Geometric Features Research Articles

Numerical Investigation on Enhanced Thermal Performance of Rectangular Microchannels with Triangular Ribs

Effective thermal management becomes crucial with the rising heat produced by electronic devices. Microchannel convection shows excellent potential as a cooling solution. This study employs numerical computational techniques to examine rectangular microchannels’ flow and heat transfer parameters on a semi-octagonal structure, specifically focusing on the Reynolds number (Re) ranging from 100 to 500. An extensive analysis evaluates the heat transfer design and hydraulic performance. Furthermore, the microchannel's diodic performance is assessed by comparing the Nusselt number (Nu) and the pressure drop in both the forward and backward directions. The study demonstrates the excellent performance of the microchannels within the microstructure under low Reynolds number (Re) conditions. Emphasizing the significance of preserving the fundamental geometric characteristics while improving the fluid dynamics within the microchannel. The semi-octagonal structure microchannel design surpasses the conventional design in bidirectional heat transfer by improving spatial fluid mixing. With a Reynolds number of 300, the Nusselt number of this microchannel surpasses that of the traditional rectangular design by a considerable margin. In addition, at this Reynolds number, the thermal diodicity (Dit) and pressure drop diodicity (Dip) showed significant improvements, with values of 1.16 and 1.45, respectively. Therefore, this study comprehensively compares the enhanced heat transfer of microchannel walls under constant heat flux, highlighting the advantages of semi-octagonal structural design in improving heat transfer and hydraulic performance. The results show that this innovative design has great potential to improve the efficiency of microchannel cooling and provides an important reference for the development of efficient thermal management systems.

Velocity of ultrasound diffusion in heterogeneous media and its applications

This paper investigates the velocity of ultrasonic diffusion in heterogeneous media under the conditions where the diffuse wave approximation is valid (λ≤a). The diffusion velocity is defined as the moving speed of the peak of the energy evolution curve. The peak arrival time as a function of transport distance is calculated for infinite spaces and bounded domains. The results show that the peak arrival time is independent of the domain’s geometry, i.e. the size, shape, and boundary conditions. This is confirmed by comparing the arrival times in an infinite space and a bounded domain with a geometric feature – a surface-breaking crack of varying depth. Therefore, the velocity of ultrasonic diffusion is an intrinsic property of a medium, and the formula for the infinite three-dimensional space is sufficient to calculate the arrival time–transport distance relationship, given the diffuse properties of the medium. These findings eliminate the needs for the finite element numerical simulations in the applications to determine geometric parameters such as the crack depth using diffuse ultrasound. The diffusion velocity is a function of transport distance in general, while its far-field asymptotic value is constant regardless of the dimensionality and geometry of the domain.

Research on a 3D Point Cloud Map Learning Algorithm Based on Point Normal Constraints.

Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local consistency and density of the point cloud normal vector. A feature map learning algorithm which integrates point normal constraints, Dirichlet energy, and coupled orthogonality bias terms is proposed. Specifically, the Dirichlet energy is employed to penalize the difference between neighboring normal vectors and combined with a coupled orthogonality bias term to enhance the orthogonality between the normal vectors and the subsurface, thereby enhancing the accuracy and robustness of the learned denoising of the feature maps. Additionally, to mitigate the effect of mixing noise, a point cloud density function is introduced to rapidly capture local feature correlations. In experimental findings on the anchor public dataset, the proposed method reduces the average mean square error (MSE) by 0.005 and 0.054 compared to the MRPCA and NLD algorithms, respectively. Moreover, it improves the average signal-to-noise ratio (SNR) by 0.13 DB and 2.14 DB compared to MRPCA and AWLOP, respectively. The proposed algorithm enhances computational efficiency by 27% compared to the RSLDM method. It not only removes mixed noise but also preserves the local geometric features of the point cloud, further improving computational efficiency.

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

Smart Carbon Fiber-Reinforced Polymer Composites for Damage Sensing and On-Line Structural Health Monitoring Applications.

High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers.

TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in blood cell detection. To address blood cell detection, we developed the TW-YOLO approach, leveraging multi-scale feature fusion techniques. Firstly, traditional CNN (Convolutional Neural Network) convolution has poor recognition capabilities for certain blood cell features, so the RFAConv (Receptive Field Attention Convolution) module was incorporated into the backbone of the model to enhance its capacity to extract geometric characteristics from blood cells. At the same time, utilizing the feature pyramid architecture of YOLO (You Only Look Once), we enhanced the fusion of features at different scales by incorporating the CBAM (Convolutional Block Attention Module) in the detection head and the EMA (Efficient Multi-Scale Attention) module in the neck, thereby improving the recognition ability of blood cells. Additionally, to meet the specific needs of blood cell detection, we designed the PGI-Ghost (Programmable Gradient Information-Ghost) strategy to finely describe the gradient flow throughout the process of extracting features, further improving the model's effectiveness. Experiments on blood cell detection datasets such as BloodCell-Detection-Dataset (BCD) reveal that TW-YOLO outperforms other models by 2%, demonstrating excellent performance in the task of blood cell detection. In addition to advancing blood cell image analysis research, this work offers strong technical support for future automated medical diagnostics.

GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features.

Recent advances in high-throughput sequencing have led to an explosion of genomic and transcriptomic data, offering a wealth of protein sequence information. However, the functions of most proteins remain unannotated. Traditional experimental methods for annotation of protein functions are costly and time-consuming. Current deep learning methods typically rely on Graph Convolutional Networks to propagate features between protein residues. However, these methods fail to capture fine atomic-level geometric structural features and cannot directly compute or propagate structural features (such as distances, directions, and angles) when transmitting features, often simplifying them to scalars. Additionally, difficulties in capturing long-range dependencies limit the model's ability to identify key nodes (residues). To address these challenges, we propose a geometric graph network (GGN-GO) for predicting protein function that enriches feature extraction by capturing multi-scale geometric structural features at the atomic and residue levels. We use a geometric vector perceptron to convert these features into vector representations and aggregate them with node features for better understanding and propagation in the network. Moreover, we introduce a graph attention pooling layer captures key node information by adaptively aggregating local functional motifs, while contrastive learning enhances graph representation discriminability through random noise and different views. The experimental results show that GGN-GO outperforms six comparative methods in tasks with the most labels for both experimentally validated and predicted protein structures. Furthermore, GGN-GO identifies functional residues corresponding to those experimentally confirmed, showcasing its interpretability and the ability to pinpoint key protein regions. The code and data are available at: https://github.com/MiJia-ID/GGN-GO.

Influence of horizontal alignment elements on operating speed of vehicles on two-lane highways

Earlier studies acknowledged that the design speed does not principally assure consistency in the design of highway geometric elements. Methodologies for estimating operating speeds are limited to specific or local traffic conditions and design standards, as in various studies conducted on two-lane and multi-lane highways. The present study develops operating speed models of the vehicle types observed on horizontal curves and tangent sections on two-lane highways in southern parts of India. Geometric features of the curve section and adjacent tangent sections, such as the radius of the curve, curve length, deflection angle, degree of curvature, gradient, carriageway width, and shoulder width, are obtained from the field. The speed data collected at 24 locations on the highway, including curved and tangent gradient sections, are used to analyze and model operating speed. The results indicate that the inverse of the square root of the radius appears to be an influencing parameter for modeling the operating speed of vehicles. The operating speed is sensitive for a curve radius of up to 600 m; thereafter, the operating speed appears insignificant. On tangent sections, the gradient has a larger influence on the operating speeds of all vehicle types. The operating speed models are validated and compared with models available in the literature. The proposed models developed in the present study are helpful to traffic engineers for predicting the operating speed on two-lane highways.

Evaluation of the Implementation Status of the Nepal Rural Road Standards (NRRS) Concerning Geometrical Parameters: A Case Study of Morang District of Nepal

Maintaining the effectiveness of the transportation system is essential to economic growth. Poor road upgrading methods in many developing nations, however, are making it impossible for traffic to flow freely. Nepal developed the Nepal Rural Road Standards (NRRS) of 2055, District Transportation Master Plan (DTMP), and Municipality Transportation Master Plan (MTMP) to regulate the construction of rural road infrastructure. These guidelines provide guidelines for rural road planning, design, building, and maintenance, among other aspects. However, each geometric design has certain features, and parameter requirements depending on the region and location to be applied. This study aims to compare the geometrical features of the rural roads that have been built with the Nepal Rural Road Standards (NRRS) 2055, second revision (2071). Using a random sampling approach, six rural roads in the Morang district were selected for the data collection. To gather geometrical data, field observations and measurements were made at random chainages. This study evaluates various geometric parameters using linear and angular field measurements. Mapping conducted via smart road software ensures road geometry compliance, including aspects such as curve radius, shoulder width, gradient, sight distance, carriageway, and additional widening. The findings demonstrate that strict adherence to established standards substantially enhances road safety. Based on the results of the field observation, the geometric elements have been built according to design specifications, except for the additional widening at shoulder width and horizontal.

Advancing structural health monitoring: Deep learning-enhanced quantitative analysis of damage in composite laminates using surface strain field

Composite materials have been widely used as critical components in aerospace applications due to their excellent performance characteristics. The real-time accurate identification and quantification of various types of damage within composite material structures pose a significant challenge. This study introduces an innovative damage detection method based on strain fields, which centrally employs deep learning techniques. Utilizing the Res-Mask R–CNN, this study accurately detects and categorizes various forms of damage within composite laminates, including open holes, subsurface holes, and delamination. Moreover, this method also enables precise localization and quantification of damaged areas. A series of experiments and simulations have validated the accuracy and robustness of the network model. Damage inversion experiments demonstrate that the area error of the damaged regions has been reduced to 7.4 %, and the positional error does not exceed 3.31 mm. In simulated scenarios, the shape context distance for complex damage contours does not exceed 0.21, indicating that the critical geometric features of the damage have been successfully preserved. This study provides an effective new approach for damage detection and real-time structural health monitoring of composite laminates.

Multi-scale quantitative characterization of three-dimensional pores and fissures in deep coal and study of the evolution laws

Native pores and fissure in deep coal bodies directly influence the deformation and destruction of coal seams. The pore structure characteristics of coal tend to be complicated because of the damage caused by tectonic stress; hence, it is necessary to study the development of pore fracture in coal. In this study, two types of coal samples—primary structural coal and tectonically deformed coal—were selected from the 12403 comprehensive mining face in the Shangwan Coal Mine, part of the Shendong Coal Mine Group. The developmental characteristics, distribution patterns, and morphological and structural differences of pores and fissures in these types of structural coal were examined using X-ray diffraction, scanning electron microscopy, mercury intrusion porosimetry, and computed tomography scanning, combined with digital image processing technology. A fractal model for the evolutionary characteristics of the pore-fracture network in a coal body is proposed. The characteristics of the pore-fracture network under different measurement accuracies are also investigated. The results showed that the primary structural coal tended to develop small fissures of simple morphologies, with poor fissure connectivity and connection strength. In contrast, the morphologies of fissures in tectonically deformed coal were complex, with better fissure connectivity and connection strength, and a higher fissure rate than that of primary structural coal. A fractal model was developed to describe and characterize the geometrical features of the pore-fracture network at various measurement accuracies. The dominant pore-size segments that can be characterized are 50 nm ≤ r ≤ 10 μm and r > 10 μm, respectively. The fractal dimensions of porosity and pore-fracture network volume increase as measurement scales decrease. The accuracy and applicability of the fractal model in predicting the porosity and volume fractal dimension of the pore-fracture network across various scales were confirmed.

Plasmonic Nanotrenches with 1 nm Nanogaps for Surface-Enhanced Raman Scattering-Based Screening of His-Tagged Proteins.

This study represents a highly sensitive and selective approach to protein screening using surface-enhanced Raman scattering (SERS) facilitated by octahedral Au nanotrenches (OANTs). OANTs are a novel class of nanoparticles characterized by narrow, trench-like excavations indented into the eight facets of a Au octahedron. This unique configuration maximizes electromagnetic near-field focusing as the gap distance decreases to ∼1 nm. Owing to geometrical characteristics of the OANTs, near-field focusing can be maximized through the confinement and reflectance of light trapped within the trenches. We used Ni ions and molecular linkers to confer selective binding affinity for His-tagged proteins on the surfaces of the OANTs for SERS-based protein screening. Remarkably, SERS-based protein screening with the surface-modified OANTs yielded outstanding screening capabilities: 100% sensitivity and 100% selectivity in distinguishing His-tagged human serum albumin (HSA) from native HSA. This highlights the significantly enhanced protein screening capabilities achieved through the synergistic combination of SERS and the OANTs.

Prediction of shear stress imposed on alveolar epithelium of healthy and diseased lungs.

Lung alveoli are modeled as spherical caps, lined internally by a thin surfactant-laden liquid film, and the periodic wall shear stress exerted along the epithelium during small-amplitude radial oscillations of their wall is computed. A novel set of boundary conditions, applied at the rim, reveals the dominant role of Marangoni stresses. These stresses develop along the air/liquid interface due to spatial gradients of interfacial surfactant concentration and are transported to the wall by the action of viscosity. The effect of a variety of geometric and functional characteristics, including rim interstitial thickness, alveolar opening angle and liquid film thickness and viscosity, is interrogated, and the results are discussed in relation to the onset and evolution of acute and chronic lung diseases, such as asthmatic attacks, pulmonary emphysema and pulmonary fibrosis.

Isolated Spiral Solutions of the Ginzburg–Landau Equation

Using finite element simulation, we study the main features of rotating wave solutions of the cubic complex Ginzburg–Landau equation. To focus on the characteristics of the waves themselves, we have used a circular domain to avoid the effects of irregular boundaries; we have inhibited the formation of defects using an Archimedean wave centered at the origin as the initial condition, and we have chosen a domain size long enough to contain several spiral arms but not so long as to promote long-wave instabilities. This allows us to focus on the geometric features of the solutions often overlooked in traditional works with random initial conditions in large domains. We show with our simulations that the convective and absolute stabilities differ from those predicted for plane waves. Likewise, we show that the appearance of spirals and anti-spirals can occur in any of the quadrants of the parameter space and depends fundamentally on the initial conditions. Finally, regarding the stability of spiral waves against sideband disturbances, we show that the persistence of two-dimensional spiral waves, if fulfilled, does not correspond to the Eckhaus criterion. Our simulations allow us to corroborate the idea that two-dimensional spirals depend on the size of the domain and that they require new analytical results whose trends are identified in this work.

Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore

Management of ageing masonry arch bridges entails periodic site inspections to identify signs of potential structural degradation. Previous research has focused on detecting surface cracks from images. This paper develops an alternative approach where cracks are identified from point clouds via geometric distortions. An image-based anomaly detection method called PatchCore is customized for 3D applications for this purpose. First, Fast Point Feature Histograms (FPFH) are used to extract geometric features. Then PatchCore is applied on synthetic point clouds with crack labels, generated using 3D finite element modelling (FEM) and graphical modelling. Results show that the proposed method can capture surface and internal cracks in arches. Analyses show that the method is robust against measurement noise, initial damage and masonry surface roughness, and can be applied to other bridge components. Limitations of the method in detecting small changes in curvature and in-plane geometric distortions are highlighted for further improvements.

Evaluation of foaming performance for polymer modified and virgin asphalt binders

The pavement suffers rapid deterioration as a result of the weak bonding strength exhibited by the foamed virgin asphalt in the mixture. Consequently, it is crucial to develop a modified asphalt suitable for foaming. The expansion height and bubble size of foamed asphalt can be continuously and simultaneously monitored by binocular ranging equipment. By analyzing these dynamic responses, the evolution of the spatial domain expansion characteristics and the geometric features of the surface bubbles of foamed thermoplastic resin modified asphalt (TRA) was analyzed. Meanwhile, multi-stress creep recovery (MSCR) and bending beam rheometer (BBR) tests were conducted to analyze its rheological properties. Subsequently, the dispersibility of foamed TRA in the mixture was explored. The results showed that thermoplastic resin significantly improved the physical performance stability of foamed asphalt, as evidenced by an increase in its half-life by 2–3 times compared to the virgin asphalt when the dosage of thermoplastic resin reached 8 %. Foamed modified asphalt has fewer bubbles, but it has larger bubble sizes and more uniform distribution of bubbles. Meanwhile, the TRA exhibited excellent high-temperature deformation resistance and elastic recovery ability. The dispersion uniformity of foamed TRA in the mixture was superior to that of virgin asphalt. This study serves as a valuable reference for the exploration of foaming behavior in modified asphalt and the utilization of foamed modified asphalt in the mixture.

A new perspective on denoising based on optimal transport

Abstract In the standard formulation of the classical denoising problem, one is given a probabilistic model relating a latent variable $\varTheta \in \varOmega \subset{\mathbb{R}}^{m} \; (m\ge 1)$ and an observation $Z \in{\mathbb{R}}^{d}$ according to $Z \mid \varTheta \sim p(\cdot \mid \varTheta )$ and $\varTheta \sim G^{*}$, and the goal is to construct a map to recover the latent variable from the observation. The posterior mean, a natural candidate for estimating $\varTheta $ from $Z$, attains the minimum Bayes risk (under the squared error loss) but at the expense of over-shrinking the $Z$, and in general may fail to capture the geometric features of the prior distribution $G^{*}$ (e.g. low dimensionality, discreteness, sparsity). To rectify these drawbacks, in this paper we take a new perspective on this denoising problem that is inspired by optimal transport (OT) theory and use it to study a different, OT-based, denoiser at the population level setting. We rigorously prove that, under general assumptions on the model, this OT-based denoiser is mathematically well-defined and unique, and is closely connected to the solution to a Monge OT problem. We then prove that, under appropriate identifiability assumptions on the model, the OT-based denoiser can be recovered solely from information of the marginal distribution of $Z$ and the posterior mean of the model, after solving a linear relaxation problem over a suitable space of couplings that is reminiscent of standard multimarginal OT problems. In particular, due to Tweedie’s formula, when the likelihood model $\{ p(\cdot \mid \theta ) \}_{\theta \in \varOmega }$ is an exponential family of distributions, the OT-based denoiser can be recovered solely from the marginal distribution of $Z$. In general, our family of OT-like relaxations is of interest in its own right and for the denoising problem suggests alternative numerical methods inspired by the rich literature on computational OT.

A Surface Determination Technique for Dimensional and Geometrical Analysis in Industrial X-ray Computed Tomography

Industrial X-ray computed tomography (XCT) is a nondestructive technique that can measure workpieces with non-accessible internal features or multimaterial components and assess the dimensional properties of assemblies in assembled states. Surface determination is one of its most crucial steps, which consists of determining boundary surfaces between a solid material and the surrounding air or between different solid materials. It allows for extracting surface points and assessing different features of the object from the data acquired through XCT scans. This task is particularly complex because of challenges associated with material properties, artefacts and noise. The main objective of this work is to assess not just the dimensional but also the geometric characteristics of industrial parts, which requires a more accurate definition of surface points. Therefore, we propose a new surface determination technique (SDT) in XCT to achieve subvoxel accuracy in determining surface points. We demonstrated the effectiveness and stability of our method by comparing it with other SDTs documented in the literature or with results from commercial software. The validation involved measuring various attributes, such as diameter, cylindricity and flatness, of a multi-stepped aluminium part calibrated by a coordinate measuring machine.

Obstructed Branching Networks: A Constructal Approach in Fluid Flow Investigation

Tree flow networks are common in both natural and manufactured systems. The organization of the flow hierarchy passes through the dimensional evolution of the form that is linked to the function. Thus, the objective of comparing bifurcated tube networks obtained by the constructal design method, where part of the structure is obstructed, aims to understand the effects on fluid flow and the prediction of evolutionary deviations in its function. This study compares designs of 3D tree networks with various homothety reduction factors for sizes, having tubes obstructed in some locals of the network. In this computational fluid dynamics study, the geometric constraint applied to these networks is the equal total volume of tubes at each branch level. The evaluation is based on the flow resistance of the networks. This study shows, among other things, that the performance of tree designs is highly dependent on geometric characteristics and the branching level where the obstructions are applied. The effect of the number and position of tubes obstructed in the network, as well as the alignment of the tubes across the network branching levels, on the asymmetry of fluid flow through the network is also studied. It is recommended that the results presented be considered when designing networks for engineering systems.

Emerging trends in large format additive manufacturing processes and hybrid techniques

AbstractLarge format additive manufacturing (LFAM) technologies are rapidly growing with significant potential for application in multiple technological sectors like aerospace, tooling, automotive, marine, construction, and energy. LFAM processes offer significant advantages including reduced lead time, cost, and material waste, which are further amplified due to the increased volume of the components. This review paper focuses on LFAM technologies with the highest technology readiness level, i.e., metal Directed Energy Deposition (DED), polymer extrusion, and solid-state deposition (i.e. cold spray additive manufacturing (CSAM)). Common system setups, the maximum deposition rate, and the range of processable materials, along with the achievable mechanical properties and geometrical characteristics, are outlined for each technology, both in individual and hybrid manufacturing formats. The main technological challenges are gathered and discussed to highlight the areas that require further development. Finally, the current industrial applications for LFAM technologies and the expected future developments are outlined. This review provides an overview of LFAM technologies’ current status and discusses their potential in improving the manufacturing of complex and large geometries, with a significant reduction in material and energy consumption, while ensuring high-quality and high-performance components.