Search Results (157)

In high-speed parachuting, complex turbulent phenomena (i.e., deadly vortices) may cause problems such as parachute inflation delay or even deployment failure. To address these issues, this study develops a high-precision numerical simulation dummy model in which adaptive mesh generation techniques, combined with Euler–Lagrange bidirectional coupling based on a large eddy simulation, are employed to model the multiphase flow field during parachute descent. The key parameters are adjusted, and the numerical model is refined based on wind tunnel experiments and User-Defined Functions. The bidirectional validation of the experimental and simulated data reveals the mechanism of turbulent flow formation and its evolutionary patterns around the parachutist–parachute system for different lateral and descent velocities during the high-speed descent phase. A prediction model based on a multi-information fusion neural network algorithm is further established to address the challenge in special parachuting scenarios whereby vortices in the flow field around the parachutist prevent the parachute from opening. The model integrates the Haar wavelet to extract global low-frequency features that characterize the overall structure and trends, an energy valley optimization algorithm, a convolutional neural network, a bidirectional long short-term memory network, and a self-attention mechanism to achieve one-second-ahead turbulence prediction. With nine physical quantities as inputs and descent velocity as the output indicator, the model has a Root Mean Square Error of 0.085, a Mean Absolute Error of 0.051, and a Mean Absolute Percentage Error of 0.0021. Full article

This study employs direct numerical simulation (DNS), combined with the Cartesian cut-cell method and quadtree adaptive mesh refinement, to systematically investigate the effects of surface roughness on the flow past a cylinder. The varying surface roughness is described mainly in terms of the wavenumber $\beta$. Results show that the non-uniform roughness disrupts the symmetry of flow structures and randomizes separation, forming a heterogeneous flow with coexisting small-scale groove vortices and large-scale side vortices. At $Re=100$, the drag coefficient exhibits a maximum at $\beta =30$, with a corresponding 1.48-fold increase in the peak local pressure coefficient over a smooth cylinder. The lift coefficient stabilizes between 0.375 and 0.38 for $\beta \ge 20$. The trend of force varies across different Reynolds number ranges. Beyond a critical roughness at $Re>100$, the mean drag and lift amplitude become roughness-insensitive. Full article

Defects such as gaps, delamination, and the misalignment of fibres impair the performance of carbon fibre-reinforced composites and can lead to structural failure during operation. Eddy current testing has proven to be a suitable method for detecting these defects early in the manufacturing process. However, validated electromagnetic modelling techniques are required to develop new eddy current sensors and gain a better understanding of the eddy current signals caused by different defect sizes. This paper proposes a novel finite element modelling approach to better account for fibre heterogeneity using spline approximation. Further, adaptive mesh refinement is used to reduce FEM solution errors. A defect in the form of a gap is modelled by adjusting the spline approximation accordingly. Finally, the model also accounts for inter-laminar current paths between carbon fibre layers, which are determined by four-terminal resistance measurement. The results show that the electromagnetic properties of the structure can be successfully modelled. The simulation is validated by comparing the virtual scans with eddy current scans of dry carbon fibre fabric with and without artificially manufactured gaps. Full article

Isogeometric analysis (IGA) is a numerical methodology for solving differential equations by employing basis functions that preserve the exact geometry of the domain. This approach is based on a class of mathematical functions known as NURBS (Non-Uniform Rational B-Splines). Representing a domain with NURBS entities often requires multiple patches, especially for complex geometries. Bivariate NURBS, defined as tensor products, enforce global refinements within a patch and, in multi-patch models, these refinements are propagated to other model patches. The use of T-Splines with extraordinary points offers a solution to this issue by enabling local refinements through unstructured meshes. The analysis of T-Spline models is performed using a Bézier extraction technique that relies on extraction operators that map Bézier functions to T-Spline functions. When generating a T-Spline model, careful attention is required to ensure that all T-Spline functions are linearly independent—a necessary condition for IGA—in order to form T-Splines that are suitable for analysis. In this sense, this work proposes a methodology to automate the generation of bidimensional unstructured meshes for IGA through T-Splines with extraordinary points. An algorithm for generating unstructured finite element meshes, based on domain decomposition of quadrilateral patches, is adapted to construct T-Spline models. Validation models demonstrate the methodology’s flexibility in generating locally refined isogeometric models. Full article

Reconstructing hand and object shapes from a single view during interaction remains challenging due to severe mutual occlusion and the need for high physical plausibility. To address this, we propose a novel framework for hand–object interaction reconstruction based on holistic, multi-stage collaborative optimization. Unlike methods that process hands and objects independently or apply constraints as late-stage post-processing, our model progressively enforces physical consistency and geometric accuracy throughout the entire reconstruction pipeline. Our network takes an RGB-D image as input. An adaptive feature fusion module first combines color and depth information to improve robustness against sensing uncertainties. We then introduce structural priors for 2D pose estimation and leverage texture cues to refine depth-based 3D pose initialization. Central to our approach is the iterative application of a dense mutual attention mechanism during sparse-to-dense mesh recovery, which dynamically captures interaction dependencies while refining geometry. Finally, we use a Signed Distance Function (SDF) representation explicitly designed for contact surfaces to prevent interpenetration and ensure physically plausible results. Through comprehensive experiments, our method demonstrates significant improvements on the challenging ObMan and DexYCB benchmarks, outperforming state-of-the-art techniques. Specifically, on the ObMan dataset, our approach achieves hand CD_h and object CD_o metrics of 0.077 cm² and 0.483 cm², respectively. Similarly, on the DexYCB dataset, it attains hand CD_h and object CD_o values of 0.251 cm² and 1.127 cm², respectively. Full article

Ultra-deep fault-controlled carbonate reservoirs exhibit strong heterogeneity, multi-scale fracture–cavity systems, and complex geological controls, which render conventional analytical well testing methods inadequate. This study proposes a geological model-based numerical well testing framework incorporating adaptive meshing, noise reduction, and machine-learning-assisted inversion. A multi-step workflow was established, including (i) single-well geological model extraction with localized grid refinement to capture near-wellbore flow behavior, (ii) pressure data denoising and preprocessing using low-pass filtering, and (iii) surrogate-assisted parameter inversion and sensitivity analysis using particle swarm optimization (PSO) to construct diagnostic type curves for different fracture–cavity control modes. The methodology was applied to different wells, yielding inverted fracture permeabilities ranging from approximately 140 to 480 mD and cavity permeabilities between about 110 and 220 mD. Results show that the numerical well testing method achieved an 85.7% interpretation accuracy, outperforming conventional approaches. Distinct parameter sensitivities were identified for single-, double-, and multi-cavity systems, providing a systematic basis for production allocation strategies. This integrated approach enhances the reliability of reservoir characterization and offers practical guidance for efficient development of ultra-deep carbonate reservoirs. Full article

The accurate prediction of crack initiation and propagation is essential for assessing the structural integrity of mechanically joined components and other complex assemblies. To overcome the limitations of existing finite element tools, a modular Python framework has been developed to automate three-dimensional crack growth simulations. The program combines geometric reconstruction, adaptive remeshing, and the numerical evaluation of fracture mechanics parameters within a single, fully automated workflow. The framework builds on open-source components and remains solver-independent, enabling straightforward integration with commercial or research finite element codes. A dedicated sequence of modules performs all required steps, from mesh separation and crack insertion to local submodeling, stress and displacement mapping, and iterative crack-front update, without manual interaction. The methodology was verified using a mini-compact tension (Mini-CT) specimen as a benchmark case. The numerical results demonstrate the accurate reproduction of stress intensity factors and energy release rates while achieving high computational efficiency through localized refinement. The developed approach provides a robust basis for crack growth simulations of geometrically complex or residual stress-affected structures. Its high degree of automation and flexibility makes it particularly suited for analyzing cracks in clinched and riveted joints, supporting the predictive design and durability assessment of joined lightweight structures. Full article

The breakup process of molten metal is the most critical stage in atomization powder production. Conducting systematic research on the breakup process of molten metal during gas atomization is highly significant for understanding the formation mechanism of droplets. In this study, a mathematical model suitable for investigating the breakup mechanism of molten aluminum in high-speed gas atomization was developed by coupling large eddy simulation (LES) with the volume of fluid (VOF) model, incorporating adaptive mesh refinement technology and periodic boundary conditions. Furthermore, the breakup behavior of molten aluminum in two close-coupled atomizers with distinct delivery tube end geometric (non-expanded type and expanded type, abbreviated as ET atomizer and NET atomizer) were compared. The development of surface waves, as well as the formation mechanisms of liquid cores, liquid ligaments, and liquid droplets during gas atomization, were systematically analyzed. The results indicated that Kelvin–Helmholtz instability was the predominant factor contributing to the primary breakup of molten metals. For the NET atomizer, the recirculation zone predominantly governed the primary breakup of molten metal, whereas the nitrogen main jet primarily controlled the secondary breakup. In the case of ET atomizer, under the influence of atomizing gas, a “conical” liquid core gradually formed, and numerous primary liquid droplets separated from the liquid core before undergoing secondary breakup. Compared to the ET atomizer, the NET atomizer produced droplets with a smaller average size. Full article

Smartphones have become increasingly powerful and widespread, enabling complex numerical computations that were once limited to desktop systems. However, implementing high-precision Finite Element Analysis (FEA) on mobile devices remains challenging due to constraints in memory, processing speed, and energy efficiency. This paper presents an optimized algorithmic framework for performing FEA on mobile platforms, focusing on the adaptation of meshing and iterative solver strategies to resource-limited environments. Several iterative solvers for large sparse linear systems are compared, and predefined refined meshing techniques are implemented to balance computational cost and accuracy. A two-dimensional bridge model is used to validate the proposed methods and demonstrate their numerical stability and computational efficiency on smartphones. The results confirm the feasibility of executing reliable FEA directly on mobile hardware, highlighting the potential of portable, low-cost devices as platforms for computational mechanics and algorithmic simulation in engineering and education. Full article

Accurate prediction of sound propagation in porous and dissipative media remains challenging when classical models struggle to capture the microscopic material characteristics. This work introduces the Enhanced Numerical Equivalent Acoustic Material (eNEAM) framework, extending the original NEAM formulation by combining analytical and numerical approaches. The analytical formulation provides closed-form expressions for effective impedance, complex wavenumber, and absorption coefficient under normal incidence, with and without thermo-viscous effects, enabling a direct validation against impedance-tube data and efficient initialization of finite-difference time-domain (FDTD) simulations. A parameter optimization strategy, focused on the thermolabile coefficient (${\Psi }_B$), significantly improves low-frequency absorption predictions. Robustness studies reveal that even substantial variations in model parameters generally remain within an optimal ±10% range. Additionally, a comparison between models with and without thermo-viscous losses was performed and shows that differences are negligible at macroscopic scales, which can be useful to reduce computational costs. Following computational time reduction, the adaptive mesh refinement technique employed also reduces time costs by over 50% in 1-D FDTD simulations, even without GPU acceleration. Taken together, these developments demonstrate that eNEAM provides a versatile, accurate, and computationally efficient framework for modeling porous materials, bridging experimental characterization, analytical formulations, and numerical simulations while maintaining robustness against parameter variations. Full article

Adjoint-based mesh adaptation method serves as an effective approach to improve the predictive accuracy of aerodynamic characteristics. However, viscous boundary layer grids often encounter issues such as hanging nodes, negative volumes, and directional constraints during adaptation, significantly limiting their practical application. To address these challenges, this study proposes an innovative polyhedral conversion strategy. Cells containing hanging nodes resulting from refinement are converted into polyhedra, effectively eliminating topological constraints between adjacent mesh elements. This approach is combined with surface-conforming projection and distance function-based mesh deformation techniques to ensure precise geometric representation and high mesh quality after adaptation. Numerical experiments demonstrate that the proposed viscous boundary layer mesh adaptation strategy successfully handles both refinement and coarsening of boundary layer grids. In a typical high-angle-of-attack case for the NACA0012 airfoil, the adjoint-based mesh adaptation method reduced lift coefficient error from 4.21% to 0.30% after four adaptation cycles. For the CHN-F1 low-aspect-ratio flying wing configuration, the method reduced the lift discrepancy from 10.05% to 6.65% at 40° angle of attack. The polyhedral conversion approach effectively resolves common challenges in viscous boundary layer mesh adaptation, providing a robust solution for high-fidelity prediction of aerodynamic characteristics with significantly improved accuracy. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

The Voith Schneider Propeller (VSP) operates with blades undergoing an approximately sinusoidal periodic motion along a circular path. Hydrodynamically, the continuous significant variation in the angle of attack between the blades and incoming flow, together with additional inertial effects caused by accelerated rotation, complicates the computation and measurement of hydrodynamic performance. To investigate the unsteady hydrodynamic behavior resulting from this coupled motion, a numerical model incorporating adaptive mesh refinement was developed to simulate VSP performance. Based on insights into the interaction between blade motion and hydrodynamics, an experimental platform was designed using servo motors to achieve precise synchronized blade control, enabling mutual validation between numerical simulations and transient hydrodynamic measurements. Results demonstrate that the coupled blade motion induces nonlinear variations in hydrodynamic forces. Rotational power loss limits VSP efficiency, and a negative thrust regime occurs at high advance coefficients. Rapid blade flipping leads to flow separation, identified as the primary cause of nonlinear lateral forces. The consistency between numerical and experimental results provides reliable data supporting theoretical studies. These findings offer valuable insights for optimizing motion control strategies in cycloidal propeller applications. Full article

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

Partial Differential Equation (PDE)-based hydrologic models demand extensive preprocessing, creating a bottleneck and slowing down the model setup process. Mesh generation typically lacks integration with hydrological features like river networks. We present GHOST Mesh (GMesh), an automated, watershed-oriented mesh generator built within the Watershed Modeling Framework (WMF), to address this. While primarily designed for the GHOST hydrological model, GMesh’s functionalities can be adapted for other models. GMesh enables rapid mesh generation in Python by incorporating Digital Elevation Models (DEMs), flow direction maps, network topology, and online services. The software creates Voronoi polygons that maintain connectivity between river segments and surrounding hillslopes, ensuring accurate surface–subsurface interaction representation. Key features include customizable mesh generation and variable refinement to target specific watershed areas. We applied GMesh to Iowa’s Bear Creek watershed, generating meshes from 10,000 to 30,000 elements and analyzing their effects on simulated stream flows. Results show that higher mesh resolutions enhance peak flow predictions and reduce response time discrepancies, while local refinements improve model performance with minimal additional computation. GMesh’s open-source nature streamlines mesh generation, offering researchers an efficient solution for hydrological analysis and model configuration testing. Full article