refining the designs of existing mechanisms. The authors note that many processes related to compaction require additional experimental studies, and the existing methods of compaction are not effective enough and have disadvantages that cannot be eliminated without making significant changes. Thus, it is necessary to apply new methods that meet the requirements of modern technological processes.

A nonstandard compact finite difference method for a truncated Bratu–Picard model

<p>In this paper, we used the nonstandard compact finite difference method to numerically solve one-dimensional truncated Bratu-Picard equations and discussed the convergence analysis of the proposed method. Depending on the parameters in the mentioned equation, it may have no solution, one solution, or two solutions; also, it may have infinitely many solutions. The numerical results show that our method covers all mentioned aspects depending on the parameters in the equation.</p>

An explicit fourth-order accurate compact method for the Allen-Cahn equation

<abstract><p>In this paper, we propose an explicit spatially fourth-order accurate compact scheme for the Allen-Cahn equation in one-, two-, and three-dimensional spaces. The proposed method is based on the explicit Euler time integration scheme and fourth-order compact finite difference method. The proposed numerical solution algorithm is highly efficient and simple to implement because it is an explicit scheme. There is no need to solve implicitly a system of discrete equations as in the case of implicit numerical schemes. Furthermore, when we consider the temporally accurate numerical solutions, the time step restriction is not severe because the governing equation is a second-order parabolic partial differential equation. Computational tests are conducted to demonstrate the superior performance of the proposed spatially fourth-order accurate compact method for the Allen-Cahn equation.</p></abstract>

Grain alignment in hexaferrite permanent magnets by compaction at room and elevated temperatures.

Shape-controlled precursors enable grain alignment without using an applied magnetic field in the permanent magnet material strontium hexaferrite. The effect is investigated by conducting four series of experiments using different compaction methods: two cold and two hot compactions. The hypothesis is that magnetic short-circuiting will diminish the grain alignment (texture) in cold compacted samples. The two cold compactions were performed below the Curie temperature, one being a simple cold-pressing (CP) and the other being a cold-pressing followed by sintering (CPS). The CP samples showed a relatively weak texture and low remanent magnetisation, while the CPS method yielded a slightly sharpened texture and improved remanent magnetisation compared with CP. In the two hot compactions, the pressing was done above the Curie temperature, using spark plasma sintering (SPS) and induction pressing (IP). SPS and IP samples both showed remarkably sharper textures than the cold-pressed pellets, with roughly doubled texture strength, and consequently better remanent magnetisation. The bulk density of the hot compacted samples increased almost twofold compared with that of the cold compactions. Hot compaction outperforms cold compaction with regard to density, sharpness of texture and remanent magnetisation. This is explained by inter-particle magnetic short-circuiting during cold pressing, which hinders the alignment process and reduces the bulk density.

Attractive solutions for Hilfer fractional neutral stochastic integro-differential equations with almost sectorial operators

<abstract><p>This paper studies the integro-differential equations of Hilfer fractional (HF) neutral stochastic evolution on an infinite interval with almost sectorial operators and their attractive solutions. We use semigroup theory, stochastic analysis, compactness methods, and the measure of noncompactness (MNC) as the foundation for our methodologies. We establish the existence and attractivity theorems for mild solutions by considering the fact that the almost sectorial operator is both compact and noncompact. Example that highlight the key findings are also provided.</p></abstract>

A Compact Constraint Incremental Method for Random Weight Networks and Its Application.

Incremental random weight networks (IRWNs) face the issues of weak generalization and complicated network structure. There is an important reason: the learning parameters of IRWNs are determined in a random fashion without guidance, which may increase many redundant hidden nodes, and thereby leading to inferior performance. To resolve this issue, a novel IRWN with compact constraint that guides the assignment of random learning parameters (CCIRWN) is developed in this brief. Using the iteration method of Greville, a compact constraint that simultaneously assures the quality of generated hidden nodes and the convergence of the CCIRWN is built to perform learning parameter configuration. Meanwhile, the output weights of the CCIRWN are assessed analytically. Two types of learning methods for constructing the CCIRWN are proposed. Finally, the performance evaluation of the proposed CCIRWN is undertaken on the 1-D nonlinear function approximation, several real-world datasets, and data-driven estimation based on the industrial data. Numerical and industrial examples indicate that the proposed CCIRWN with compact structure can achieve favorable generalization ability.

Stability and convergence analysis for a uniform temporal high accuracy of the time-fractional diffusion equation with 1D and 2D spatial compact finite difference method

<abstract><p>The 1D and 2D spatial compact finite difference schemes (CFDSs) for time-fractional diffusion equations (TFDEs) were presented in this article with uniform temporal convergence order. Based on the idea of the modified block-by-block method, the CFDSs with uniform temporal convergence order for TFDEs were given by combining the fourth-order CFDSs in space and the high order scheme in time. The stability analysis and convergence order of CFDSs with uniform convergence order in time for TFDEs strictly proved that the provided uniform accuracy time scheme is $ (3-\alpha) $ temporal order and spatial fourth-order, respectively. Ultimately, the astringency of 1D and 2D spatial CFDSs was verified by some numerical examples.</p></abstract>

Uniform Error Bounds of a Conservative Compact Finite Difference Method for the Quantum Zakharov System in the Subsonic Limit Regime

Uniform Error Bounds of a Conservative Compact Finite Difference Method for the Quantum Zakharov System in the Subsonic Limit Regime

紧致有限差分方法求解全离散波动方程

紧致有限差分方法求解全离散波动方程

Cone-beam computed tomographic evaluation and fracture resistance of endodontically retreated teeth using hyflex remover, Mtwo, and ProTaper retreatment file systems: An in vitro study

Introduction: One of the important factors contributing to the success of nonsurgical endodontic retreatment is the efficient removal of the obturating material from the root canal space. However, the fracture resistance of the tooth is likely to be affected by this procedure. Therefore, the study envisages assessing residual filling material in the root canals and the tooth’s resistance to fracture postretreatment using different retreatment files. Materials and Methods: Ninety extracted mandibular premolars with one root and a single canal were decoronated, followed by chemo-mechanical preparation, and thereafter, using the lateral compaction method, root canals were obturated. The samples were divided into three groups (n = 30) in accordance with retreatment file systems, namely Hyflex Remover, Mtwo, and ProTaper. All three groups were subjected to the cone-beam computed tomography evaluation to assess remnants of obturating material inside the canals postretreatment, and the same samples were further assessed for fracture resistance, respectively. The collected data were evaluated using the statistical analysis using the Kruskal–Wallis and post hoc Bonferroni tests. Results: There were significant differences in the volume of residual filling among the tested groups in the cervical thirds (P < 0.05). The HyFlex remover file showed the highest fracture resistance (427.30 ± 33.53 N), followed by the Mtwo R (396.90 ± 27.17 N) and ProTaper R (378.50 ± 36.82 N). Conclusion: All the tested nickel-titanium retreatment files performed satisfactorily. Hyflex remover left the least amount of filling material in the cervical third and displayed the highest fracture resistance among the respective groups.

Exploring the Mpemba effect: a universal ice pressing enables porous ceramics.

Piezoceramics with global porosity and local compaction are highly desired to exploit the combination of mechanical and electrical properties. However, achieving such a functional combination is challenging because of the lack of techniques for applying uniform pressure inside porous ceramic green parts. Nature provides many examples of generating strong forces inside the macro and micro channels via the state transformation of water. Inspired by these phenomena, we present a technique of "ice and fire", that is, water freezing (ice pressing) and high-temperature sintering (fire), to produce ideal porous piezoceramics. We introduce a new compaction method called the "ice pressing method", which manipulates liquid phase transition for compaction. This method has several advantages, including uniform pressure distribution, a wide pressure range, high effectiveness, and selective freezing. It can generate an ultrahigh pressure of up to 180 MPa on the piezoceramic green skeletons in minutes while retaining their functional pore structures. By exploiting the Mpemba phenomenon, we further accelerate the compaction procedure by 11%. The first ice-pressed and second fire-consolidated lead zirconate titanate (PZT) ceramics are highly densified and exhibit an outstanding piezoelectric response (d33 = 531 pC N-1), comparable to conventional pressed bulk counterparts and 10-20 times higher than those of unpressed materials. The novel ice pressing method breaks the limitation of lacking a compaction technique for porous ceramics. The versatile and effective ice pressing method is a green and low-cost route promoting applications in sensors, acoustics, water filtration, catalyst substrates, and energy harvesting.

Effervescent Formulation of Oil Palm Leaf Extract as a Free Radical Scavenger

Non-communicable diseases have become the leading cause of death in Indonesia, accounting for approximately 69% of deaths in 2023. Various health disorders, such as cancer, neurodegenerative diseases, inflammation, hypertension, and heart disease, are linked to oxidative stress caused by reactive oxygen species (ROS). Chemical treatments often have side effects and high costs, prompting many to turn to natural remedies. One preventive measure against ROS is consuming natural foods rich in antioxidants, such as oil palm leaf extract. This study uses the roller slugging compaction method to transform oil palm leaf extract into effervescent tablets. Palm oil leaf extract has been prepared in 2 different sources ( 1st and 17th leaf) to assess percent inhibition. The experimental results showed that the 17th leaf extract had the highest antioxidant activity value (IC50 = 20.35). Then, the 17th palm oil leaf extract was used in effervescent tablet formulations. The results obtained indicate that formulation 1, consisting of 0.5 grams of oil palm leaf extract, 0.195 grams of citric acid, 0.2925 grams of tartaric acid, 0.4875 grams of NaHCO3, 0.975 grams of maltodextrin, and 0.05 grams of PVP, is the best formula for oil palm leaf extract effervescent tablets. This formulation demonstrated the highest antioxidant activity (IC50 = 30.22), consistent with its total phenolic content (394.451 mg GAE/mL).

Parametric Study on The Bearing Capacity of Down-hole Dynamic Compaction

The DDC method is well known as Down-Hole Dynamic Compaction, an effective ground treatment method. DDC combines dynamic compaction and soil replacement methods to enhance bearing capacity, reduce settlement, minimize the potential for collapse, and mitigate liquefaction. DDC has been commonly used to stabilize collapsible soil, DDC basically forms a column inside the soil stratum which is similar to a stone column except DDC materials are put in sequence and then compacted by using DDC hammer, this is known as the self-tamping method. DDC is considered as a suitable method for soft soil improvement. This study modeled DDC as reinforcement for runway area which is predominantly soft soil in the study location. DDC is modeled with various dimension and spacing to assess its impact on bearing capacity and settlement, thereby simplifying the selection of suitable dimension during installation. The results show that the stress induced by external loads on Down-hole Dynamic Compaction (DDC) rises in correlation with the spacing between DDC installations. The peak stress was documented on a DDC unit with a 1-meter diameter positioned at a distance of 2.5 meters from the other DDC units, measuring 83.9 kN/m2. The highest stress level was recorded in the soil surrounding a DDC unit with a 1.5-meter diameter, which was positioned 3 meters away from other DDC units, measuring 157.89 kN/m2. The highest bearing capacity was achieved when a DDC with a diameter of 1.5 meters was positioned at a distance of 3 meters, resulting in a bearing capacity value of 1407.32 kN/m2.

Hydrothermal characteristics of ferrofluid in a wavy chamber with magnetic field-dependent viscosity: Effects of moving walls

The present investigation aims to scrutinize the role of different moving walls on the hydrothermal characteristics and thermal performance of a Fe3O4-H2O ferrofluid within a wavy chamber under mixed convection. Notably, the study also takes into consideration the effects of a magnetic field and internal heat generation or absorption phenomena. To achieve this, a numerical simulation employing a compact finite difference method is conducted, focusing on key factors such as the Richardson number (0.1≤Ri≤100), Hartmann number (0≤Ha≤30), magnetic orientation (00≤γ≤900), Grashof number (Gr=104), solid concentrations (0≤ϕnp≤0.04), magnetic number (0≤δ0≤1), Prandtl number (Pr=6.8377), and internal heat generation parameter (−2≤Q≤2). All of this is undertaken while carefully managing the ferromagnetic particle volume fraction. The primary aim is to assess how different types of moving walls affect the transport behavior of the ferrofluid and to determine the impacts of the magnetic field on the hydrothermal characteristics of the ferrofluid. The outcomes of this investigation reveal that the presence of moving walls leads to a non-uniform temperature distribution within the ferrofluid, significantly affecting the overall transport behavior. The addition of ferrofluid particles to the base fluid results in noteworthy enhancements in the average Nusselt numbers, signifying improved convective heat transfer efficiency. Specifically, Case-I experienced an increase of up to 3.21%, Case-II showed an improvement of 0.93%, Case-III exhibited a rise of 1.70%, and Case-IV demonstrated the highest enhancement of 20.71%. Furthermore, the inclusion of magnetic field-dependent viscosity and the internal heat generation coefficient significantly influences the behavior of the ferrofluid. The findings from this study hold paramount importance for the design and optimization of heat transfer systems that employ ferrofluids and have the potential to contribute to the development of novel technologies with a wide range of industrial applications.