Search Results (3,899)

In this work, a partitioned coupling algorithm is developed by integrating the improved discrete velocity method (IDVM) with the lattice Boltzmann flux solver (LBFS) to address conjugate heat transfer (CHT) in microscale systems across all flow regimes. Specifically, the flow field is solved by the IDVM, generating a heat flux that acts as a Neumann boundary condition at the interface for the solid domain. Subsequently, the LBFS calculates the thermal distribution inside the solid, and the updated temperature at the interface is then applied to the fluid computations as a Dirichlet condition. The proposed framework effectively combines the strengths of the IDVM in modeling rarefied gas flows with the advantages of the LBFS in handling heat conduction in complex geometries. Crucially, the current approach implicitly captures temperature jump discontinuities at the conjugate boundary, bypassing the requirement for supplementary jump conditions. To evaluate its performance, several CHT test cases involving rarefied gas in microchannels were conducted. Computational evidence suggests that the scheme is robust across diverse flow regimes. Full article

In the field of industrial robotic vision, accurate recognition and localization of transparent objects pose significant challenges. Unlike opaque objects, transparent objects are difficult to distinguish in RGB images, and due to refraction and reflection, their depth information often suffers from large-area missing or erroneous values, leading to failed grasp pose prediction. Therefore, depth completion is crucial for transparent object grasping tasks. However, existing depth completion methods still exhibit obvious limitations. Multi-stage optimization methods, while achieving high accuracy, involve complex pipelines and high computational costs. Single-stage end-to-end networks, when processing sparse edge features of transparent objects that are also contaminated by background interference, are constrained by the receptive field and smoothing effect of conventional convolutions, often resulting in contour blurring and loss of geometric details. Moreover, existing methods still lack sufficient capability in modeling multi-directional gradient variations of transparent objects under complex backgrounds. To address these issues, this paper proposes ADDFNet for transparent object depth completion, achieving synergistic improvement in accuracy and robustness through two key designs: MDAM and CMFR. To tackle the problem of sparse edge features of transparent objects that are easily disturbed by noise, we design the Multi-directional Differential Attention Module (MDAM), which explicitly extracts multi-directional gradient information through multi-branch differential convolution. Within MDAM, we introduce the Detail Enhancement Differential sub-Module (DEDM) and the Dynamic Convolution with Symmetry-enhanced Geometry Attention sub-module (DSCA) to enhance the network’s perception of fine contours and improve global–local synergistic modeling capability. To address insufficient cross-modal information interaction, we introduce the Cross-Modal Feature Refinement (CMFR) module, which utilizes RGB context to guide and enhance depth features layer by layer during the encoding stage, improving the accuracy and robustness of depth completion while mitigating feature degradation caused by traditional simple fusion approaches. Experimental results on the ClearPose and TransCG datasets demonstrate that ADDFNet outperforms comparison methods in terms of RMSE, REL, MAE, and threshold accuracy metrics, exhibiting more stable performance in edge recovery and internal detail reconstruction of transparent objects. Full article

Quantitative characterization of permeability in porous media represents a central problem in filtration theory, geosciences, and materials engineering. Standard numerical approaches, including finite element methods and Lattice Boltzmann simulations, typically require extensive domain-specific expertise together with specialized computational software. This motivates the development of computationally simpler and more accessible geometric approaches applicable directly to binary volumetric data. We introduce a novel algorithmic framework for the analysis of porous structures that reformulates permeability-related characterization in terms of discrete geometry and graph-based computation. The method combines parallel raster-grid and graph representations of a binarized three-dimensional CT image. The principal transport-limiting feature of the pore network, interpreted as the minimal constriction governing connectivity, is identified through iterative morphological dilation coupled with a three-dimensional scanline seed-fill procedure. In addition, a dichotomous bisection strategy is proposed to accelerate the determination of the critical bottleneck scale. The proposed methodology was evaluated on five volumetric datasets of size 100 × 100 × 100 voxels obtained from CT-derived porous structures. Experimental results demonstrate that dilation- and erosion-based formulations yield equivalent estimates of the bottleneck parameter in four of the five investigated samples. Furthermore, incorporation of the bisection optimization reduces computational time in three-dimensional experiments by approximately 50% relative to sequential iteration. The presented approach provides a computationally efficient and fully open-source alternative to conventional physics-based permeability solvers for binary porous media. The resulting bottleneck parameter b should be interpreted as a discrete geometric invariant characterizing the pore-network connectivity and minimal transport cross-section. It is not intended to replace the absolute permeability coefficient K appearing in Darcy’s law, but rather to serve as an independent structural descriptor suitable for comparative and topological analysis of porous systems. Full article

Simulating wildfires in large areas is commonly achieved by methods based on cellular automata (CA), especially when the available computational resources are limited. However, the discrete nature of CA, in both the space and time domains, often leads to systematic distortions and artifacts in the generated fire perimeter geometry. This article presents a hybrid wildfire simulation framework that integrates a two-dimensional CA model with an ant colony optimization (ACO) algorithm to improve fire perimeter shape representation. The proposed approach employs ACO to iteratively adjust the coefficients integrated into the local CA transition rules, under varying environmental and forestry fuel conditions. This optimization step corrects geometric inconsistencies arising from the underlying nature of a CA model, while preserving their computational advantages. The hybrid implementation is evaluated through simulation experiments under varying environmental and fuel conditions, demonstrating improved agreement between simulated and reference fire perimeters, compared to a baseline CA model. Full article

Road traffic noise in urban streets is shaped not only by traffic sources but also by sound propagation through the surrounding street geometry. Existing prediction methods are still largely source-oriented, and receptor-specific models that rely on street morphology alone remain uncommon. We developed and compared interpretable machine-learning models to predict a cyclist-side sound pressure level (SPL) under fixed source conditions, using 12 spatial parameters extracted from 5060 street sections on 195 streets in Harbin, China. Acoustic simulations were performed in ODEON under fixed source-power conditions, and four models—Linear Regression, support vector regression (SVR), extreme gradient boosting (XGBoost), and Random Forest (RF)—were evaluated through an illustrative 80/20 split, 20 repeated random 80/20 splits, and 20 road-name-based grouped holdout repetitions. The nonlinear models consistently outperformed the linear baseline. Under grouped holdout validation, XGBoost achieved the highest predictive accuracy (R² = 0.953 ± 0.018, RMSE = 0.583 ± 0.119 dB, MAE = 0.418 ± 0.082 dB). RF reached comparable accuracy (R² = 0.938 ± 0.041, RMSE = 0.662 ± 0.210 dB, MAE = 0.453 ± 0.128 dB) and was retained for the interpretation of feature importance and marginal response patterns. A computation-time comparison based on 93 representative ODEON simulations showed that ODEON required a median of 2 min 33 s per street section, whereas the trained models predicted all 5060 sections in 0.013 s with XGBoost and 0.143 s with RF. The RF-based interpretation identified vehicle-lane width, sidewalk width, and near-zone cross-sectional enclosure degree as the most influential variables. Width-related parameters dominated cyclist-side SPL prediction, while enclosure-related parameters became more relevant mainly under narrower width conditions. The framework is therefore intended as a comparative morphology-screening tool under fixed source conditions, not as a predictor of real-world traffic noise under varying traffic states. Full article

This paper analyzes the vibrational characteristics of a novel sandwich plate configuration composed of an auxetic honeycomb (AH) core and laminated functionally graded carbon nanotube-reinforced composite (FG-CNTRC) face sheets, hereafter referred to as the SD-AuCNT plate. Based on Reddy’s third-order shear deformation theory (SDT), which accurately accounts for transverse shear effects without requiring shear correction factors, the equations of motion are derived using Hamilton’s principle and subsequently solved using a pb-2 Ritz formulation combined with the Newmark time integration scheme for dynamic response analysis. By combining an auxetic core with negative Poisson’s ratio characteristics and laminated FG-CNTRC face sheets featuring tailored CNT distribution patterns and orientations, the hybrid SD-AuCNT plate can improve structural stiffness, energy absorption, and dynamic performance; however, it has not been thoroughly investigated in the existing literature. After verifying the accuracy of the proposed computational procedure, the effects of auxetic core geometry, CNT distribution patterns, thickness ratios, and boundary conditions on the natural frequencies and transient responses of the plate are comprehensively investigated. The results provide new insights into the dynamic behavior of advanced sandwich plates and offer practical guidance for the design of high-performance lightweight structures in aerospace, marine, defense, and other engineering applications. Full article

Collaborative aircraft design environments face significant challenges in intuitive geometry manipulation and tool integration. This research develops an AI-assisted interface using the Model Context Protocol (MCP) to bridge natural language commands with established aerospace tools. The approach integrates large language models with three specialized applications for optimization, visualization, and aircraft geometry modification. Results demonstrate successful implementation, enabling designers to accomplish complex tasks such as multi-objective optimization and empennage reconfiguration through conversational prompts. While occasional AI misinterpretations required prompt refinement, the system proved effective at translating intent into precise tool operations. The study concludes that MCP provides a viable framework for creating intuitive design interfaces while maintaining accuracy via integration with domain-specific computational methods. Full article

Understanding the mechanical behavior of valve materials and the hemodynamic characteristics of blood flow is important for improving prosthetic heart valve design. In this study, a comprehensive computational investigation was conducted to evaluate the biomechanical and hemodynamic behavior of a three-dimensional tricuspid valve model constructed from reported prosthetic valve geometries. The structural response of the valve was evaluated using linear elastic, viscoelastic, and hyperelastic constitutive models for four different materials: pyrolytic carbon, polyurethane, porcine tissue, and bovine tissue. The results demonstrated clear material-dependent trends. Pyrolytic carbon exhibited negligible deformation (1.7166 × 10⁻⁸ m), confirming its rigid mechanical behavior, whereas biological tissues showed greater compliance, with the largest deformation observed for the bovine hyperelastic model (9.6837 × 10⁻⁵ m). Hyperelastic tissue models produced lower peak von Mises stresses (1.3951 × 10⁴–1.8603 × 10⁴ Pa) than the corresponding linear elastic tissue models (2.6842 × 10⁴–2.7017 × 10⁴ Pa), indicating improved stress redistribution under nonlinear deformation. Polyurethane showed intermediate mechanical behavior, with moderate deformation and lower stress under viscoelastic modeling than under the linear elastic assumption, suggesting its potential as a polymeric alternative to traditional valve materials. The Computational Fluid Dynamics (CFD) analysis of the rigid open valve geometry revealed a central velocity jet with a peak velocity of approximately 0.092 m/s, localized vortex formation with a maximum vorticity magnitude of about 177 s⁻¹ and a peak instantaneous wall shear stress of 1.32 Pa near the leaflet edges and valve opening. Overall, the results highlight the trade-off between rigidity, compliance, and durability among prosthetic valve materials and suggest that polyurethane may provide a balanced alternative for tricuspid valve replacement. Full article

This paper introduces a geometric framework for analyzing finite-energy signals observed with additive noise by representing them as points on statistical manifolds equipped with the Fisher–Rao metric. Each signal is associated with a parameter vector θ, which defines a unique probability distribution $p\left(\mathit{x}\right|\mathit{\theta })$ on a statistical manifold. We propose a unified approach based on the normal multivariate model to describe a raw signal mixed with additive stationary noise. In the approach considered, the background noise is typically assumed to be stationary, whereas the unknown signal is regarded as deterministic. Leveraging tools from information geometry, we compute geodesic equations for the statistical manifolds. We re-derive known results regarding the multivariate normal models and extend them to the signal processing domain. We show that in some cases, the geodesic equations can be solved to obtain a closed-form expression of the Fisher–Rao distance. This expression corresponds to a minimum bound when the sub-manifold is not geodesic, revealing a fundamental geometric constraint in signal parameter estimation. We introduce the spectral distance function, which characterizes the influence of each spectral component of the signals on the Fisher–Rao distance. Our findings provide theoretical insights for signal clustering and machine learning applications, where geometric distances can characterize classification and estimation tasks. Full article

To mitigate large-scale blackout risks in urban distribution systems under typhoon-induced extreme weather and to reduce post-disaster restoration costs, this study proposes a resilience-oriented spatiotemporal co-optimization framework integrating transportation networks, power grids, and distributed energy resources. First, a city-scale typhoon spatiotemporal model is established, integrating static wind field, dynamic evolution, and trajectory-based mobility with urban-geometry-driven wind speed correction to characterize the spatiotemporal progression of extreme wind hazards. Second, the time-varying failure rates of distribution network components are quantified by explicitly accounting for network topology correlations, while the spatiotemporal dispatchability and output characteristics of distributed resources under disaster conditions are systematically modeled. Third, a pre-disaster proactive deployment model is formulated to minimize load curtailment costs and resource allocation expenditures. The model integrates active network reconfiguration with coordinated placement of distributed generation (DG) and mobile energy storage systems (MESSs), enabling resilience-enhancing pre-positioning strategies. Subsequently, a post-disaster restoration scheduling model is developed with the objective of minimizing unserved load. By embedding traffic flow constraints and optimal path computation under disrupted transportation conditions, the proposed framework realizes spatiotemporal coordination among MESSs, DG, and electric vehicles (EVs), thereby accelerating system-level recovery. Finally, the effectiveness of the proposed strategy is validated on a 51-node urban distribution system located in eastern coastal China, demonstrating significant improvements in restoration performance and resilience enhancement. Full article

Wheel hubs in heavy-duty agricultural trailers operate under demanding conditions comprising rough terrain, impact loads, and highly variable load spectra. Current design practice relies predominantly on experience-based sizing rather than systematic multi-physics analysis. This study presents an integrated design methodology combining finite element analysis (FEA), density-based topology optimisation, and computational fluid dynamics (CFD) to concurrently improve the structural and tribological performance of a GGG40 spheroidal graphite cast iron agricultural trailer wheel hub. A reference commercial hub geometry was modelled and analysed under multiple load conditions with a safety factor of 5. Critical stress regions were identified, and the free design volume was optimised while preserving all functional surfaces. The optimised design achieved 35% mass reduction (14.9 to 9.6 kg), 30% lower maximum von Mises stress (235 to 165 MPa), and up to 40% stress reduction in the bearing seat region. Oil-circulation channels integrated into the bearing housing raised mean lubrication flow velocity by 28% and eliminated stagnation zones, yielding a more homogeneous oil-film distribution and directly benefiting bearing tribological performance. The proposed framework provides a manufacturable engineering methodology that concurrently addresses structural integrity and lubrication performance in agricultural wheel hub design. Full article

Additive manufacturing (AM) enables customized, efficient restorative workflows, though the accuracy of 3D-printed restorations may be compromised by polymerization, sintering shrinkage, and post-processing. This study evaluated the geometric accuracy of 3D-printed partial restorations compared with the computer-aided design (CAD) reference. The null hypothesis stated that no significant differences would be found between Varseo Smile Crown^plus (by BEGO, Italy) and IRIXMax (by DWS System, Italy) materials, which are printed and cured with different technologies. A model was prepared for an overlay and designed with a 1.5 mm uniform thickness. Restorations were produced in two groups with two different printing processes: DLP (digital light processing)-printed Varseo Smile Crown^plus and SLA (stereolithography)-printed IRIXMax. Six samples per group were printed at 90° orientation and scanned. Meshes were aligned to the master geometry via pre-alignment and ICP (Iterative Closest Point) registration. Deviations were quantified in CloudCompare using mean, standard deviation (SD), and 90th percentile values. IRIXMax showed the lowest deviations from the ideal geometry, while Varseo Smile Crown^plus exhibited greater variability. Pairwise comparisons found IRIXMax significantly more accurate than Varseo Smile Crown^plus. Color maps confirmed material-specific deviation patterns. IRIXMax provided the highest geometric accuracy. Material-specific calibration is essential for reliable 3D-printed definitive restorations. Full article

Three-dimensional (3D) orebody boundary modeling primarily relies on spatial interpolation methods, such as radial basis functions (RBFs). However, these methods struggle with large datasets and require gradient or normal constraints for stable geometric extrapolation. This study proposes an adaptive finite difference implicit-modeling method, which avoids gradient information and can handle complex 3D orebody boundaries from large-scale, irregular datasets. We utilized difference operators for hanging and constrained octree nodes and applied adaptive density-based smoothing to reduce artifacts from sparse data, enabling complex boundary construction on nearly one million non-uniform control points. We used octree-based convolutional neural networks to fuse spatial features across octree levels, merging points with similar local geometries into the same finest-level cells. This enabled optimal adaptive octree mesh partitioning that accounts for spatial similarity among control points while controlling the total mesh count. Using this adaptive octree mesh, a finite difference scheme suitable for non-uniform mesh structures was constructed. The method outperforms traditional RBFs and uniform-grid finite difference methods in model accuracy, computational efficiency, and memory usage, exhibiting a robust performance across various data distribution patterns. Full article

Point cloud registration in environments lacking rich textures or containing repetitive structures remains highly susceptible to misalignments. The core challenge lies in balancing the demand for extracting highly distinctive local features with the computational cost of global context modeling. In this paper, we propose a robust registration framework that efficiently combines rotation-equivariant geometric representations with state space models of linear complexity to mitigate feature ambiguity and mismatch. First, a multivariate geometric encoding mechanism is embedded within convolutional layers, enhancing local feature distinctiveness under strict rotation equivariance by explicitly leveraging surface properties. Second, to efficiently establish long-range spatial dependencies, we replace standard dense attention with a hybrid geometry-state aggregation module. This module integrates local geometric self-attention with the Mamba architecture, strengthening focus on overlapping regions without the quadratic computational burden. Finally, we optimize the generated correspondences through a physically consistent hypothesis generator to compute reliable rigid transformation results. On standard benchmarks, our framework demonstrates exceptional robustness to ambiguous matches, achieving a 96.3% registration recall on the 3DMatch dataset and outstanding accuracy on the KITTI dataset. Full article

Digital-avatar systems still provide limited control over emotionally expressive behavior in human–computer interaction, especially in Large Language Model (LLM)-based chatbots and virtual assistants with personalized visual embodiments. To address this problem, we propose Multimodal Avatar Generation (MAVAGEN), a multimodal avatar generation framework for synthesizing upper-body digital avatars with personalized appearance and controllable emotional expression. The user specifies the desired gender and age, as well as provides a short text input from which the target emotional state is inferred. MAVAGEN then retrieves an identity image from the HaGRIDv2-1M corpus and generates an avatar clip with synchronized facial expressions, hand gestures, and expressive speech. The framework uses the following six feature streams: textual features, emotion-distribution features, landmark-based pose features, depth-geometry features, RGB-appearance features, and acoustic features. In a quantitative evaluation against recent human animation methods, MAVAGEN achieves the best overall avatar quality, with FID 48.20, FVD 592.00, SSIM 0.741, Sync-C 7.40, HKC 0.929, HKV 25.30, CSIM 0.563, and EmoAcc 0.88. Ablation results show that emotion and acoustic features contribute most to emotional agreement, while landmark-based pose and depth features improve geometric and motion stability. These results support the practical use of MAVAGEN in personalized LLM-based assistants and other emotion-sensitive interactive systems. Full article