Search Results (104)

This paper presents a broadband near-field probe designed for measuring the normal magnetic field ($H_z$) in radio frequency (RF) circuits operating within a frequency range of 2–8 GHz. The proposed probe uses a cost-effective 4-layer printed circuit board (PCB) structure made with an FR-4 substrate. The probe primarily consists of an $H_z$ detection unit, a broadband microstrip balun, and a coaxial-like output. The broadband balun facilitates the conversion from differential to single-ended signals, thereby enhancing the probe’s common-mode rejection capability. This design ensures that the probe achieves both cost efficiency and high broadband measurement performance. Additionally, this work investigates the feasibility of employing microstrip lines as calibration standards for the $H_z$ probe. The probe’s structural parameters and magnetic field response were initially determined through simulations, and the calibration factor was subsequently verified by calibration experiments. In practical measurements, the field distributions above a microstrip line and a low-noise amplifier (LNA) were captured. The measured field distribution of the microstrip line was compared with simulation results to verify the probe’s performance. Meanwhile, the measured field distribution of the LNA was utilized to identify the radiating components within the amplifier. Full article

This study presents the optimization of a laser ablation process designed to achieve the precise removal of polyamide coatings from ultra-thin platinum wires. Removing polymer coatings is a critical challenge in high-reliability manufacturing processes such as aerospace thermocouple fabrication. The ablation process must not only ensure the complete removal of the polyamide insulation but also maintain the tensile strength of the wire to withstand mechanical handling in subsequent manufacturing stages. Additionally, the exposed platinum surface must exhibit low surface roughness to enable effective soldering and be free of thermal damage or residual debris to pass strict visual inspections. The wires have a total diameter of 65 µm, consisting of a 50 µm platinum core encased in a 15 µm polyamide coating. By utilizing a UV laser with a wavelength of 355 nm, average power of 3 W, a repetition rate range of 20 to 200 kHz, and a high-speed marking system, the process parameters were systematically refined. Initial attempts to perform the ablation in an air medium were unsuccessful due to inadequate thermal control and incomplete removal of the polyamide coating. Hence, a water-assisted ablation technique was explored to address these limitations. Experimental results demonstrated that a scanning speed of 1200 mm/s, coupled with a line spacing of 1 µm and a single ablation pass, resulted in complete coating removal while ensuring the integrity of the platinum substrate. The incorporation of a water layer above the ablation region was considered crucial for effective heat dissipation, preventing substrate overheating and ensuring uniform ablation. The laser’s spot diameter of 20 µm in air and a focal length of 130 mm introduced challenges related to overlap control between successive passes, requiring precise calibration to maintain consistency in coating removal. This research demonstrates the feasibility and reliability of water-assisted laser ablation as a method for a high-precision, non-contact coating material. Full article

Aortic dissection (AD) is a highly lethal cardiovascular emergency, and clinical studies have found that a high percentage of AD patients are hypertensive. In previous studies, the AD model was simplified, such as by treating the vessel wall as a single-layer rigid material, ignoring the complex biomechanical factors of the vascular lumen. This study elucidates key biomechanical mechanisms by which hypertension promotes primary AD progression using multiscale modeling. First, based on experimental data from longitudinal and circumferential uniaxial tensile testing of porcine aortic walls (5–7-month-old specimens), a constitutive model of the aortic wall was developed using the Holzapfel–Gasser–Ogden (HGO) framework. The material parameters were calibrated via inverse optimization in ABAQUS-ISIGHT, achieving close alignment with mechanical properties of the human aorta. Using this validated model to define the hyperelastic properties of the aortic wall, a multiphysics coupling platform was constructed in COMSOL Multiphysics 6.2, integrating computational fluid dynamics (CFD) and fluid–structure interaction (FSI) algorithms. This framework systematically quantified the effects of blood pressure (bp) fluctuations on compressive stresses, von Mises stresses, and deformation of the intimal flap within the AD lesion region. With constant blood rheology, elevated blood pressure enhances wall stresses (compressive and von Mises), and intima-media sheet deformation, this can trigger initial rupture tears, false lumen dilation, and branch arterial flow obstruction, ultimately deteriorating end-organ perfusion. Full article

The absence of standardized evaluation methodologies for single-layer dimensional accuracy significantly hinders the broader implementation of direct ink writing (DIW) technology. Addressing the critical need for precision non-contact assessment in DIW fabrication, this study develops a novel machine vision-based framework for dimensional accuracy evaluation. The methodology encompasses three key phases: (1) establishment of an optimized hardware configuration with integrated image processing algorithms; (2) comprehensive investigation of camera calibration protocols, advanced image preprocessing techniques, and high-precision contour extraction methods; and (3) development of an iterative closest point (ICP) algorithm-enhanced evaluation system. The experimental results demonstrate that our machine vision system achieves 0.04 mm × 0.04 mm spatial resolution with the ICP convergence threshold optimized to 0.001 mm. The proposed method shows an 80% improvement in measurement accuracy (0.001 mm) compared to conventional approaches. Process parameter optimization experiments validated the system’s effectiveness, showing at least 76.3% enhancement in printed layer dimensional accuracy. This non-contact evaluation solution establishes a robust framework for quantitative quality control in DIW applications, providing critical insights for process optimization and standardization efforts in additive manufacturing. Full article

Many countries employ mining and ore processing techniques to concentrate and extract precious natural resources. However, the slow leaching of numerous dissolved elements and compounds from large quantities of waste rock and mine tailings can significantly threaten groundwater quality in the affected region. When exposed to oxygen and water, sulfide minerals in mine tailing oxidize, potentially forming acid mine drainage (AMD). Various reclamation techniques can inhibit AMD generation, including monolayer cover combined with an elevated water table (EWT), hydraulic barrier, and cover with capillary barrier effect (CCBE). Selecting the most suitable technique requires consideration of site-specific hydrogeological conditions (e.g., water table depth) and available cover materials. Numerical modeling tools such as PHT3D and MT3D can help identify optimal reclamation methods during preliminary planning stages. The 119-hectare Quémont 2 mine site near Rouyn-Noranda city will undergo reclamation following the closure of its tailings storage facilities (TSF). A three-dimensional numerical groundwater and solute-transport model were constructed and calibrated to simulate the site’s hydrogeological behavior post-closure, enabling selection of the most effective AMD control technique. Subsequently, a three-dimensional multicomponent reactive transport model incorporating various cover designs was developed, with simulations considering climate change impacts. The PHT3D model code, which integrates the PHREEQC geochemical model with the MT3D three-dimensional transport simulator, was employed to evaluate cover performance on the Quémont 2 TSF. Four reclamation configurations were tested: Cell #1 (80 cm single-layer clay cover), Cell #2 (60 cm single-layer clay-sand cover), Cell #3 (60 cm single-layer clay-silt cover), and Cell #4 (120 cm multilayer clay-sand-clay sequence). Simulations were conducted under various climate change scenarios (Representative Concentration Pathways—RCPs 2.6, 4.5, and 8.5). This paper describes the numerical model, cover materials, and modeling results both with and without covers. Results indicate that Cells #1 and #4, completely reduced sulfate in groundwater, suggesting these configurations would provide the most effective reclamation solutions for the Quémont 2 mine site. Full article

Phase demodulation is the core of fringe projection profilometry systems. However, current U-Net-based phase demodulation approaches demonstrate deficiencies in global context propagation, adversely affecting wrapped phase extraction precision. To this end, this paper proposes a deep-learning-based model for single-shot wrapped phase extraction, named the full-scale connection and attention enhancement network (SEC-UNet3+). The network mitigates the limitations of the traditional U-Net architecture by introducing cross-layer full-scale connection and a feature integration module in the decoder, enabling efficient interaction between shallow detail features and deep semantic features. Unlike the skip connection strategy within the same-layer in U-Net, cross-layer full-scale connection can enhance the feature utilization. Additionally, a skip connection is embedded between the feature mapping layer and the output transformation layer in the squeeze and excitation module, preventing information loss during the feature calibration process. Compared to the U-Net model, the proposed method achieves an approximately 5% to 15% reduction in both the mean squared error and mean absolute error for phase extraction. The experimental results confirm that SEC-UNet3+ outperforms traditional Fourier transform and mainstream U-Net-based approaches in phase demodulation accuracy, proving particularly effective for single-shot wrapped phase retrieval in dynamic scenarios. Full article

Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. By analyzing these factors, the ANN captures complex relationships influencing fatigue cracking more effectively than traditional methods. A comprehensive dataset from the Long-Term Pavement Performance (LTPP) program is used for model training and validation. The ANN’s ability to adapt and recognize patterns enhances its predictive accuracy, allowing for more reliable pavement condition assessments. Model performance is evaluated against real-world data, confirming its effectiveness in predicting fatigue cracking with an overall R² of 0.9. This study’s findings provide valuable insights for pavement maintenance and rehabilitation planning, helping transportation agencies optimize repair schedules and reduce costs. This research highlights the growing role of AI in pavement engineering, demonstrating how machine learning can improve infrastructure management. By integrating ANN-based predictive analytics, road agencies can enhance decision-making, leading to more durable and cost-effective pavement systems for the future. Full article

The online calibration algorithm for camera and LiDAR helps solve the problem of multi-sensor fusion and is of great significance in autonomous driving perception algorithms. Existing online calibration algorithms fail to account for both real-time performance and accuracy. High-precision calibration algorithms require high hardware requirements, while it is difficult for lightweight calibration algorithms to meet the accuracy requirements. Secondly, sensor noise, vibration, and changes in environmental conditions may reduce calibration accuracy. In addition, due to the large domain differences between different public datasets, the existing online calibration algorithms are unstable for various datasets and have poor algorithm robustness. To solve the above problems, we propose an online calibration algorithm based on multi-scale cost volume fusion. First, a multi-layer convolutional network is used to downsample and concatenate the camera RGB data and LiDAR point cloud data to obtain three-scale feature maps. The latter is then subjected to feature concatenation and group-wise correlation processing to generate three sets of cost volumes of different scales. After that, all the cost volumes are spliced and sent to the pose estimation module. After post-processing, the translation and rotation matrix between the camera and LiDAR coordinate systems can be obtained. We tested and verified this method on the KITTI odometry dataset and measured the average translation error of the calibration results to be 0.278 cm, the average rotation error to be 0.020°, and the single frame took 23 ms, reaching the advanced level. Full article

The increasing global reliance on water resources has necessitated improvements in turfgrass irrigation efficiency. This study aimed to compare measured field data with predicted data on irrigation water distribution in turfgrass rootzones to verify and enhance the accuracy of the HYDRUS-2D simulation model. Data were collected under controlled greenhouse conditions across unvegetated plots with two- and three-layered rootzone construction methods, each receiving 10 mm of water (intensity of 10 mm h⁻¹) via subsurface drip irrigation (SDI) or a sprinkler (SPR). The water content was monitored at various depths and time intervals. The hydraulic soil parameters required for the simulation model were determined through laboratory analysis. The HYDRUS-2D model was used for testing the sensitivity of various soil hydraulic parameters and subsequently for model calibration. Sensitivity analysis revealed that soil hydraulic property shape factor (n) was most sensitive, followed by factor θ_s^w (water content at saturation for the wetting water retention curve). The model calibration based on shape factors n and α_w either in Layer 1 for SPR variants or in both upper layers for SDI variants yielded the highest improvement in model efficiency values (NSEs). The calibrated models exhibited good overall performance, achieving NSEs up to 0.81 for the SDI variants and 0.75 for the SPR variants. The results of the irrigation management evaluation showed that, under SPR, dividing the irrigation amount of 10 mm into multiple smaller applications resulted in a higher soil storage of irrigation water (SOIL_S) and lower drainage flux (DFLU) compared to single large applications. Furthermore, the model data under the hybrid irrigation approach (HYBRID-IA) utilizing SPR and SDI indicated, after 48 h of observation, the following order in SOIL_S (mm of water storage in the topmost 50 cm of soil): HYBRID-IA3 (3.61 mm) > SDI-IA4 (2.53 mm) > SPR-IA3 (0.38 mm). HYDRUS-2D shows promise as an effective tool for optimizing irrigation management in turfgrass rootzones, although further refinement may be necessary for specific rootzone/irrigation combinations. This modeling approach has the potential to optimize irrigation management, improving water-use efficiency, sustainability, and ecosystem services in urban turfgrass management. Full article

Cold forming offers high dimensional accuracy, energy and cost efficiency in the mass production of highly stressed components but is also associated with high tribological loads. Complex lubrication systems are required to ensure smooth production. As environmental standards rise, traditional zinc phosphate-based lubricants are to be replaced by less harmful single-layer systems. However, these new lubricants are temperature-sensitive, which requires precise knowledge of the temperatures in the forming zone for optimal design. Due to high compressive stress, conventional measuring methods cannot measure temperatures directly in the forming zone. In this work, lubricants are expanded into a temperature sensor using thermochromic pigments so that temperatures can be measured directly in the forming zone. This work outlines the selection and integration of the indicators, the development of a calibration method for thermochromic lubricants to characterize the correlation between colour value and temperature. It is shown that the lubricant behaviour does not deteriorate up to concentrations of 10%. The transfer of the measurement methodology from the laboratory application to the industrial multi-stage process has been successfully implemented and local temperature peaks are measured directly in the contact zone and correspond to the simulation results. The results of the work show an approach to closing the gap identified in existing research work, namely that the temperature cannot be measured directly in the forming zone during cold forging. The measuring system developed can be transferred to various processes in the future and contribute to the identification of correlations between temperature, lubricant failure and wear. Full article

A graphene sample (EGr) was obtained in a single-step synthesis by electrochemical exfoliation of graphite rods. A combination of 0.05 M ammonium sulfate and 0.05 M ammonium thiocyanate was employed, leading to a graphene sample composed of few-layer, multi-layer and graphene oxide flakes. Due to the mild exfoliation conditions, large sheets with linear sizes in the range of tens to hundreds of micrometers were produced. The LSV technique gave information about the effect of catechol concentration on the electrochemical signal of bare and graphene-modified electrodes. Based on the resulting calibration plots, the corresponding analytical parameters (linear range, sensitivity, limit of quantification and limit of detection) were calculated for each electrode. In the case of the EGr/GC electrode the linear range was from 6 × 10⁻⁷ to 1 × 10⁻⁴ M catechol. The detection limit was low (1.82 × 10⁻⁷ M) while the quantification limit was 6 × 10⁻⁷ M. The sensitivity was five times higher than that corresponding to bare GC, proving the excellent electro-catalytic properties of the graphene-modified electrode. The practical applicability of the graphene-modified electrode was tested in tap water, obtaining an excellent recovery of 102%. Full article

Thermal bridges significantly affect the thermal performance of light steel framing systems due to the high thermal conductivity of steel. The objective of this study is to identify modifications on the steel profiles to reduce heat flux and improve the thermal resistance of both single- and double-layer wall panels. Three approaches were analyzed: (i) slotted steel studs, (ii) integration of less-conductive materials into the web section, and (iii) modifications to web geometry. A numerical model was calibrated based on experimental data and used to perform dynamic simulations with different configurations. Results show that incorporating less-conductive materials, such as rigid polyamide, achieved a heat flux reduction of up to 98%, while optimized slotted patterns reduced heat flux by up to 90%. The results also demonstrated that all web modifications effectively reduced heat flux through the wall, with approaches (i) and (ii) showing the greatest impact. The shape of the slots also has an important impact on the heat flux. The most effective strategy for enhancing the thermal performance of the steel studs was the use of a less-conductive material. Full article

In this paper, technical code analysis and recognition of the defect signal of the suppression rigging based on a convolutional neural network are carried out given the difficulty and low recognition rate of the defect detection and recognition of the suppression rigging. Firstly, the magnetic induction signal of the suppression rigging defects is collected using CM-801 (Anshan, China), Kalman filtering is used to screen and pre-process the collected data, and the noise reduction data are presented in the form of a cloud image. The pressed rigging defect data set is constructed, and the region of broken wire defect and stress in the image is calibrated. The single-stage object detection algorithm YOLOv5 (You Only Look Once) based on convolutional neural network model calculation is used, the scale detection layer and positioning loss function of the YOLOv5 algorithm are improved and optimized, and the improved YOLOv5 algorithm is used for experiments. The experimental results show that the detection accuracy of the convolution neural network model can reach 97.1%, which can effectively identify the defect signal of the suppressed rigging. Full article

Nuclear power plant decommissioning requires the rapid and accurate classification of radioactive waste in narrow spaces and under time constraints. Photon-counting detector technology offers an effective solution for the quick classification and detection of radioactive hotspots in a decommissioning environment. This paper characterizes a 5 mm CdTe Timepix3 detector and evaluates its feasibility as a single-layer Compton camera. The sensor’s electron mobility–lifetime product and resistivity are studied across bias voltages ranging from −100 V to −3000 V, obtaining values of ${\mathrm{\mu }}_e{\tau }_e$ = (1.2 ± 0.1) × 10⁻³ cm²V⁻¹, and two linear regions with resistivities of ${\rho }_I=(5.8\pm 0.2) \mathrm{G}\Omega \mathrm{cm}$ and ${\rho }_{II}=(4.1\pm 0.1) \mathrm{G}\Omega \mathrm{cm}$. Additionally, two calibration methodologies are assessed to determine the most suitable for Compton applications, achieving an energy resolution of 16.3 $\mathrm{k}$$\mathrm{e}\mathrm{V}$ for the ¹³⁷Cs photopeak. The electron’s drift time in the sensor is estimated to be (122.3 ± 7.4) ns using cosmic muons. Finally, a Compton reconstruction of two simultaneous point-like sources is performed, demonstrating the detector’s capability to accurately locate radiation hotspots with a ∼51 cm resolution. Full article

To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required parameters are calibrated against several characteristic cell experiments. As a result, it is hardly possible to describe the behavior of the individual battery components, which reduces the level of detail. In this work, a new data-driven material model is presented, which not only provides the homogenized behavior but also information about the components. For this purpose, a representative volume element (RVE) of the cell structure is created. To determine the constitutive material models of the individual components, different characterization tests are performed. A novel method for carrying out single-layer compression tests is presented for the characterization in the thickness direction. The parameterized RVE is subjected to a large number of load cases using first-order homogenization theory. This data basis is used to train an artificial neural network (ANN), which is then implemented in commercial FEA software LS-DYNA R9.3.1 and is thus available as a material model. This novel data-driven material model not only provides the stress–strain relationship, but also outputs information about the condition of the components, such as the thinning of the separator. The material model is validated against two characteristic cell experiments. A three-point-bending test and an indentation test of the cell is used for this purpose. Finally, the influence of the architecture of the neural network on the computational effort is discussed. Full article