Search Results (213)

Freshwater accumulation is one of the most striking observations in the Beaufort Gyre (BG) region in the Arctic Ocean. A 39-year simulation, using the validated high-resolution, geometrical-fitting, unstructured grid Finite-Volume Community Ocean Model for the Arctic Ocean, aimed to investigate the contributions of coastal currents and their interannual variability to this phenomenon. The model reasonably reproduced the interannual variability of freshwater content (FWC) in the BG region. Analysis revealed the constructive role of Ekman pumping in supplying FWC, while the lateral flux generally acts to remove FWC from the region. The disparity between Ekman pumping and lateral flux drives the interannual variability of total FWC, with accumulation occurring when the downward Ekman FWC flux surpasses the net outflow-induced lateral FWC flux. Since 2007, there has been a significant increase in downward Ekman pumping, accompanied by a rise in net outflow lateral flux, indicating heightened variability of FWC in the BG region. The model results suggested that the coastal flow over the Arctic continental shelf underwent dramatic changes, especially during summer, and these changes were partially due to increased freshwater and sea ice melting. Increased lateral FWC flux during summer has become a competitive source for unprecedented seasonal freshwater accumulation in the BG region. Flow intensification over the North American coast is influenced by increased freshwater runoff, including the Firth, Kobuk, and Mackenzie Rivers. Interannual FWC variation in the Beaufort Sea could be influenced by the changes in slope flow, with the water originating in part from the Barents and Kara Seas. Full article

To support the increasing complexity of innovation, design, and performance evaluation in the maritime industry, a ship-specific computational fluid dynamics (CFD) software suite tailored to incompressible viscous flow is required. This study utilizes the MarineFlow marine fluid dynamics code to explore numerical simulation schemes for cylindrical flow problems across a broad range of Reynolds numbers (1–10⁷) that are applicable to self-developed codes. Additionally, an analysis of the flow around a cylinder is conducted from the perspective of code developers. Various grid types and turbulence model schemes are employed to analyze and compare the drag coefficient, separation points, and pressure distribution characteristics of the cylinder. The results obtained from these simulations are then contrasted with those derived from commercial CFD software to assess their accuracy. Despite the presence of certain numerical artifacts, within the Reynolds number range of 1–10⁵, the unstructured grids combined with the laminar flow models effectively capture experimental data. Further exploration of the transitional Reynolds number range ($Re$ = $2\times {10}^5$–$6\times {10}^5$) shows a consistent decreasing trend in the mean drag coefficient, although significant deviations from theoretical predictions are evident. From the perspective of code developers, this study aims to reveal the limitations of current computational schemes and code architecture in accurately capturing flow dynamics within the transitional Reynolds number range. This provides a crucial basis for future optimization of turbulence models and algorithmic improvements, which are essential for the continued development of self-developed CFD codes and their engineering applications. Full article

The high-temperature engine nozzle is a critical component of a rocket motor, and its stability and performance are significantly influenced by internal high-temperature gas radiative heat transfer. Due to the non-gray nature of the nozzle medium and the complexity of the Radiative Transfer Equation (RTE), rapid and accurate simulation of radiative heat transfer is crucial for engineering applications. This paper presents a high-efficiency solution coupling the Full-Spectrum Correlated k-Distribution (FSCK) model with the Null-Collision Monte Carlo Method (NCMCM). To address the inherent computational bottleneck of linear traversal in unstructured grids, a hybrid ray-localization model integrating KD-tree and Bounding Volume Hierarchy (BVH) is proposed. This model shifts the search mechanism from element-wise iteration to spatial topological indexing, achieving logarithmic search complexity and significantly mitigating the sensitivity of computational cost to grid scale. Furthermore, a collaborative MPI–OpenMP parallel framework is established to maximize hardware utilization, where an optimized guided scheduling strategy effectively counteracts the stochastic load imbalances encountered in traditional static schemes. Results indicate that the proposed method reduces the total execution time to approximately 1/4 compared to traditional models. Simulations identify the convergent section as the primary radiation zone, where CO₂ contributes less to the radiative source term than H₂O under high-temperature conditions. Full article

In contrast to structured urban settings, road networks in post-disaster or unstructured wildland environments are often incomplete or compromised. Navigation in these contexts requires navigating complex terrains and mitigating potential hazards that impede unmanned ground vehicles (UGVs). While high-mobility off-road vehicles are specifically designed to traverse challenging features like ditches and steep slopes, traditional path planning algorithms often fail to exploit these capabilities. These algorithms typically suffer from a binary focus, either relying strictly on road networks or ignoring them altogether, thereby neglecting the synergy between infrastructure and vehicle mobility. This chapter introduces a global path planning method based on traversability analysis and terrain matching to bridge this gap. The methodology incorporates a grid-based traversability evaluation, a road network expansion algorithm for densifying critical segments, and a unified planning strategy. By correlating terrain characteristics with vehicle mobility limits and optimizing the road network density, the proposed framework achieves an integrated on-road and off-road planning solution that maximizes the operational efficiency of high-mobility vehicles in degraded environments. Full article

Artificial reefs (ARs) are eco-friendly coastal protection infrastructures that mitigate wave-induced erosion while maintaining hydrodynamic connectivity and supporting ecological functions. This study evaluates the protective efficacy of a shellfish-algae reef system—a new type of AR—within the Houlong Bay coastal restoration project (Quanzhou, China) using an integrated numerical modeling approach. A coupled model system was established, incorporating MIKE 21 FM for hydrodynamics, MIKE 21 SW for waves, and MIKE ZERO ST for sediment transport, using unstructured triangular grids to resolve complex coastal topography. The model was validated against field data, including tidal currents and wave heights, showing good agreement. Pre-implementation simulations identified key coastal issues: insufficient wave attenuation in the southern fishery port segment, which results in localized erosion. Post-project simulations demonstrate that the novel integrated system—comprising shellfish-algae reefs, broad gentle beaches, and coastal vegetation—effectively reduced nearshore current speeds by approximately 0.15 m/s and attenuated significant wave heights by up to 70% during typhoon events. Short-term (1-year) sediment evolution showed mild deposition (0.1–0.8 m) at the toe of the artificial beach, which is consistent with design expectations. Long-term (10-year) simulations further confirmed coastal stability, with minimal long-term shoreline retreat (maximum 15 m) and low net alongshore sediment transport (annual average: 800 m³). This study provides a validated, data-driven reference for the design and implementation of AR-based restoration strategies in semi-enclosed bays, highlighting their dual role in erosion control and sustainable coastal management. Full article

Surface defects in hydraulic concrete structures exhibit extreme topological heterogeneity. and are frequently obscured by unstructured environmental noise. Conventional detection models, constrained by fixed-grid convolutions, often fail to effectively capture these irregular geometries or suppress background artifacts. To address these challenges, this study proposes the Adaptive Local–Global Synergistic Perception Network (ALGSP-Net). First, to overcome geometric constraints, the Defect-aware Receptive Field Aggregation and Adaptive Dynamic Receptive Field modules are introduced. Instead of rigid sampling, this design adaptively modulates the receptive field to align with defect morphologies, ensuring the precise encapsulation of slender cracks and interlaced spalling. Second, a dual-stream gating fusion strategy is employed to mitigate semantic ambiguity. This mechanism leverages global context to calibrate local feature responses, effectively filtering background interference while enhancing cross-scale alignment. Experimental results on the self-constructed SDD-HCS dataset demonstrate that the method achieves an average Precision of 77.46% and an mAP50 of 72.78% across six defect categories. Comparative analysis confirms that ALGSP-Net outperforms state-of-the-art benchmarks in both accuracy and robustness, providing a reliable solution for the intelligent maintenance of hydraulic infrastructure. Full article

The application of two-dimensional (2D) hydrologic and hydraulic modeling tools is increasing for overland flow simulation, as they represent spatial changes in depth, velocity, and flow conditions more accurately. Recently, the US Army Corps HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) added the capability to import an unstructured 2D mesh, which enables the routing of excess precipitation across the mesh, as a fully distributed hydrological model. In HEC-HMS, the 2D diffusion-wave component functions as a hydrologic transform representing overland flow routing. In contrast, HEC-RAS 2D (Hydrologic Engineering Center-River Analysis System), initially applied to river flow simulation, can apply either the 2D shallow-water equations or the 2D diffusion-wave option. Similarly to HEC-HMS, HEC-RAS also includes rain-on-grid (RoG) capability and infiltration algorithms, and in this fashion has some hydrological modeling capabilities. Still, while HEC-HMS is capable of representing extended-period hydrological simulations, HEC-RAS hydrological capabilities are limited to event-based simulations, as there are no provisions to represent abstractions such as evapotranspiration or groundwater/baseflow contributions together. Studies performing a direct comparison between the HEC-HMS RoG and HEC-RAS RoG approaches for representing urban hydrology remain scarce. This study aims to fill that gap by assessing their performance in Moore’s Mill Creek Watershed, in Lee County, Alabama, with a focus on continuous rainfall-runoff modeling. Both models run on the same unstructured mesh and use identical rainfall, terrain, land-use, and soil data. Model simulations are compared over an extended period to evaluate simulated depth, velocity, and flow hydrographs against field observations. The comparison shows HEC-HMS’s superior performance for extended simulation and provides practical guidance on parameter alignment, data needs, and tool selection. Full article

Drivable area (DA) detection in unstructured off-road environments remains challenging for unmanned ground vehicles (UGVs) due to limited field-of-view, persistent occlusions, and the inherent limitations of individual sensors. While existing fusion approaches combine aerial and ground perspectives, they often struggle with misaligned spatiotemporal viewpoints, dynamic environmental changes, and ineffective feature integration, particularly at intersections or under long-range occlusion. To address these issues, this paper proposes a cooperative air–ground perception framework based on multi-source data fusion. Our three-stage system first introduces DynCoANet, a semantic segmentation network incorporating directional strip convolution and connectivity attention to extract topologically consistent road structures from UAV imagery. Second, an enhanced particle filter with semantic road constraints and diversity-preserving resampling achieves robust cross-view localization between UAV maps and UGV LiDAR. Finally, a distance-adaptive fusion transformer (DAFT) dynamically fuses UAV semantic features with LiDAR BEV representations via confidence-guided cross-attention, balancing geometric precision and semantic richness according to spatial distance. Extensive evaluations demonstrate the effectiveness of our approach: on the DeepGlobe road extraction dataset, DynCoANet attains an IoU of 61.14%; cross-view localization on KITTI sequences reduces average position error by approximately 10%; and DA detection on OpenSatMap outperforms Grid-DATrNet by 8.42% in accuracy for large-scale regions (400 m × 400 m). Real-world experiments with a coordinated UAV-UGV platform confirm the framework’s robustness in occlusion-heavy and geometrically complex scenarios. This work provides a unified solution for reliable DA perception through tightly coupled cross-modal alignment and adaptive fusion. Full article

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

Autonomous navigation in cluttered and dynamic industrial environments remains a major challenge for mobile robots. Traditional occupancy-grid and geometric planning approaches often struggle in such unstructured settings due to partial observability, sensor noise, and the frequent presence of moving agents (machinery, vehicles, humans). These limitations seriously undermine long-term reliability and safety compliance—both essential for Industry 4.0 applications. This paper introduces S3PM, a lightweight entropy-regularized framework for simultaneous mapping and path planning that operates directly on dense 3D point clouds. Its key innovation is a dynamics-aware entropy field that fuses per-voxel occupancy probabilities with motion cues derived from residual optical flow. Each voxel is assigned a risk-weighted entropy score that accounts for both geometric uncertainty and predicted object dynamics. This representation enables (i) robust differentiation between reliable free space and ambiguous/hazardous regions, (ii) proactive collision avoidance, and (iii) real-time trajectory replanning. The resulting multi-objective cost function effectively balances path length, smoothness, safety margins, and expected information gain, while maintaining high computational efficiency through voxel hashing and incremental distance transforms. Extensive experiments in both real-world and simulated settings, conducted on a Raspberry Pi 5 (with and without the Hailo-8 NPU), show that S3PM achieves 18–27% higher IoU in static/dynamic segmentation, 0.94–0.97 AUC in motion detection, and 30–45% fewer collisions compared to OctoMap + RRT* and standard probabilistic baselines. The full pipeline runs at 12–15 Hz on the bare Pi 5 and 25–30 Hz with NPU acceleration, making S3PM highly suitable for deployment on resource-constrained embedded platforms. Full article

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

This paper presents a unified framework for constructing partially unstructured B-spline transfinite finite elements with arbitrary nodal distributions. Three novel, distinct classes of elements are investigated and compared with older single Coons-patch elements. The first consists of classical transfinite elements reformulated using B-spline basis functions. The second includes elements defined by arbitrary control point networks arranged in parallel layers along one direction. The third features arbitrarily placed boundary nodes combined with a tensor-product structure in the interior. For all three classes, novel macro-element formulations are introduced, enabling flexible and customizable nodal configurations while preserving the partition of unity property. The key innovation lies in reinterpreting the generalized coefficients as discrete samples of an underlying continuous univariate function, which is independently approximated at each station in the transfinite element. This perspective generalizes the classical transfinite interpolation by allowing both the blending functions and the univariate trial functions to be defined using non-cardinal bases such as Bernstein polynomials or B-splines, offering enhanced adaptability for complex geometries and nonuniform node layouts. Full article

The expansion of electronic health records (EHRs) has generated a large amount of unstructured textual data, such as clinical notes and medical reports, which contain diagnostic and prognostic information. Effective classification of these textual medical notes is critical for improving clinical decision support and healthcare data management. This study presents a statistically rigorous comparative analysis of four traditional machine learning algorithms—Random Forest, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine—for multiclass classification of medical notes into four disease categories: Neoplasms, Digestive System Diseases, Nervous System Diseases, and Cardiovascular Diseases. A dataset containing 9633 labeled medical notes was preprocessed through text cleaning, lemmatization, stop-word removal, and vectorization using term frequency-inverse document frequency (TF–IDF) representation. The models were trained and optimized through GridSearchCV with 5-fold cross-validation and evaluated across five independent stratified 90-10 train–test splits. Evaluation metrics, including accuracy, precision, recall, F1-score, and multiclass ROC-AUC, were used to assess model performance. Logistic Regression demonstrated the strongest overall performance, achieving an average accuracy of 0.8469 and high macro and weighted F1 scores, followed by Support Vector Machine and Multinomial Naive Bayes. Misclassification patterns revealed substantial lexical overlap between digestive and neurological disease notes, underscoring the limitations of TF–IDF representations in capturing deeper semantic distinctions. These findings confirm that traditional machine learning models remain robust, interpretable, and computationally efficient tools for textual medical note classification, and the study establishes a transparent and reproducible benchmark that provides a solid foundation for future methodological advancements in clinical natural language processing. Full article

Accurate sea surface temperature (SST) forecasting in coastal upwelling systems requires predictive models capable of representing complex oceanic geometries. This work revisits grid-to-mesh coupling strategies in Graph Neural Networks (GNNs) and analyzes how mesh topology and connectivity influence prediction accuracy and artifact formation. This standard coupling process is a significant source of discretization errors and spurious numerical artifacts that compromise the final forecast’s accuracy. Using daily Copernicus SST and 10 m wind reanalysis data from 2000 to 2020 over the Canary Islands and the Northwest African region, we evaluate four mesh configurations under varying grid-to-mesh connection densities. We analyze two structured meshes and propose two new unstructured meshes for which their nodes are distributed according to the bathymetry of the ocean region. The results show that forecast errors exhibit geometric patterns equivalent to order-k Voronoi tessellations generated by the k-nearest neighbor association rule. Bathymetry-aware meshes with $k=3$ and $k=4$ grid-to-mesh connections significantly reduce polygonal artifacts and improve long-term coherence, achieving up to 30% lower $\mathrm{RMSE}$ relative to structured baselines. These findings reveal that the underlying geometry, rather than node count alone, governs error propagation in autoregressive GNNs. The proposed analysis framework provides a clear understanding of the implications of grid-to-mesh connections and establishes a foundation for artifact-aware, geometry-adaptive learning in operational oceanography. Full article

The construction of a high-order shape function is a key and difficulty for unstructured grid mesh and sliding boundary problems. In this paper, a construction method of space-time absolute nodal coordinate formulation quadrilateral cable (SACQ) is proposed, and the accuracy of the SACQ element is studied and verified with three different applications. First, the shape function of SACQ is constructed with spatiotemporal reduction coordinates, and the action integral of SACQ is composed with the Lagrangian function and discrete with perspective transformation. Second, the numerical convergence region is discussed and determined with the Courant number. Furthermore, a space-time nodal dislocation and its relation with the Courant number are studied. The simulation and verification are focusing on some realistic problems. Finally, a one-sided impact, a free-flexible pendulum, a taut string with a sliding boundary and a deployable guyed mast under an impact transverse wave are simulated. In these problems, an unstructured grid meshed with SACQ has similar energy convergence and accuracy to a structured grid but shows better efficiency. Full article