Irregular Geometry Research Articles

Three-dimensional visualization and detection of early bruise in apple based on near-infrared hyperspectral imaging coupled with geometrical influence correction

Visible-near-infrared (Vis-NIR) spectral imaging holds great promise for the automatic detection of fruit defects. However, uneven brightness resulting from fruit geometry and the limitations of one-directional imaging significantly restrict the current detection to a limited area. This study presents a rotation measurement system that combines a line-scan NIR-hyperspectral imaging (HSI) camera with a laser profile. A total of 72 apple samples with bruises in the central and edge regions were prepared. A 360° scanning approach was employed to collect HSI and shape data from the entire surface of the samples over a 6-h post-bruising period. Height and angle corrections were applied to eliminate the surface geometric influences on the HSI data, resulting in improved reflectance spectrum uniformity. A two-step principal component analysis method was employed for image enhancement, followed by a straightforward bruise detection technique using global segmentation and connected-domain selection. The results demonstrated an overall improvement in bruise detection over time. Moreover, the correction significantly enhanced the detection accuracy. After 6 h of bruising, the corrected data achieved a classification accuracy of 90.3% and an identification rate of 83.3% for central bruises and 61.1% for edge bruises, whereas the uncorrected data yielded 70.8%, 58.3%, and 31.9%, respectively. Thus, this study successfully detected early bruising across the entire surface of apples and improved the detection in low-intensity edge areas. The proposed method has the potential to contribute to the comprehensive evaluation of agricultural products with irregular geometries.

Modelling of grain size effects in progressive microforming using CPFEM

Progressive microforming is widely recognized as one of the most efficient and desirable methods of mass production in micromanufacturing arena. To predict the deformation behaviour and size effects of materials induced in progressive microforming, the finite element method (FEM) employed for modelling the microforming process needs to account for microstructure details and deformation/failure mechanisms of the materials. This led to the development of the novel crystal plasticity finite element method (CPFEM) and cohesive zone model (CZM). Previous research and application of CPFEM have been mainly limited to simple deformations such as uniaxial tension and compression, whereas the new method can provide physical insights into how the grain size affects the interplay between crystallographic slip and mechanical twinning in complex microforming, and further material deformation during sheet blanking. A case study was conducted to manufacture a hexagonal socket part using a three-step progressive microforming system, with the comparison between experiments and CPFEM simulations focusing on microstructure evolution, deformation load, and product quality. The CPFEM was found to be more reliable than the conventional FEM in predicting complex deformation, particularly in microstructure and texture evolution, dimensional accuracy and irregular geometries. Results showed that the total height of the part increases with the decreasing grain size, while the head diameter rises with grain size. Simulations successfully anticipated the distributions of dead metal zones and shear bands and identified hole and rollover geometries and corresponding grain size effects. In conclusion, this research facilitates the understanding of grain size effects on the deformation behaviour in progressive microforming and presents a novel approach and strategy for modelling, prediction, and product quality assurance in complex micro deformation and forming processes.

High-Order Well-Balanced Finite Difference AWENO Schemes for Shallow Water Flows along Channels with Irregular Geometry

High-Order Well-Balanced Finite Difference AWENO Schemes for Shallow Water Flows along Channels with Irregular Geometry

Response Reduction Factor for Structures Irregularities in Plan and Elevation Taking into Account Soil-Structure Interaction

The structure's ability to release energy through inelastic behaviour is reflected in the response reduction factor (R). It results from a combination of redundancy, ductility, and over strength. Thus, determining R with accurate values plays a critical function in the process of seismic design. This study aims to determine the response factor of structures of reinforced concrete (RC). With different structural irregularities, additionally, determine the impact of the interaction between soil and structure (SSI) using non-linear static pushover analysis was adopted in numerical simulation by using SAP2000. The selected irregularities included elevational and in-plan changes It was determined that, in comparison to regular buildings, buildings with irregular vertical geometries had poorer inelastic seismic capacities. Therefore, R is less than that which is stated in the ECP 2020, so it ought to be reduced by 15–40% before the design stage. The outcomes demonstrated that It isn't concluded that irregularity has a considerable effect on weak soil (C). The reduction in R, considering (SSI), reached 20.3% and 13.1% for fixed and isolated supports, respectively, in case of loose soil. Moreover, it became obvious that for the same structure the stiffer the base soil the greater the R's value.

Solving time fractional partial differential equations with variable coefficients using a spatio-temporal meshless method

<p>This paper presents a novel spatio-temporal meshless method (STMM) for solving the time fractional partial differential equations (TFPDEs) with variable coefficients based on the space-time metric. The main idea of the STMM is to directly approximate the solutions of fractional PDEs by using a multiquadric function with the space-time distance within a space-time scale framework. Compared with the existing methods, the present meshless STMM entirely avoids the difference approximation of fractional temporal derivatives and can be easily applied to complicated irregular geometries. Furthermore, both regular and irregular nodal distribution can be used without loss of accuracy. For these reasons, this new space-time meshless method could be regarded as a competitive alternative to the conventional numerical algorithms based on difference decomposition for solving the TFPDEs with variable coefficients. Numerical experiments confirm the ability and accuracy of the proposed methodology.</p>

RGB pixel information fusion segmentation of dense point cloud data for transmission and distribution 3D digital corridors

Abstract Point cloud segmentation, as a key link in 3D point cloud data processing, can realize power transmission and distribution positioning, object identification, classification, and feature extraction, which helps to refine power grid management. In this paper, firstly, dense point cloud transmission and distribution 3D digital corridor modeling is carried out. Alignment splicing and noise reduction are carried out after obtaining the original dense point cloud. Contour line extraction, geometric modeling, and texture mapping are realized after processing the data to ultimately realize the transmission and distribution of 3D digitization. Then, the conversion formula for the pixel coordinate system and world coordinate system is derived to extract features from point clouds. Finally, a distance-based feature fusion method is designed to extract spatial features from point clouds and use the joint attention layer to segment them by fusing RGB pixel information. The original dense point cloud of a transmission and distribution digital corridor is segmented using the model presented in this paper for application after testing the dataset. It is found that the under-segmentation ratio of this paper’s algorithm is 0.96%, 3.44%, and 2.87% for the three scenarios of regular, irregular, and multi-targets, respectively, which is much lower than that of RANSAC+ECS with FCM + ECS. The intersection and concatenation ratios of this paper’s algorithm for the scenarios of irregular geometry as well as multi-target objects are 91.49% and 89.56%. It is much higher than 64.31% and 72.17% for RANSAC + ECS and 76.85% and 60.91% for FCM + ECS, which illustrates that this paper’s algorithm has a significant advantage in performance. In this study, the target point cloud can be segmented with high accuracy from the dense point cloud of a 3D model of power transmission and distribution with a large amount of data, effectively avoiding the phenomenon of under-segmentation and over-segmentation and contributing to the accurate control of power grid data.

Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation of Indoor Scenes.

In recent years, sparse voxel-based methods have become the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being oblivious to the underlying geometry, voxel-based methods suffer from ambiguous features on spatially close objects and struggle with handling complex and irregular geometries due to the lack of geodesic information. In view of this, we present Voxel-Mesh Network (VMNet), a novel 3D deep architecture that operates on the voxel and mesh representations leveraging both the Euclidean and geodesic information. Intuitively, the Euclidean information extracted from voxels can offer contextual cues representing interactions between nearby objects, while the geodesic information extracted from meshes can help separate objects that are spatially close but have disconnected surfaces. To incorporate such information from the two domains, we design an intra-domain attentive module for effective feature aggregation and an inter-domain attentive module for adaptive feature fusion. Experimental results validate the effectiveness of VMNet: specifically, on the challenging ScanNet dataset for large-scale segmentation of indoor scenes, it outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74.6% vs 72.5% and 73.6% in mIoU) with a simpler network structure (17M vs 30M and 38M parameters).

LATTICE BOLTZMANN ANALYSIS OF MIXED CONVECTION IN A LID-DRIVEN CAVITY WITH AN INNER HOT ELLIPTICAL BLOCK: EFFECT OF BLOCK INCLINATION

This study is a computational analysis of hydrodynamic and thermal behavior of combined natural and forced convection with a moving wall in a square enclosure containing an elliptical block. This inner block is exposed to a hot temperature. The enclosure is full of air which is cooled on its side walls by a low temperature. The remaining surfaces are thermally insulated, and the upper surface is sliding at a steady speed <i>u</i><sub>0</sub>. In particular, the impact of various factors related to geometry, such as the emplacement and the elliptical block inclination are analyzed for the range of the dimensionless parameter Richardson number (0.01 ≤ Ri ≤ 100) by using lattice Boltzmann method (LBM) with single relaxation time model (SRT). LBM offers the advantage of handling irregular geometries and curved boundaries without requiring complex mesh generation, simplifying computations, and reducing computational costs in comparison to the traditional methods. The findings are in the forms of streamlines, temperature contours, the mean Nusselt number, and the fluid mean temperature inside the enclosure. The results show that for the prevailing natural convection and for the centrally placed elliptical block, the dynamic and thermal fields are almost indicated by symmetry with regard to the mid-vertical of the enclosure. Moreover, the inclination of the elliptical block prevents the enhancement of the heat transfer for all positions of the block; except when the block is located close to the upper surface, the inclination improves the rate of heat transfer especially for Ri ≤ 1.

Artificial neural network to predict the structural compliance of irregular geometries considering volume constraints

Artificial neural network to predict the structural compliance of irregular geometries considering volume constraints

Performance of 2D-spectral finite element method in dynamic analysis of concrete gravity dams

This article presents a two-dimensional spectral finite element formulation for dynamic analysis of concrete gravity dams. Finite element method (FEM) is the one of the most extensively used analysis tools for solving problems of dynamic analysis of structures. However, it needs extensive computational resources and time for large structures, leading to the development of alternate computationally efficient modeling techniques over the past few decades. Some of these techniques are broadly termed as “Spectral Finite Element Methods” (SFEMs). Frequency domain-based SFEM (FDSFEM) and time domain-based SFEM (TDSFEM) are the most commonly used SFEMs found in the literature. This article provides a brief review of these methods identifying their key advantages and disadvantages; and explores the feasibility of applying such methods in the dynamic analysis of concrete gravity dams. Here, the TDSFEM has been used to perform the dynamic time history analysis of a concrete gravity dam due to its relative advantages over FDSFEM, which is challenging for irregular geometry and finite domain. Sensitivity analysis and convergence studies have been performed using 4-noded and 9-noded elements. The foundation of the dam has been modeled using two-dimensional infinite elements in both FEM and TDSFEM analysis, which is a novel application in the case of TDSFEM. This article quantifies the computational efficiency of TDSFEM over the conventional FEM by comparing the computation time in both methods and thus emphasizes the applicability of TDSFEM in dynamic analysis, especially in case of complex geometries and large two-dimensional structures (like concrete gravity dam). The results demonstrate the computational efficiency of TDSFEM over FEM when higher order elements are considered. Modal analysis and time history analysis results point that the use of higher order elements of TDSFEM can be a viable alternative to the use of the conventional FEM leading to significant saving in computational time with reasonable accuracy for dynamic analysis of large structures like concrete gravity dams.

Investigation and Application of Perforation Optimization Method on Shale Gas Horizontal Well with Numerical Simulation of Multicluster Fracturing under Dense-Segment Pattern

Investigation and Application of Perforation Optimization Method on Shale Gas Horizontal Well with Numerical Simulation of Multicluster Fracturing under Dense-Segment Pattern

Geometric Stochastic Resonance in an Asymmetric T-Shaped Chamber

The investigation of a Brownian particle subjected to an AC force that diffuses within a T-shaped chamber was conducted. This T-shaped chamber is composed of a strip cavity and a trapezoidal cavity positioned below it. The interplay between the AC force and asymmetric geometry creates a spatially bistable potential perpendicular to the AC force. With the assistance of noise, the particles can transition between two stable states and oscillate along the AC force at corresponding amplitudes at every spatially stable state. The asymmetric geometry facilitates the trapezoid cavity’s ability to more easily trap the Brownian particle than the upper strip cavity in the weak noise limit. Our observations reveal that proper noise can ensure the particle’s efficient trapping within the upper strip cavity and synchronization with the AC force, indicating the occurrence of geometric stochastic resonance. The T-shaped chamber serves as a simplified model, aiding in the further understanding of geometric stochastic resonance induced by irregular geometries and enabling the manipulation of microscopic particles in various small-scale systems.

Deformation Mechanism of Aluminum, Copper, and Gold in Nanoimprint Lithography Using Molecular Dynamics Simulation.

Material deformation during nanoimprinting of aluminum (Al), copper (Cu), and gold (Au) was explored through molecular dynamics simulations. A comparative understanding of the deformation behavior of three substrate materials important for design and high-resolution pattern transfer was highlighted. In this study, we analyzed three metrics, including von Mises stresses, lattice deformation, and spring-back for the chosen materials. Of the three materials, the highest average von Mises stress of 7.80 MPa was recorded for copper, while the lowest value of 4.68 MPa was computed for the gold substrate. Relatively higher von Mises stress was observed for all three materials during the mold penetration stages; however, there was a significant reduction during the mold relaxation and retrieval stages. The Polyhedral Template Matching (PTM) method was adopted for studying the lattice dislocation of the materials. Predominantly Body-Centered Cubic (BCC) structures were observed during the deformation process and the materials regained more than 50% of their original Face-Centered Cubic (FCC) structures after mold retrieval. Gold had the lowest vertical spring-back at 6.54%, whereas aluminum had the highest average spring-back at 24.5%. Of the three materials, aluminum had the lowest imprint quality due to its irregular imprint geometry and low indentation depth after the NIL process. The findings of this research lay a foundation for the design and manufacture of Nanoimprint Lithography (NIL) molds for different applications while ensuring that the replicated structures meet the desired specifications and quality standards.

Investigation on spontaneous liquid–liquid imbibition in capillaries with varying axial geometries using lattice Boltzmann method

Spontaneous liquid–liquid imbibition in capillaries with irregular axial geometries is common in the petroleum industry. Monitoring the real-time dynamic contact angle (DCA) of the meniscus is crucial during such processes. In this work, we extend the Bell–Cameron–Lucas–Washburn (BCLW) equation by considering the axial shape of the capillaries, inertial force, and non-wetting fluid viscosity. We also develop a cascaded multi-component Shan–Chen lattice Boltzmann method (CLBM) with a modified mass-conservative curved boundary scheme to accurately simulate imbibition processes in sinusoidal capillaries. The results indicate that the DCA is highly sensitive to variations in the axial geometry of the capillary during imbibition, displaying a periodic time evolution pattern. When the axial geometry diverges, the DCA increases, and when it converges, the DCA decreases. The viscosity ratio affects the imbibition velocity, controlling the evolution period and extreme values of the DCA. A critical contact angle exists for a fixed capillary axial geometry and viscosity ratio. Continuous spontaneous imbibition occurs if the static contact angle is smaller than this critical value. However, if it exceeds this threshold, imbibition ceases within regions where axial geometry divergence. Moreover, we noticed a discrepancy in imbibition lengths predicted by the extended BCLW equation that ignores the DCA compared to those computed through the CLBM. To address this issue, we employed CLBM to monitor the DCA in real time and used the gathered data to refine the extended BCLW equation. As a result, the prediction of imbibition lengths by the extended BCLW equation for coupling the DCA became more accurate.

Measuring coating layer shape in arbitrary geometry

Coating processes are typically analyzed on systems with flat substrates, such as the Landau–Levich–Derjaguin configuration in dip coating. However, actual applications often exhibit a much wider variety of geometries. For example, dip coating is also employed as a batch process with three-dimensional substrates. After the batch dip coating process, the coating layer is likely to exhibit irregular geometries near the lower edge of the substrate; the substrate profile is not flat, and the fluid can form overhangs depending on process conditions and rheological properties. These irregularities make it impossible to define layer shape measures, such as average thickness and roughness, in traditional ways. In this study, we propose generalized measures to overcome this issue by using offset distance and curve similarity. Our measures can quantify the shape of the coating layers in arbitrary geometries and are, therefore, robust against irregularities. We applied our measures to analyze the formation of external electrodes on multi-layer ceramic capacitors by batch dip coating. Coating layer profiles during the process were acquired for the analysis using a simple machine vision technique. As a result, differences in the coating layer shapes between fluids with different rheological properties were quantified. The results show that our measures can be used to compare coating qualities in arbitrary geometries for designing optimal process conditions.

An Efficient Parallel Iterative Solver for Controlled-source Electromagnetic 3-D Adaptive Forward Modeling in General Anisotropic Media

Controlled-source electromagnetic (CSEM) surveys are moving increasingly toward realistic and complicated scenarios where the survey region may contain undulated topography, complex geometries, and electrical anisotropy media. In this paper, the adaptive finite element numerical method is employed to discrete the total electric field equation, which can provide precise electromagnetic (EM) responses even with a coarse initial mesh. The unstructured tetrahedral grid is employed to effectively address arbitrary irregular geometry and mountain terrain. Then, the flexible generalized minimum residual solver (FGMRES), auxiliary-space Maxwell pre-conditioner, and grid division technique were used to solve the large-scale linear system of equations, which can stably solve ill-conditioned problems using fewer computing resources. Finally, we conducted a numerical experiment via our newly proposed forward modeling scheme on a synthetic model with multi-contrast electrical anisotropy. It validated that accurate EM fields could be obtained against the semi-analytic solutions, and this iterative solver has good robustness for various anisotropic media. As a result, we have developed a state-of-the-art 3D CSEM anisotropic forward modeling engine, which can quickly and accurately deal with large-scale and complex geo-models.

Deep residual learning for acoustic emission source localization in A steel-concrete composite slab

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

3D Dispensing of Waterborne Polyurethane on Textile

The paper presents an investigation into the digital printing of waterborne Thermoplastic Polyurethane (TPU) ink using advanced dispensing technology. The study focuses on assessing the ink’s adhesion properties on four distinct textile substrates and explores the challenges and possibilities of applying this ink to curved textile geometries. The research employed cutting-edge digital printing equipment to precisely dispense waterborne HAPTIC® ink onto polyester, cotton, and viscose textile. Adhesion testing was conducted to evaluate the ink’s performance on each substrate, considering factors such as elongation at maximal force. The results provide valuable insights into the suitability of waterborne TPU ink for various textile types, offering guidance for potential applications in the textile industry. Furthermore, this study delves into the intricate process of digitally dispensing waterborne TPU ink onto curved textile surfaces. The investigation examines the challenges posed by irregular textile geometries, including curvature, elasticity, and stretchability, and explores innovative solutions for achieving consistent and high-quality printing results on such surfaces. The findings of this research contribute to the advancement of digital printing technology in the textile industry, offering a deeper understanding of ink-substrate interactions and paving the way for new possibilities in textile design, customization, and manufacturing.

Lattice Boltzmann simulations on transient radiative transfer problems in irregular geometries with constant or graded refractive index

This work is concerned with the transient radiative transport in inhomogeneous two- or three-dimensional semitransparent graded-index media with irregular geometry. The lattice Boltzmann method is applied to the graded-index media mapped with non-orthogonal grids to solve the radiative transfer while the finite volume method expresses the angular processing. The solution of the transient radiation from the methodology developed is firstly validated with good accuracy by considering benchmark solutions available in the literature for comparisons for various forms of geometry including a cube, elliptic cylinder as well as irregular shapes. Afterwards, the solver is used to analyze the radiative transfer in graded-index media including regular and irregular geometries, and various governing parameters such as laser and optical properties, scattering laws, incident angle of the incoming beam, refractive index, and temperature distributions. Suitable calculations of the transient radiometric heat flux and incident energy help to evaluate the time-resolved reflectance and transmittance. The results from this study indicate that the present methodology and boundary treatments are simple and suitable to deal with multidimensional radiative transfer in irregular domains.

Enhanced composite thermal conductivity by percolated networks of in-situ confined-grown carbon nanotubes

Despite the ever-increasing demand of nanofillers for thermal enhancement of polymer composites with higher thermal conductivity and irregular geometry, nanomaterials like carbon nanotubes (CNTs) have been constrained by the nonuniform dispersion and difficulty in constructing effective three-dimensional (3D) conduction network with low loading and desired isotropic or anisotropic (specific preferred heat conduction) performances. Herein, we illustrated the in-situ construction of CNT based 3D heat conduction networks with different directional performances. First, to in-situ construct an isotropic percolated conduction network, with spherical cores as support materials, we developed a confined-growth technique for CNT-core sea urchin (CNTSU) materials. With 21.0 wt.% CNTSU loading, the thermal conductivity of composites reached 1.43 ± 0.13 W/(m·K). Secondly, with aligned hexagonal boron nitride (hBN) as an anisotropic support, we constructed CNT-hBN aligned networks by in-situ CNT growth, which improved the utilization efficiency of high density hBN and reduced the thermal interface resistance between matrix and fillers. With ~ 8.5 wt.% loading, the composites possess thermal conductivity up to 0.86 ± 0.14 W/(m·K), 374% of that for neat matrix. Due to the uniformity of CNTs in hBN network, the synergistic thermal enhancement from one-dimensional (1D) + two-dimensional (2D) hybrid materials becomes more distinct. Based on the detailed experimental evidence, the importance of purposeful production of a uniformly interconnected heat conduction 3D network with desired directional performance can be observed, particularly compared with the traditional direct-mixing method. This study opens new possibilities for the preparation of high-power-density electronics packaging and interfacial materials when both directional thermal performance and complex composite geometry are simultaneously required.