Characteristics Of Layer Research Articles

Influence of back pressure adjustment of porous media on cavity flow noise control

Control of self-sustained oscillation and noise reduction poses a significant challenge. The present study employs Implicit Large Eddy Simulation at a Mach number of 0.85 to investigate the influence of a porous cavity floor on flow dynamics. By substituting the solid floor with porous media, the fundamental pressure–velocity relationship within the medium is established according to Darcy's law. Findings reveal marked suppression of wall pulsations, accompanied by a 10 dB decrease in sound pressure levels. The porous medium induces blowing and suction effects, effectively modulating large-scale re-circulation and mitigating shear layer instability, thereby approximating free mixing layer characteristics and suppressing cavity flow oscillations. At an optimized porosity for maximum noise reduction, altering back pressure at the cavity floor induces a transition in the local flow regime from suction-dominated to blowing-dominated state. Excessive reduction of back pressure promotes suction; conversely, increased pressure intensifies blowing, further attenuating feedback mechanisms and enhancing noise reduction. To explore noise reduction mechanisms, mode decomposition analyses demonstrate the efficacy of porous media in disrupting large-scale coherence structures within shear layer and redistributing energy from dominant modes to a broader frequency spectrum that engages smaller flow structures. This energy reallocation mechanism contributes to the mitigation of cavity flow noise and deepens insights into the role of porous media in flow modulation and noise control.

Layer Characteristics on Quartz and Feldspar Bed Particles in an Industrial-Scale Fast Pyrolysis Process of Woody Biomass

Layer Characteristics on Quartz and Feldspar Bed Particles in an Industrial-Scale Fast Pyrolysis Process of Woody Biomass

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

A new contact force model for revolute joints considering elastic layer characteristics effects

The contact forces generated during the motion of revolute joints with clearances significantly impact the dynamic performance of mechanisms, making it essential to develop an accurate contact force model. Traditional contact models often neglect the influence of elastic layers or assume that the bushing thickness is equal to its radius, which limits their applicability. To address the dynamic contact problem in revolute joints with small clearances, this study employs an improved Winkler elastic foundation model and introduces an elastic layer correction coefficient to refine the static contact model. The model's validity is verified using the finite element method. Building on this, a new contact force model is proposed, incorporating the optimal damping term from multiple continuous contact models. Finally, experimental validation is conducted using the crank-slider mechanism and typical applications in the Variable Stator Vane (VSV) mechanism. The results demonstrate that the proposed contact force model effectively predicts the dynamic characteristics of mechanisms with clearances.

Calculation Modeling of Adsorbed and Bulk-Phase Oil Resources Based on Molecular Dynamics Simulations

Summary Nanopores prevalent in shale reservoirs significantly impact shale oil occurrence characteristics due to the strong intermolecular forces between crude oil molecules and the pore walls. Unlike bulk-phase oil, which is more readily recoverable with current technologies, the behavior of oil within these small-scale environments presents unique challenges. This study utilizes molecular dynamics simulations (MDSs) to investigate the characteristics of shale oil in slit nanopores, with the goal of refining a model that estimates the quantities of both bulk and adsorbed oil in shale reservoirs. We constructed models for three types of nanopores—organic graphene, illite, and quartz—using n-hexane (n-C6H14) as a proxy for shale oil. Our analysis reveals that mineral composition significantly influences fluid adsorption capacity, ranked as graphene > illite > quartz. Unlike prior research, we found that the critical flow pore diameter, which dictates the transition from adsorbed to free-flowing oil, cannot be simplistically equated to the combined thickness of adsorption layers. Specifically, in graphene pores with a diameter of 3.8 nm, the fluid mass density at the pore center still exhibits adsorption layer characteristics, forming up to nine layers. Building on these insights, we revised the shale reservoir resource estimation model to account for adsorption variances across different pore types. Our findings highlight the significant role of adsorbed oil in nanopores within shale reservoirs. Data from the Gulong shale oil block in the Daqing oil field indicate that adsorbed oil constitutes 37.15% of geological reserves, while bulk-phase oil accounts for the remaining 62.85%. This research provides essential data for accurately calculating shale oil reserves in nanopores, which are crucial for the effective exploitation of shale oil reservoirs.

Insight into the effect of oxygen content on the corrosion behavior of X70 pipeline steel in a typical simulated soil solution by dissolution-diffusion-deposition model

In this work, traditional experimental methods were employed to investigate the effect of dissolved oxygen (DO) on the corrosion behavior of X70 pipeline steel in a low-temperature acidic-bentonite simulation soil solution. A novel model was utilized to describe the complex dissolution-diffusion-deposition process at the metal/solution interface. This model aims to enhance comprehension of the dynamics of the corrosion product layer of X70 pipeline steel under varying oxygen concentrations, as well as the effects of alloying elements (Fe, Cr and Cu) on the corrosion resistance. By integrating traditional experimental methods with calculation models, the results reveal a positive correlation between DO levels and the corrosion rate X70 pipeline steel. This relationship is attributed to the distinct characteristics of the corrosion product layers formed under different DO conditions. At low DO levels, the nucleation and growth rates of oxide/hydroxide are slower, leading to the formation of a denser, more protective corrosion product layer. Conversely, at high DO levels, the accelerated nucleation and growth rates produce larger oxide/hydroxide particles, resulting in a porous and less protective corrosion product layer. Furthermore, the variation in the protective qualities of the corrosion product layers under different DO conditions causes differences in corrosion morphology: X70 pipeline steel exhibits localized corrosion in low DO environments and more uniform corrosion under high DO conditions.

Complex Diagnostics of Silicon-on-Insulator Layers after Ion Implantation and Annealing

A technology was developed for activating ion-implanted dopants in silicon-on-insulator layers at a low annealing temperature (600°C) using the pre-amorphization technique of a silicon device layer. In the case of phosphorus implantation, silicon was amorphized directly by dopant ions. In the case of boron implantation for pre-amorphization, the layers were preliminary irradiated with argon or fluorine ions. Complex diagnostics of the implanted layers was carried out using secondary ion mass spectrometry, X-ray diffractometry and small-angle X-ray reflectometry. The combination of methods made it possible to characterize the impurity distribution, the degree of silicon crystallinity, the layer thicknesses, and the interface widths in structures. The results of diagnostics of the structure and composition correlate well with calculations in the SRIM software package and the electrophysical characteristics of the layers after annealing. It was shown that the use of argon for pre-amorphization of silicon interfered with the recrystallization process and did not make it possible to achieve acceptable electrical characteristics of the doped layer. Amorphization with phosphorus and pre-amorphization with fluorine during boron implantation allowed obtaining the required values of the resistance of the doped layers after annealing at a temperature of 600°C. The use of a complex approach made it possible to optimize the modes of amorphization, ion doping, and annealing of silicon-on-insulator structures at low temperatures, necessary for the creation of light-emitting device structures based on silicon-germanium nanoislands.

Impact of Annealing in Various Atmospheres on Characteristics of Tin-Doped Indium Oxide Layers towards Thermoelectric Applications.

The aim of this work was to investigate the possibility of modifying the physical properties of indium tin oxide (ITO) layers by annealing them in different atmospheres and temperatures. Samples were annealed in vacuum, air, oxygen, nitrogen, carbon dioxide and a mixture of nitrogen with hydrogen (NHM) at temperatures from 200 °C to 400 °C. Annealing impact on the crystal structure, optical, electrical, thermal and thermoelectric properties was examined. It has been found from XRD measurements that for samples annealed in air, nitrogen and NHM at 400 °C, the In2O3/In4Sn3O12 share ratio decreased, resulting in a significant increase of the In4Sn3O12 phase. The annealing at the highest temperature in air and nitrogen resulted in larger grains and the mean grain size increase, while vacuum, NHM and carbon dioxide atmospheres caused the decrease in the mean grain size. The post-processing in vacuum and oxidizing atmospheres effected in a drop in optical bandgap and poor electrical properties. The carbon dioxide seems to be an optimal atmosphere to obtain good TE generator parameters-high ZT. The general conclusion is that annealing in different atmospheres allows for controlled changes in the structure and physical properties of ITO layers.

Effect of powder activated carbon (PAC) addition on the performance of anaerobic dynamic membrane bioreactor (AnDMBR): Stratification analysis of dynamic membrane characteristics

Powdered activated carbon (PAC), proved to be an effective performance enhancer in membrane bioreactor studies, has yet been applied in anaerobic dynamic membrane bioreactors (AnDMBRs). A better understanding of dynamic membrane (DM) characteristics is important for optimizing AnDMBR performance. In this study, three AnDMBRs were operated for synthetic municipal wastewater treatment with PAC dosages of 0, 1, and 3 g/L. Particular efforts were devoted to understanding the DM characteristics in layers. It was found that PAC addition increased COD removal efficiency (10 %) and decreased DM filtration resistance (60 %). The extent of performance enhancement, particularly the decrease of DM filtration resistance, appears to be much more substantial in AnDMBR than aerobic DMBR. PAC addition significantly reduced the fouling potential of suspended sludge and consequently the filtration resistance of DM. Compared with inner DM layer, the outer DM layer exhibited relatively higher fouling characteristics, such as less porous structure, higher EPS content (mainly proteins), hydrophobicity, and relative abundance of biofouling-related bacteria. The positive effect of PAC addition on reducing DM fouling characteristics turned out to be more significant for outer DM layer, mainly because of its higher PAC content associated with the higher amount of small particles.

Study on the initial corrosion characteristics of weathering steel bridges with corrugated steel webs

In this study, the characteristics and influencing factors of the early corrosion evolution process of the weathering steel (WS) bridge in northwest China were examined, revealing the seldom-studied corrosion evolution law of the corrugated web of weathering steel. The corrosion losses and average corrosion thickness of WS bridge components were measured and evaluated. The results revealed that compared with traditional flat steel webs, the corrosion of corrugated steel webs was enhanced in the vertical direction and at the wave peak, and resulted in a more complex corrosion pattern. The trend of the rust layer on the inner and outer surfaces of the box girder is related to the stage characteristics of the corrosion products. The appearance of microscopic characteristics of the rust layer is periodic, with the rust layer thickness, colour, and particle size varying with the bridge’s location. The development of the corrosion pit followed a lognormal distribution, and the corrosion rate was balanced in the radial and depth directions. These results can serve as a basis for designers or inspectors to design WS bridge structures, select correct corrosion allowances, and improve corrosion monitoring and detection techniques.

Infectious meningitis. Why are the leptomeninges preferentially involved? Electron microscopic insights.

In infectious meningitis, pathogens preferentially attack the leptomeninges (pia mater and arachnoid) rather than the pachymeninges (dura mater). This study aims to provide ultra-anatomical insights from our extensive collection of electron microscopy images and propose mechanisms, highlighting structures that favor the introduction, adherence, colonization, and proliferation of microorganisms leading to spinal meningitis. Over several years, we analyzed an extensive collection of transmission and scanning electron microscopy images of human spinal meninges captured in our laboratories. Upon examining 378 of those images, we identified potential sites for the iatrogenic or hematogenic introduction and adherence of microorganisms, as well as sites for their colonization and proliferation. These included the outer surface of the spinal dural sac, structures within the epidural space, and the spinal dural sac itself, which comprises compact dura mater with interwoven collagen fibers and tightly bound arachnoid cells. Also, the subdural (extra-arachnoid) compartment, consisting of fragile neurothelial cells prone to rupture under force, formed an acquired spinal subdural space, a new subarachnoid compartment, limited by arachnoid trabeculae, that surrounded the nerve roots and spinal cord and the pia mater. Macrophages, fibroblasts, mast cells, and plasma cells were also observed within the dura mater, arachnoid layer, arachnoid trabeculae, and pia mater. These images illustrate how the characteristics of the meningeal layers could contribute to bacterial adhesion and proliferation at various locations, inducing selective inflammation during (iatrogenic) spinal meningitis. In addition, the images help to explain why magnetic resonance imaging enhancement appears preferentially at specific sites.

Optimizing the Die-Sink EDM Machinability of AISI 316L Using Ti-6Al-4V-SiCp Electrodes: A Computational Approach

Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi’s L27 orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use.

Impact of variable hydrostatic pressure and intermittent operation on filtration performance and biofouling layer in gravity-driven membrane system for practical decentralized water supply

The gravity-driven membrane (GDM) systems offer a promising solution for decentralized drinking water supply due to its low maintenance and energy consumption. However, research on the practical application of GDM under variable hydrostatic pressure (variable load) and intermittent operation conditions, particularly in response to high-turbidity water, remains limited. This study investigates the long-term performance and characteristics of the biofouling layer of GDM under ultra-low variable hydrostatic pressure (20–60 mbar) and intermittent operation (8–12 h) conditions in practical decentralized water supply. The results indicate that the membrane flux (1.9–6.0 L∙m−2∙h−1, LMH) remained relatively stable despite variations in gravity-driven pressure (ΔP). Long-term operation of GDM can stably remove turbidity and potential pathogens from raw water. The total protein/polysaccharide ratios of extracellular polymeric substances (EPS) in the biofouling layer were below 0.50, reducing contaminant adhesion on the membrane surface. The 0.2–0.4 μm transparent exopolymer particles (TEP) fraction might be crucial for biofouling layer formation. The key species, belong to Proteobacteria and Patescibacteria, significantly alleviated membrane fouling by degrading polysaccharide and proteins in biofouling layer. Comammox Nitrospira were significantly enriched, contributing to efficient nitrification. GDM with variable load maintained a stable flux of 2.2–5.2 LMH under high-turbidity water conditions (300–1200 NTU), and simple forward flushing combined with sludge discharge can quickly restore ΔP. Overall, the variable load GDM system effectively manages membrane fouling and maintains stable filtration performance through the combined effects of variable load and intermittent operation, ensuring long-term and low-maintenance operation for decentralized water supply.

An Analytical Insight Into Stability Analysis of Unsaturated Multi‐Layered Slopes Subjected to Rainfall Infiltration

ABSTRACTSlopes in nature usually present layered characteristics, and its stability is susceptible to rainfall events. Considering that current analytical solutions are only suited to simulate the rainfall infiltration of double‐layered infinite unsaturated slopes, an analytical procedure is hence proposed in this study to tackle the consideration of multiple layers. The variable separation method and transfer matrix method are combined to derive the analytical solution of pore water pressure (PWP) for simulating rainfall infiltration in layered infinite unsaturated slopes. After having validated the proposed model and analytical solutions by comparing with existing literature and numerical simulation, the closed‐form solution of PWP is incorporated into the limit equilibrium for assessing slope stability. A three‐layer slope is selected as an example for further discussion. PWP distribution and factor of safety are calculated, considering the effects of saturated hydraulic conductivity and thickness of the upper layer, intensity of antecedent and subsequent rainfall, and varied soil unit weight along the depth. The slope stability subjected to rainfall effects is consistent with the variation of PWP. The proposed analytical solutions provide a simple and practical avenue for computing PWP distribution and evaluating the stability of multi‐layered slopes under rainfall conditions, which can also serve as a benchmark for numerical solutions.

Assessment of petrophysical properties of reservoirs by well data

The use of traditional research complexes is of great importance in solving such important problems as correlating well sections for the purpose of lithological assessment, determining the reservoir properties of reservoir layers that make up the section, namely, saturation, porosity, permeability and clay content, as well as identifying oil and gas-bearing and aquifer layers. The complexity of solving such important problems depends entirely on the petrophysical properties of reservoir layers. For example, the electrical resistivities of oil-bearing and aquifer reservoirs are similar in magnitude, which leads to high clay content and significant variation in porosity throughout the field, which, as a result, makes it difficult to separate oil-bearing and aquifer reservoirs from each other. On the other hand, it is also known that an increase in the amount of cementing clays in reservoirs leads to a decrease in porosity and an increase in residual water saturation. In this regard, in the article, based on a set of well data, the values of the petrophysical parameters of reservoir layers were determined, models were built that determine the correlation between them, and regression equations based on the statistical distribution of these parameters were established. The article also built models for the distribution of porosity, permeability, clay content and oil and gas saturation along well sections. The constructed models provide the basis for forming a definite opinion about the field and obtaining results of both a scientific and practical nature when studying the filtration and reservoir characteristics of reservoir layers, as well as when determining the relationship between petrophysical parameters. The object of the study was the on the Kirmakinskaya (KS) and Underkirmaky (UK) formations of the productive series (PS) selected from the wells of the Gyurgyan-Deniz field.

Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers

Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers

Application of Convolutional Neural Network in Image Recognition

Image recognition is of great significance for both human social development and daily life. This paper mainly discusses the application research of convolutional neural network (CNN) in the field of image recognition. Firstly, the basic structure and working principle of CNN, including the core components of convolution layer, pooling layer and full connection layer, are introduced, and then the structure and characteristics of the middle layer are explained. Then, this paper compares the advantages and disadvantages of different CNN models such as LeNet-5, AlexNet, GoogLeNet, ResNet, etc., and analyzes their application in face recognition, medical image recognition, traffic recognition, character recognition and other fields. Finally, this paper summarizes the application of CNN in image recognition, and points out the problems faced in its practical application, such as model complexity, computational resource requirements, etc., aiming to provide reference for further research of image recognition technology.

Soil chemical characteristics, yield and technological quality of sugarcane under subsurface drip irrigation with treated sewage effluent

ABSTRACT Sugarcane cultivation requires sustainable strategies to meet water and nutritional needs. This study tested the hypothesis that irrigation with treated sewage effluent (TSE) can meet the water needs of sugarcane plants, improving their technological quality and yield. The objective of this study was to assess the effects of subsurface drip irrigation (SDI) with TSE or surface reservoir water (SRW) on soil chemical characteristics and technological quality and productivity. Thus, an SDI system was installed at soil depths of 0.20 and 0.40 m to apply SRW and TSE to sugarcane plants, composing four irrigation treatments (TSE0.2 m, TSE0.4 m, SRW0.2 m and SRW0.4 m) and a control (non-irrigated treatment). Analyses of soil chemical characteristics in different layers showed no negative effects for the use of SDI, which maintained favorable soil conditions for sugarcane plantations. The surface layer (0–0.2 m) irrigated with SRW or TSE showed higher accumulation of nutrients and organic matter. The results confirmed the hypothesis that irrigation with TSE or SRW improves the technological quality and yield of sugarcane; the treatment TSE0.2 m showed the best results regarding increases in sugar yield and alcohol production. However, continuous monitoring of soil salinity is necessary in wastewater-irrigated agricultural systems.

A Novel Resource-Aware Distributed Cooperative Decision-Making Mechanism for Connected Automated Vehicles

<div>This article proposes a new model for a cooperative and distributed decision-making mechanism for an ad hoc network of automated vehicles (AVs). The goal of the model is to ensure safety and reduce energy consumption. The use of centralized computation resource is not suitable for scalable cooperative applications, so the proposed solution takes advantage of the onboard computing resources of the vehicle in an intelligent transportation system (ITS). This leads to the introduction of a distributed decision-making mechanism for connected AVs. The proposed mechanism utilizes a novel implementation of the resource-aware and distributed–vector evaluated genetic algorithm (RAD-VEGA) in the vehicular ad hoc network of connected AVs as a solver to collaborative decision-making problems. In the first step, a collaborative decision-making problem is formulated for connected AVs as a multi-objective optimization problem (MOOP), with a focus on energy consumption and collision risk reduction as example objectives. RAD-VEGA then cooperatively solves this MOOP, taking into account the availability of AV’s onboard resources and the application layer characteristics of today’s ITS communication tools. The performance of the proposed mechanism is evaluated by solving the ZDT1 test problem and studying pareto-frontier solutions to the true front over time. The scalability of the proposed solution is estimated to be 305 CAVs, considering a communication bandwidth of 6 MB/s. Additionally, cooperative AV planning scenario examples are simulated, and the effectiveness of the proposed mechanism is demonstrated by comparing final and initial solutions after solving the MOOP using RAD-VEGA.</div>

Theory of Linear-Circular Dichroism in Monoatomic Layers of Transition Metal Dichalcogenides Taking into Account the Rabi Effect

We have developed a theory of dimensional quantization for nanostructures, both one-dimensional and zero-dimensional, constructed from monoatomic layers of transition metal dichalcogenides (TMDCs). This theory has enabled us to derive expressions for the energy spectra of charge carriers in both even and odd states (relative to coordinate inversion), as these states occur within quantum-confined lines and points of the TMDC monoatomic layers, dependent on their geometric dimensions. Our numerical analysis provides a detailed exploration of the quantum-confined energy states of electrons within these nanostructures, offering insights into their potential applications in advanced nanoelectronic devices. This work not only advances our understanding of the energy characteristics of TMDC monoatomic layers but also contributes to the broader field of material science by exploring the effects of dimensional quantization on electronic properties.