Search Results (107)

Computational modeling offers a cost-effective approach to exploring complex geotechnical behavior. This study uses PLAXIS 2D finite element software to simulate nailed soil slopes under plane strain conditions, with models calibrated against laboratory-scale experiments involving a sand-filled Perspex box, steel nail reinforcements, and a rigid foundation. The soil mass, structural elements, and reinforcements are modeled using fifteen-node triangular elements, five-node plate elements, and two-node elastic spring elements, respectively. In this paper, parametric studies evaluate the influence of slope angles, mesh density, domain dimensions, constitutive models, and reinforcement configurations. Both prototype-scale and 3D-approximated models are included to assess scale effects and spatial behavior. The results highlight the significant impact of model size and material behavior, particularly when using the Hardening Soil model and its small-strain extension. Reinforcement optimization, including nail length reduction strategies, demonstrates the potential for maintaining slope stability while improving material efficiency. Validation against experimental data confirms that the numerical models accurately capture deformation patterns and internal stress development across different construction and loading phases. This study observed that the Hardening Soil (small-strain) material model significantly improved slope performance by reducing settlements and better capturing stress behavior, especially for steep slopes. Optimized redistribution of nail lengths across the slope depth enhanced stability while reducing reinforcement usage, demonstrating a cost-effective alternative to uniform configurations. The findings offer practical guidance for optimizing nailed slope stabilization in sandy soils, supporting safer and more economical geotechnical design for real-world applications. Full article

This work introduces a block-coupled finite volume method for simulating the large-strain deformation of incompressible hyperelastic solids. Conventional displacement-based finite-volume solvers for incompressible materials often exhibit stability and convergence issues, particularly on unstructured meshes and in finite-strain regimes typical of biological tissues. To address these issues, a mixed displacement–pressure formulation is adopted and solved using a block-coupled strategy, enabling simultaneous solution of displacement and pressure increments. This eliminates the need for under-relaxation and improves robustness compared to segregated approaches. The method incorporates several enhancements, including temporally consistent Rhie–Chow interpolation, accurate treatment of traction boundary conditions, and compatibility with a wide range of constitutive models, from linear elasticity to advanced hyperelastic laws such as Holzapfel–Gasser–Ogden and Guccione. Implemented within the solids4Foam toolbox for OpenFOAM, the solver is validated against analytical and finite-element benchmarks across diverse test cases, including uniaxial extension, simple shear, pressurised cylinders, arterial wall, and idealised ventricle inflation. Results demonstrate second-order spatial and temporal accuracy, excellent agreement with reference solutions, and reliable performance in three-dimensional scenarios. The proposed approach establishes a robust foundation for fluid–structure interaction simulations in vascular and soft tissue biomechanics. Full article

Three-dimensional Human Reconstruction from Monocular Vision is a key technology in Virtual Reality and digital humans. It aims to recover the 3D structure and pose of the human body from 2D images or video. Current methods for dynamic 3D reconstruction of the human body, which are based on monocular views, have low accuracy and remain a challenging problem. This paper proposes a fast reconstruction method based on Instant Human Model (IHM) generation, which achieves highly realistic 3D reconstruction of the human body in arbitrary poses. First, the efficient dynamic human body reconstruction method, InstantAvatar, is utilized to learn the shape and appearance of the human body in different poses. However, due to its direct use of low-resolution voxels as canonical spatial human representations, it is not possible to achieve satisfactory reconstruction results on a wide range of datasets. Next, a voxel occupancy grid is initialized in the A-pose, and a voxel attention mechanism module is constructed to enhance the reconstruction effect. Finally, the Instant Human Model (IHM) method is employed to define continuous fields on the surface, enabling highly realistic dynamic 3D human reconstruction. Experimental results show that, compared to the representative InstantAvatar method, IHM achieves a 0.1% improvement in SSIM and a 2% improvement in PSNR on the PeopleSnapshot benchmark dataset, demonstrating improvements in both reconstruction quality and detail. Specifically, IHM, through voxel attention mechanisms and Mesh adaptive iterative optimization, achieves highly realistic 3D mesh models of human bodies in various poses while ensuring efficiency. Full article

Topology optimization (TO) with the variable density concept has made significant advancements in academic research and engineering applications; yet it still encounters obstacles associated with computer inefficiencies in the filtering process. This work introduces a novel filter implementation method that significantly enhances the optimization process by adapting the k-d tree data structure. The proposed method converts traditional neighborhood search operations into extremely efficient spatial searches while preserving solution accuracy. This method inherently accommodates a comprehensive array of manufacturability constraints, including symmetry, local volume control, periodic patterning, stamping-oriented overhang control, and more, without compromising computational duration. Extensive numerical examples validate the proposed method’s efficiency yielding precise, scalable designs, achieving substantial acceleration relative to conventional methods The method demonstrates specific advantage in large scale optimization challenges and intricate complex geometric restrictions, encompassing unstructured meshes. This study explores a new paradigm for effective constraint integration in topology optimization through advanced data structures, providing extensive applicability in engineering design. Full article

Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article

This study aims to address the problem that traditional helical gears generate significant axial forces during transmission and innovatively proposes a design scheme of double helical face gears (DHFG). An accurate mathematical model of the tooth surface is established using spatial meshing theory and coordinate transformation. A systematic investigation using the orthogonal test method is then conducted to analyze the influence of key parameters, such as the pinion tooth number, transmission ratio, and helix angle, on gear performance. The finite element analysis results show that the overlap degree of this double helical tooth surface gear pair in actual transmission can reach 2–3, demonstrating excellent transmission smoothness. More importantly, its unique symmetrical tooth surface structure successfully achieves the self-balancing effect of axial force. Simulation verification shows that the axial force is reduced by approximately 70% compared to traditional helical tooth surface gears, significantly reducing the load on the bearing. Finally, the prototype gear is successfully trial-produced through a five-axis machining center. Experimental tests confirmed that the contact impressions are highly consistent with the simulation results, verifying the feasibility of the design theory and manufacturing process. Full article

The High Temperature Gas-cooled Reactor (HTGR) is characterized by a high output temperature and inherent safety due to its fuel design. However, the double heterogeneity of the reactor component structure poses a challenge in thermal analyses, where fuel temperature is a key safety parameter. In this paper, a methodology for coupled thermal and neutron calculations with power discretization is developed to accurately reflect the spatial phenomena occurring in the moderator. The method is based on the point generation of power in the thermal model, and these points are determined based on the location of the fuel in the neutron model. The multi-physics interface capabilities of the Serpent code were used to investigate several configurations of the thermal model mesh and its alignment with the fuel. The impact of the radial discretization of power density was further analyzed in detail. The study revealed that the highest accuracy was achieved when the thermal model mesh was aligned with the TRi-structural ISO-tropic (TRISO) fuel particle size, and the TRISO particle arrangement was centered relative to the mesh cells. Moreover, it was found that due to the power–temperature feedback phenomena, the power is shifted outwards within a range of 1% of the relative power density. Full article

To address the limitations of poor robustness, low efficiency, and memory waste in traditional Cartesian grid generation, this paper proposes targeted improvements focusing on three key aspects: the surface mesh data structure, the Cartesian grid data structure, and the determination of the spatial relationship between the grid and the object surface. Specifically, a nested bounding box-based binary tree is established to store discrete triangles, enhancing the efficiency and accuracy of triangle data access. Secondly, a “member packaging and neighbor threading” Cartesian grid structure is constructed, improving generation speed while reducing memory consumption. Thirdly, a comprehensive Cartesian cell classification method is developed, integrating intersection checks, interior/exterior classification, and intersected cell center assessment; three strategies are further proposed to accelerate intersection checks for large-scale grids. The performance of Cartesian grid generation is compared with traditional methods and the commercial CFD code. The results demonstrate a grid generation efficiency of less than two seconds per million cells, significantly outperforming traditional methods, with a 20% reduction in memory consumption. Finally, an airflow simulation past a train illustrates the suitability of the generated grids for practical computational applications. Full article

A two-dimensional shallow flow model is developed by integrating a positivity-preserving, well-balanced central-upwind scheme with a block-structured quadtree AMR grid implemented in the Afivo open-source framework. A non-negative water depth reconstruction ensures second-order spatial accuracy and the robust treatment of wetting and drying, while coarse-grid fluxes at refinement boundaries are obtained by summing the corresponding fine-grid fluxes, thereby guaranteeing strict mass conservation between refinement levels. Mesh refinement is driven by gradients in water surface elevation, which focus resolution on regions of rapid flow variation, thereby improving both accuracy and computational efficiency. Model validation through benchmark problems and the Malpasset dam-break event show close agreement with analytical solutions, laboratory measurements, and previous numerical simulations, while achieving substantial reductions in computational cost. Full article

This study presents the Fourier–Gegenbauer integral Galerkin (FGIG) method, a new numerical framework that uniquely integrates Fourier series and Gegenbauer polynomials to solve the one-dimensional advection–diffusion (AD) equation with spatially symmetric periodic boundary conditions, achieving exponential convergence and reduced computational cost compared to traditional methods. The FGIG method uniquely combines Fourier series for spatial periodicity and Gegenbauer polynomials for temporal integration within a Galerkin framework, resulting in highly accurate numerical and semi-analytical solutions. Unlike traditional approaches, this method eliminates the need for time-stepping procedures by reformulating the problem as a system of integral equations, reducing error accumulation over long-time simulations and improving computational efficiency. Key contributions include exponential convergence rates for smooth solutions, robustness under oscillatory conditions, and an inherently parallelizable structure, enabling scalable computation for large-scale problems. Additionally, the method introduces a barycentric formulation of the shifted Gegenbauer–Gauss (SGG) quadrature to ensure high accuracy and stability for relatively low Péclet numbers. This approach simplifies calculations of integrals, making the method faster and more reliable for diverse problems. Numerical experiments presented validate the method’s superior performance over traditional techniques, such as finite difference, finite element, and spline-based methods, achieving near-machine precision with significantly fewer mesh points. These results demonstrate its potential for extending to higher-dimensional problems and diverse applications in computational mathematics and engineering. The method’s fusion of spectral precision and integral reformulation marks a significant advancement in numerical PDE solvers, offering a scalable, high-fidelity alternative to conventional time-stepping techniques. Full article

Curved roofs constructed using hyperbolic paraboloid (HP) modules are gaining popularity in structural engineering due to their unique aesthetic and structural advantages. Consequently, these studies have investigated steel bar modules based on HP geometry, focusing on how variations in geometric configuration and bar topology affect internal force distribution and overall structural performance. Each module was designed on a 4 × 4 m square plan, incorporating external bars that formed the spatial frame and internal grid bars that filled the frame’s interior. Parametric modeling was conducted using Dynamo, while structural analysis and design were performed in Autodesk Robot Structural Analysis Professional (ARSAP). Key variables included the vertical displacement of frame corners (0–1.0 m at 0.25 m intervals), the orientation and spacing of internal bar divisions, and the overall mesh topology. A total of 126 structural models were analyzed, representing four distinct bar topology variants, including both planar and non-planar mesh configurations. The results demonstrate that structural efficiency is significantly influenced by the geometry and topology of the internal bar system, with notable differences observed across the various structural types. Computational analysis revealed that asymmetric configurations of non-planar quadrilateral subdivisions yielded the highest efficiency, while symmetric arrangements proved optimal for planar panel applications. These findings, along with observed design trends, offer valuable guidance for the development and optimization of steel bar structures based on HP geometry, applicable to both single-module and multi-module configurations. Full article

Neuroscience education must convey 3D structure with clarity and accuracy. Traditional 2D renderings are limited as they lose depth information and hinder spatial understanding. High-resolution resources now exist, yet many are difficult to use in the class. Therefore, we developed an educational brain video that moves from gross to microanatomy using MRI-based models and the published literature. The pipeline used Fiji for preprocessing, MeshLab for mesh cleanup, Rhino 6 for target fixes, Houdini FX for materials, lighting, and renders, and Cinema4D for final refinement of the video. We had our brain models validated by two neuroscientists for educational fidelity. We tested the video in a class with 96 undergraduates randomized to video and lecture or lecture only. Students completed the same pretest and posttest questions. Student feedback revealed that comprehension and motivation to learn increased significantly in the group that watched the video, suggesting its potential as a useful supplement to traditional lectures. A short, well-produced 3D video can supplement lectures and improve learning in this setting. We share software versions and key parameters to support reuse. Full article

Reversible data hiding in encrypted point clouds presents unique challenges due to their unstructured geometry, absence of mesh connectivity, and high sensitivity to spatial perturbations. In this paper, we propose an efficient and secure reversible data hiding framework for encrypted point clouds, incorporating KD tree-based path planning, adaptive multi-MSB prediction, and a dual-model design. To establish a consistent spatial traversal order, a Hamiltonian path is constructed using a KD tree-accelerated nearest-neighbor algorithm. Guided by this path, a prediction-driven embedding strategy dynamically adjusts the number of most significant bits (MSBs) embedded per point, balancing capacity and reversibility while generating a label map that reflects local predictability. The label map is then compressed using Huffman coding to reduce the auxiliary overhead. For enhanced security and lossless recovery, the encrypted point cloud is divided into two complementary shares through a lightweight XOR-based (2, 2) secret sharing scheme. The Huffman tree and compressed label map are distributed across both encrypted shares, ensuring that neither share alone can reveal the original point cloud or the embedded message. Experimental evaluations on diverse 3D models demonstrate that the proposed method achieves near-optimal embedding rates, perfect reconstruction of the original model, and significant obfuscation of the geometric structure. These results confirm the practicality and robustness of the proposed framework for scenarios involving secure 3D point cloud transmission, storage, and sharing. Full article

Hydrogen is a promising zero-carbon fuel for internal combustion engines; however, the geometric optimization of injectors for low-pressure direct-injection (LPDI) systems under lean-burn conditions remains underexplored. This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. The optimization focused on two critical geometric parameters: injector hole count and injection angle. Partial indicated mean effective pressure (pIMEP) and in-cylinder NO_x emissions were selected as conflicting objectives to balance performance and emissions. Adaptive mesh refinement (AMR) was employed to resolve transient in-cylinder flow and combustion dynamics with high spatial accuracy. Among 22 evaluated configurations including both capped and uncapped designs, the injector featuring three holes at a 15.24° injection angle outperformed the baseline, delivering improved mixture uniformity, reduced knock tendency, and lower NO_x emissions. These results demonstrate the potential of geometry-based optimization for advancing hydrogen-fueled LPDI engines toward cleaner and more efficient combustion strategies. Full article

Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment–environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model–equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model–equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model–equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model–equipment interactions, and supporting progress information and operational insights. Full article