Layer Model Research Articles

Tungsten-liquid triboelectric nanogenerator for water-flowing energy harvesting with low-resistance and high-DC density

In the new energy era, triboelectric nanogenerator (TENG) effectively converts weak water energy into electricity to drive sensors and electronic components, which has a very broad research prospect. However, the functional materials of TENG are usually insulating, resulting in low-current and high-resistance problems. Guided by Wang's electrical double layer model, we demonstrate that tungsten presents excellent electricity generation performance in flowing water due to its unique defect structure as evidenced by comparing the electricity generation of tungsten, tantalum, titanium, and copper, and testing their electrochemical impedance spectroscopy and X-ray diffraction characteristics. Hence, the study proposes a novel tungsten-liquid TENG for water-flowing energy harvesting, which has outstanding advantages of high DC density, low internal resistance, simple structure, and no complex electrical power required. A tungsten-liquid TENG continuously outputs 3∼20μA in 0.1–2.0M NaCl liquids at a flow rate of 400 mL/min, with output current densities up to 230 mA/m2 and internal resistances down to 6.4–67 kΩ. Ten-tungsten-liquid TENG outputs nearly 40μA, and multiple tungsten electrodes in parallel can effectively enhance the current. Notably, the study is not only beneficial for capturing the current energy in oceans with high-ionic concentrations, but also broadens the research ideas of TENG for promoting the selection of novel functional materials.

Love wave propagation in a smart composite structure of linear and exponential functionally graded porous piezoelectric material

Functionally graded materials (FGMs) have emerged as a promising avenue for enhancing the performance and longevity of surface acoustic wave (SAW) devices. This importance is gained due to gradual variation in functional properties of FGMs. In this paper, a mathematical model of a piezoelectric layer overlying a functionally graded porous piezoelectric (FGPP) layer resting on the elastic substrate is presented for the study of Love waves. The material parameters of the FGPP layer are considered to vary linearly and exponentially with the thickness of the layer. The solutions of governing equations corresponding to the FGPP layer are obtained by the Wentzel Kramers Brillouin (WKB) method. Dispersion equations are derived for both electrically open and shorted boundaries, linear and exponential gradation in both mechanical and electrical parameters, and for gradation in mechanical parameters alone as well. After numerical computation, dispersion curves are plotted to investigate the influence of wavenumber, type of gradation, extent of gradation, and type of boundaries on the phase and group velocity. The phase velocity decreases with wavenumber and is found more for linear gradation in comparison to exponential gradation. The electromechanical coupling factor is analyzed for different propagating modes of Love waves, in the case when gradation in mechanical properties is considered, the electromechanical coupling factor is greater than that when variation in both mechanical and electrical properties is considered. It is also observed that the tailoring of gradation can help to get the suitable value of the electromechanical coupling factor. The insights obtained from these findings can offer valuable contributions toward advancing the functionality and efficiency of SAW devices, there by bolstering their applicability in various technological domains.

Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province

Abstract In order to reveal the restriction in shale gas enrichment of the Wufeng-Longmaxi Formation in the northern Guizhou province, the influence model of detachment layer was established through field geological investigation, core observation, logging, sample analysis, and geological background data. The response relationship between the detachment layer and the shale gas enrichment model in different structural formats was analyzed. The results show that the thickness of the Wufeng-Longmaxi Formation’s detachment layer is influenced by the conditions near the fault zones and mineralogical characteristics. The lithofacies of the detachment layer shows mainly a combination of clay-rich shale facies. This indicates that lithofacies type is one of the main factors influencing the variation in slip layer thickness. The detachment layer exhibits distinct well logging response characteristics and is influenced by nitrogen enrichment. The development of detachment fractures allows atmospheric nitrogen to infiltrate shale gas. It leads to poor gas saturation in the shale gas. In addition, the overall tectonic deformation in the northern Guizhou province was found to gradually intensify from Northwest to Southeast, and there were two tectonic models: a slot-shift tape transition belt and a spacer type deformation belt. The influence of decollements on shale gas preservation was barely found in the northern Guizhou province. It is mainly controlled by buried depth of the target layer, conditions of the cover layer, structural type, and deformation intensity.

Vibration analysis of functionally graded carbon nanotube‐reinforced composite open cylindrical shells with damping film embedded

AbstractIn the present study, a discrete layer model was established to explore the vibration performance of Functionally Graded Carbon Nanotube‐Reinforced Composite (FG‐CNTRC) open cylindrical shells with damping film embedded based on the first‐order shell theory. There are four configurations of stacking arrangements considered for FG‐CNTRC open cylindrical shells with damping film embedded. The equivalent structural parameters of the top and bottom FG‐CNTRC panels are generated by implementing the extended mixing rule. Governing equations are derived based on the Hamilton principle and solved with the Naiver solution. Subsequently, after verifying the validity of this paper's solution by comparing it with the published literature, a parametric elaborated investigation discloses the variation patterns of vibration performance of four FG‐CNTRC open cylindrical shells with damping film embedded. The conclusions of the study can be used as a useful guide about open cylindrical composite shell structures with the design of high strength and damping.Highlights Discrete layer vibration model was bulit based on first‐order shell theory. Vbration performance of FG‐CNTRC open sandwich shells was studied. Variation patterns of frequency and loss factor was disclosed.

Hypersonic Stability and Breakdown Measurements on the AFRL/AFOSR BOLT II Flight Geometry

The Air Force Research Laboratory/Air Force Office of Scientific Research (AFRL/AFOSR) introduced the Boundary Layer Turbulence (BOLT II) flight experiment to further the understanding and modeling of a hypersonic turbulent boundary layer on a geometry that features concave surfaces with swept leading edges. In support of the flight experiment, this paper identifies and documents the transition instabilities and quantifies the breakdown to turbulence on a 25%-scale BOLT II model in hypersonic flow. Experiments were conducted in the conventional Actively Controlled Expansion wind tunnel facility and the Mach 6 Quiet Tunnel located at the Texas A&M University National Aerothermochemistry and Hypersonics Laboratory. Global surface heating was viewed using infrared thermography with on-surface measurements made using high-frequency Kulite and PCB Piezotronics surface pressure transducers. Modal growth was observed among the sensors both upstream and downstream of the model. Off-surface measurements were made in the mixed-mode region utilizing constant-temperature hot-film anemometry in the Mach 6 Quiet Tunnel. Comparisons were made to the Direct Numerical Simulation (DNS) results from the University of Minnesota. Amplified content was observed between f=53 and 75 kHz in the root-mean-square voltage fluctuations of the hot-film measurements.

Direct numerical simulation of skin friction drag reduction on supersonic turbulent boundary layers with micro-blowing

The flow control mechanism and skin friction drag reduction characteristics of micro-blowing on a Ma2.25 supersonic turbulent boundary layers are investigated through direct numerical simulations, and the effects of blowing intensity and micro-hole arrangement on turbulent structure and skin friction drag in the local control region and downstream region are considered. The results show that the skin friction drag decreases remarkably in the control region under the influence of micro-blowing, and a certain drag reduction can still be maintained in the downstream region. The drag reduction performance in the control region is jointly determined by blowing intensity and micro-hole arrangement. The drag reduction performance of the staggered arrangement is 5.7% and 11.1% higher than that of the inline arrangement at blowing intensities of 0.2% and 0.5%, respectively. However, it is found that the drag reduction in the downstream region is only determined by the blowing intensity and almost independent of the micro-hole arrangement. The effect of micro-blowing on turbulent structures is quantitatively characterized by energy spectrum analysis, and it shows that the streamwise scales of the near-wall streaks are significantly reduced under the influence of micro-blowing. In addition, the compressibility of fluids and the local reverse transfer in the strong expansion region are significantly improved under the influence of micro-blowing. These effects should be considered when performing Large Eddy Simulation modeling of supersonic turbulent boundary layers with micro-blowing.

Significant effect of salinity on zinc adsorption on tropical coastal and floodplain soils

AbstractRising sea levels due to climate change are causing increased salinisation of low‐lying coastal and floodplain soils, and the impact of this process on the bioavailability of plant nutrients needs to be understood as mitigation strategies are adapted. Zinc (Zn) is an element of particular importance due to its function as a micronutrient for plants including rice and other staple foods. In the current study, our aim was to investigate the effects of salinisation on zinc adsorption onto soils representing at‐risk coastal and floodplain environments, addressing in particular our knowledge gap concerning the roles that solution chemistry and soil composition play. To this end, we conducted batch adsorption experiments in the laboratory and ran geochemical models in saline solutions up to 0.7 mol L−1 ion strength incorporating both (i) a multi surface model (MSM) for surface reactions containing three phases, that is iron hydroxides, organic matter and phyllosilicate clays, and (ii) aqueous‐phase complexation to dissolved organic and inorganic ligands. Surface reactions were modelled using the diffuse double layer model, the NICA–Donnan model and an ion exchange model using the Gaines–Thomas convention. We combined the experimentally determined mass composition of surface phases with generic modelling parameters taken from the literature. We first show that increasing salinity enhances the formation of aqueous Zn‐chloride complexes in the presence of dissolved organic matter and bicarbonate, thereby decreasing the availability of free Zn2+ and supressing the partitioning of zinc to the adsorbed phase. We demonstrate using batch adsorption experiments with a calcareous hydraquent and a tropaquept, that salinity decreases zinc adsorption strongly in the pH range between 3 and 6. Satisfactory agreement between experiments and model calculations was achieved with root‐mean‐square errors ranging for different salinities between 2.88% and 2.92% for the hydraquent and between 4.59% and 2.74% for the tropaquept soil. Model predictions of adsorption were slightly inferior at low salinity for the hydraquent soil and at high salinity for the tropaquept soil, pointing possibly to an incomplete geochemical model or to a need to parametrise surface adsorption models at higher ionic strengths. Present surface models have been largely parametrised at lower ionic strength. We lastly apply the MSM to examine zinc adsorption in five endoaquepts soils, representing soil series from Bangladesh. We show that increasing salinity decreases zinc adsorption to the soil organic matter and the clay fractions. We conclude from our findings that increased soil salinity due to rising sea levels and climate change will have a significant impact on zinc cycling and possibly other micronutrients in areas where coastal soils and floodplain soils overlap, such as deltas and estuaries. In particular, we predict a decrease in zinc adsorption in acidic to neutral soils. The availability of zinc for biouptake through the roots of crop plants including rice will be significantly disturbed following salinisation, most likely affecting crop production. Our study demonstrates the potential that geochemical modelling combined with experimental data has to improve our capability to assess the effects of salinity due to rising seawater levels in vulnerable regions of the world.

Multi-field coupling in the scrape-off layer of tokamak plasma

Abstract We study a reduced electrostatic fluid model for the tokamak scrape-off layer (SOL), which incorporates temperature gradient and vorticity gradient as two free energy fields. Two scenarios of field coupling are addressed: (1) sheath condition; (2) vortex wave coupling. For the sheath condition induced field coupling, the poloidal E×B flow shear is coupled with the temperature gradient. Combining an eigenmode analysis and the nonlinear phase dynamics approach, our findings indicate that in the absence of a vorticity gradient, the overall effect of the sheath condition induced flow shear can either stabilize or destabilize the interchange mode, depending on the competition between the flow shear suppression and the temperature gradient driving. This is different from the case where the gradient drive and shear damping are decoupled. When the field coupling is mediated by wave interactions, by setting an idealized step-like temperature and vorticity profiles, a joint mode forms through resonant interaction between the interfacial waves driven by the temperature and vorticity gradients, respectively. Near the phase locking condition, the joint mode can be more unstable than pure temperature gradient driven mode.

Effects of 2.5-D ultra-low and ultra-high velocity zones on flip-reverse-stacking (FRS) of the ScS wavefield

SUMMARY Within the last decade, thin ultra-low velocity zone (ULVZ) layering, sitting directly on top of the core–mantle boundary (CMB), has begun to be investigated using the flip-reverse-stack (FRS) method. In this method, pre- and post-cursor arrivals that are symmetrical in time about the ScS arrival, but with opposite polarities, are stacked. This same methodology has also been applied to high velocity layering, with indications that ultra-high velocity zones (UHVZs) may also exist. Thus far, studies using the FRS technique have relied on 1-D synthetic predictions to infer material properties of ULVZs. 1-D ULVZ models predominantly show a SdS precursor that reflects off the top of the ULVZ and an ScscS reverberation within the ULVZ that arrives as a post-cursor. 1-D UHVZ models are more complex and have a different number of arrivals depending on a variety of factors including UHVZ thickness, velocity contrast, and lateral extent. 1-D modelling approaches assume that lower mantle heterogeneity is constant and continuous everywhere across the lower mantle. However, lower mantle features display lateral heterogeneity and are either finite in extent or display local thickness variations. We examine the interaction of the ScS wavefield with ULVZs and UHVZs in 2.5-D geometries of finite extent. We show that multiple additional arrivals exist that are not present in 1-D predictions. In particular, multipath ScS arrivals as well as additional post-cursor arrivals are generated. Subsequent processing by the FRS method generates complicated FRS traces with multiple peaks. Furthermore, post-cursor arrivals can be generated even when the ScS ray path does not directly strike the heterogeneity from above. Analysing these predictions for 2.5-D models using 1-D modelling techniques demonstrates that a cautious approach must be adopted in utilization and interpretation of FRS traces to determine if the ScS wavefield is interacting with a ULVZ or UHVZ through a direct strike on the top of the feature. In particular, traveltime delays or advances of the ScS arrival should be documented and symmetrical opposite polarity arrivals should be demonstrated to exist around ScS. The latter can be quantified by calculation of a time domain multiplication trace. Because multiple post-cursor arrivals are generated by finite length heterogeneities, interpretation should be confined to single layer models rather than to interpret the additional peaks as internal layering. Furthermore, strong trade-offs exist between S-wave velocity perturbation and thickness making estimations of ULVZ or UHVZ elastic parameters highly uncertain. We test our analysis methods using data from an event occurring in the Fiji-Tonga region recorded in North America. The ScS bounce points for this event sample the CMB region to the southeast of Hawaii, in a region where ULVZs have been identified in several recent studies. We see additional evidence for a ULVZ in this region centred at 14°N and 153°W with a lateral scale of at least 250 km × 360 km. Assuming a constant S-wave velocity decrease of −10 or −20 per cent with respect to the PREM model implies a ULVZ thickness of up to 16 or 9 km, respectively.

Construction of Ensemble Learning Model for Home Appliance Demand Forecasting

Given the increasing competition among household appliance enterprises, accurately predicting household appliance demand is crucial for enterprise supply chain management and marketing. This paper proposes a combined model integrating deep learning and ensemble learning—LSTM-RF-XGBoost—to assist enterprises in identifying customer demand, thereby addressing the complexity and uncertainty of the household appliance market demand. In this study, Long Short-Term Memory Network (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are established separately. Then, the three individual algorithms are used as the base models in the first layer, with the multiple linear regression (MLR) algorithm serving as the meta-model in the second layer, merging the demand prediction model based on the hybrid model into the overall demand prediction model. This study demonstrates that the accuracy and stability of demand prediction using the LSTM–RF–XGBoost model significantly outperform traditional single models, highlighting the significant advantages of using a combined model. This research offers practical and innovative solutions for enterprises seeking rational resource allocation through demand prediction.

The Guardian Node Slow DoS Detection Model for Real-Time Application in IoT Networks.

The pernicious impact of malicious Slow DoS (Denial of Service) attacks on the application layer and web-based Open Systems Interconnection model services like Hypertext Transfer Protocol (HTTP) has given impetus to a range of novel detection strategies, many of which use machine learning (ML) for computationally intensive full packet capture and post-event processing. In contrast, existing detection mechanisms, such as those found in various approaches including ML, artificial intelligence, and neural networks neither facilitate real-time detection nor consider the computational overhead within resource-constrained Internet of Things (IoT) networks. Slow DoS attacks are notoriously difficult to reliably identify, as they masquerade as legitimate application layer traffic, often resembling nodes with slow or intermittent connectivity. This means they often evade detection mechanisms because they appear as genuine node activity, which increases the likelihood of mistakenly being granted access by intrusion-detection systems. The original contribution of this paper is an innovative Guardian Node (GN) Slow DoS detection model, which analyses the two key network attributes of packet length and packet delta time in real time within a live IoT network. By designing the GN to operate within a narrow window of packet length and delta time values, accurate detection of all three main Slow DoS variants is achieved, even under the stealthiest malicious attack conditions. A unique feature of the GN model is its ability to reliably discriminate Slow DoS attack traffic from both genuine and slow nodes experiencing high latency or poor connectivity. A rigorous critical evaluation has consistently validated high, real-time detection accuracies of more than 98% for the GN model across a range of demanding traffic profiles. This performance is analogous to existing ML approaches, whilst being significantly more resource efficient, with computational and storage overheads being over 96% lower than full packet capture techniques, so it represents a very attractive alternative for deployment in resource-scarce IoT environments.

Non-uniform distribution model of rust layer around steel bar circumference and its effect on corrosion-induced concrete cracking

This paper presents a critical review of non-uniform distribution models of rust layer around a reinforcing bar’s circumference and a numerical investigation into the effect of non-uniform distribution of rust on (a) crack patterns and (b) development of concrete surface crack width. Different shapes of rust layer simulated by mathematical models were established based on statistical distribution functions which were compared with the transport-electrochemical model and published testing results. In addition, an advanced 2D finite element model was implemented to simulate crack propagation in concrete caused by rebar corrosion. It is found that non-uniform rust distribution shape around the rebar not only affected concrete surface crack width evolution, but also crack length and crack pattern. Transport-electrochemical model and Gaussian distribution-based model could reasonably simulate rust distribution and associated corrosion-induced cracks. In addition, the authors proposed a simplified semi-empirical model that incorporated an analytical chloride transport model and a Gaussian model to simulate time-dependent advancement of non-uniform rust layer. Furthermore, a parametric study of the effect of elastic stiffness of the rust layer on crack patterns and surface crack width development was also performed.

Machine learning-based acoustic emission technique for corrosion-induced damage monitoring in reinforced concrete structures

Early-stage detection of corrosion in reinforcement embedded inside concrete, generally considered as the major problem in structures under extreme marine environments, can help to schedule timely maintenance/repair and to avoid any catastrophic failure. However, quantitative evaluation of the degree of deterioration, especially at a very early stage, i.e., before its appearance on the surface, is extremely difficult due to the complex process in heterogeneous material. This study aims to characterize the critical damage stages in reinforced concrete specimens under accelerated conditions using the Acoustic emission (AE) damage quantification methods supported by machine learning (ML) techniques. The stages of corrosion-induced damage are predicted by a two-step predictive model that involve both regression and classification tasks. Three supervised regression models — support vector regression (SVR), relevance vector machine (RVM) and kernel ridge regression (KRR) —are first adopted to evaluate the amount of current. Then the output from regression model is given as an input to the classification problem to predict the discrete class label output. Characterized damage stages are validated based on mass loss and crack width measurements. The results show that the corrosion initiation (at day 3) and crack formation (at day 7) can be determined from the sudden change in acoustic parameters. Further, a double layer (supervised-unsupervised) model is proposed which is found be more effective in correctly detecting the progression of damage. The findings indicate that the proposed approach that combines corrosion detection by analysing AE signals with ML technique can accurately and robustly predict corrosion severity.

Evaluation of Solidification and Interfacial Reaction of Sn-Bi and Sn-Bi-In Solder Alloys in Copper and Nickel Interfaces

Although there are studies devoted to lower Indium (In) addition, Sn-Bi alloys containing 10 wt.% In or more have been barely investigated so far. Higher In contents may offer the potential for improved joint production, better control over the growth of interfacial layers, and enhanced mechanical strength. The present article focuses on the solidification, wettability, adhesion strength, and interfacial intermetallic growth in the Sn-40%Bi-10%In alloy soldered on Cu and Ni pads. SEM-EDS, wettability tests, and tensile tests were performed. The contact angles were measured in Cu and Ni as 24° and 26°, respectively. Indium addition promoted coarsening of the as-solidified microstructure due to an increase in the alloy solidification range. The Bi spacing was increased at least three times, with a strong segregation of Bi towards the interface. The formation and growth of alloy/Cu reaction layers were also evaluated under the different aging conditions of the as-soldered joints, simulating real service. A growth kinetics model of the reaction layer showed that In increases the activation energy, thereby reducing the layer growth. The adhesions of the formed intermetallics films in Cu and Ni were analyzed using tensile tests. It was observed that the alloy/Ni couple exhibited better adhesion. Premature fracturing appears to happen in the alloy/Cu joint due to the higher intermetallic compound’s (IMC) thickness, rough morphology, and coarser microstructure. Both ductile fracture features with dimples and cleavage zones associated with Bi, Cu6(Sn,In)5, and Ni3Sn4 intermetallics were observed.

Leveraging electrochemical double layer structure to rationally control electrolysis.

This perspective delves into the electrochemical microenvironment, uncovering entropic effects in CO2 reduction, revealing neutral molecule electrosorption under polarization, highlighting challenges in the classical double layer model, and proposing research approaches for future interface studies.

Physics-guided federated learning as an enabler for digital twins

Digital twins bring the potential to increase the efficiency of assets, systems, and processes by building virtual replicas through real-time data and modeling. However, data are often confidential and distributed, high-fidelity models based on physical principles tend to be slow and require detailed knowledge about the asset design that may not be available, simplified models lack accuracy, and data-driven models are not interpretable and limited to their training space. We demonstrate the interplay of two enabling technologies, namely federated learning and hybrid analysis and modeling, to combine the strengths of physics-based and data-driven models on distributed data sets that are kept confidential by different stakeholders. Throughout the work, an ensemble of physics-guided neural networks is designed, optimized, and validated to infer the parameters of digital twin components. The physics-guided neural network is injected with output from a simplified physics-based model in intermediate layers for increased performance, and the injection layer is optimized through weight regularization. The model is then integrated into a simulation of the digital twin asset and validated there. It is shown that the model outperforms the simplified physics-based and the data-driven models both on component and on asset level. In the final step, the physics-guided neural network is trained using federated methods, which allows training on confidential data owned by different stakeholders without requiring the stakeholders to share their data. We show that federated hybrid models can – with the right training strategy – by design achieve results that are always identical and potentially superior to models trained on a central data set. The problem chosen for this demonstration is the prediction of airfoil lift and drag coefficients based on the airfoil shape and the angle of attack, which is a task crucial for simulating wind turbines. Using the physics-guided neural network, the estimation of the airfoil coefficients experiences a speedup of several orders of magnitude compared to the high-fidelity simulation, and integration into blade element momentum theory shows that the turbine thrust, torque, and mechanical power can be accurately calculated. The federated approach makes it possible to extend the training of the model to any number of distributed confidential data sets from both simulations and experiments to further increase the model’s accuracy.

Mechanic-electric-thermal coupling simulation method of Lamb wave under variable temperature

The propagation mechanism of Lamb waves exhibits more intricate in the variable operational environment of high-speed trains. Simulation methods are widely used to study Lamb wave propagation characteristics due to economic and time constraints. A mechanic-electric-thermal coupling simulation method to study the propagation mechanism of Lamb waves under variable temperatures is proposed in this paper. To accurately simulate the dynamic behavior of Piezoelectric Transducer (PZT) under variable temperature, a nonlinear numerical model of the dynamic parameters (piezoelectric coefficient, dielectric constant) of PZTs has been developed based on the experiment of the temperature influence on Lamb wave. The model emphasizes the nonlinear pattern of change in the key dynamic parameters of the PZT with temperature fluctuations. In addition, a thermal expansion stress model of the PZT-bonding layers-structure is established to reveal the mechanical-thermal coupling mechanism. Based on the COMSOL, a mechanical-electrical-thermal direct coupling simulation model of PZT-bonding layers- structure under variable temperature is established. The simulation results at −40 °C to 80 °C are obtained and compared to the experiment. The results show that the simulation signal is highly consistent with the experimental measurement signal, and the simulation method proposed in this paper has high accuracy.

تقييم أداء هوائي المايكروسترب لتقليل إمتصاص الموجات الكهرومغناطيسية في األنسجة البشرية في نطاقات 5جي

The absorption of electromagnetic waves emitted by wireless devices in human tissues is quantified by the Specific Absorption Rate (SAR) measured for 1g or 10g of body tissues. The primary objective of this research is to identify an effective shield material that can minimize the impact and potential harm of electromagnetic waves on human tissues. The study involves the design of a single radiator Microstrip antenna operating at 28 GHz and the modelling of human tissue layers. SAR calculations for 1g of body tissues are performed based on the distance between the antenna and the head model. To evaluate the influence of antenna position on the head model, SAR calculations are conducted using two different approaches. In the first approach, the head model is positioned on the patch radiator side, while in the second approach, it is placed on the ground plane side. The aim is to determine which configuration yields lower SAR values. Furthermore, SAR calculations are reanalysed by incorporating shielding materials onto the Microstrip antenna. Three different absorbed materials are used as shields, and their impact on SAR reduction and radiation parameters of the Microstrip antenna are evaluated. The objective is to identify the shield material that achieves the highest SAR reduction while minimally affecting the radiation parameters of the antenna.

A comparative study of fatigue crack driving force considering in-plane constraint effect

Nonlinear fracture mechanics parameters, ΔCTODp and ΔJ are implicitly used to characterize the crack driving force (CDF) in estimating Fatigue Crack Growth Rate (FCGR) in ductile material. However, in the presence of structure geometry effect, evaluation of effective CDF remains to be clarified. In this regard, a comparative study was conducted and the in-plane-constraint parameter was introduced to quantify geometry effect based on the constraint theory. Using modified boundary layer model (MBL) with multi-stage node releasing embedded, the effect of in-plane-constraint on ΔCTODp and ΔJ was investigated. Results indicate that ΔCTODp can demonstrate geometry effect on CDF, as it correlates well with plastic deformation. Compartively, the effective cyclic J-integral (ΔJop) was found to have a significant dependency only on the crack closure level, thus incapable of capturing the effect of geometry on CDF. A humpbacked curve was found between in-plane constraint level and ΔCTODp. Further investigation reveals that the gain of in-plane constraint on FCGR can be attributed to the interaction between in-plane constraint and crack closure, as well as the interplay between in-plane and out-of-plane constraint. This work not only reveals the mechanism of the in-plane constraint effect on CDF, but also provides a basis for establishing FCG model across different structure geometries.

An Efficient and Accurate Quality Inspection Model for Steel Scraps Based on Dense Small-Target Detection

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness.