Search Results (1,713)

In thermal engineering applications, finned surfaces are extensively employed to enlarge the effective heat transfer area and thereby enhance the efficiency of heat exchangers. The present study examines cylindrical tubes externally fitted with rectangular and triangular fins under the condition of a constant transverse cross-sectional area and total fin volume. For all five rectangular fin configurations analyzed, the cross-sectional area and total volume were kept constant, while for the five triangular fin configurations, these parameters were also maintained constant but were approximately half of those of the rectangular fins due to geometric characteristics. Both analytical calculations and numerical simulations using ANSYS Fluent were conducted to evaluate thermal performance across different fin thicknesses and heights. Results show that rectangular fins provide up to 9.75% higher heat flux than triangular fins at the optimal thickness; however, this improvement requires nearly a twofold increase in material consumption. The analysis further indicates that most heat transfer occurs near the fin base, where convective efficiency is highest, with effectiveness diminishing along the extended surface. These findings highlight the importance of selecting fin geometries that balance thermal performance with material economy. In conclusion, this study provides practical insights for designing more efficient and cost-effective heat exchangers. Full article

Optimal airfoil design remains a critical challenge in aerodynamic engineering, with traditional methods requiring extensive computational resources and iterative processes. This paper presents GEO-DSGA, a novel framework integrating hybrid geometric neural networks with deep symbiotic genetic algorithms for enhanced airfoil optimization. The methodology employs graph-based representations of airfoil geometries through a hybrid architecture combining graph convolutional networks with traditional deep learning, enabling precise capture of spatial geometric relationships. The parametric modeling stage utilizes CST, Bézier curves, and PARSEC methods to generate mathematically robust airfoil representations, subsequently transformed into graph structures preserving local and global shape characteristics. The optimization framework incorporates a deep symbiotic genetic algorithm enhanced with dominant feature phenotyping, applying biological symbiotic principles where design parameters achieve superior performance through mutual enhancement rather than independent optimization. This systematic exploration maintains geometric feasibility and aerodynamic validity throughout the design space. Experimental results demonstrate an 88.6% reduction in computational time while maintaining prediction accuracy within 1.5% error margin for aerodynamic coefficients across diverse operating conditions. The methodology successfully identifies airfoil geometries outperforming baseline NACA profiles by up to 12% in lift-to-drag ratio while satisfying manufacturing and structural constraints, establishing GEO-DSGA as a significant advancement in computational aerodynamic design optimization. Full article

Vacuum-assisted biopsy needle is an important tool for minimally invasive tissue sampling. Its procedural efficiency is largely compromised by the limited pneumatic efficiency and the recurrent tissue winding problem. In this study, a fluid dynamics model of vacuum-assisted biopsy is established, and its pneumatic performance is investigated. The analysis focuses on the interplay between pneumatic efficiency and structural design, particularly examining how geometric parameters influence the internal flow dynamics. The results demonstrate that the vacuum pressure applied linearly increases the flow rate. The main energy loss is located at the inlet area. Key findings reveal trade-offs between flow enhancement and winding risks, where anti-winding structures improve tissue winding but impair the pneumatic efficiency. The study can provide guidance for the structural optimization design of vacuum-assisted biopsy needles. Full article

This article proposes a methodological framework for the design of a freeform glass pavilion based on surface curvature analysis and systematic panel classification. The research methodology consists of two stages. The first stage is a historical review, presenting the development of glass-bending technologies, panelization strategies, and the significance of transparency in architecture. The analysis of selected freeform realizations aims to identify structural solutions and their limitations. The second stage involves parametric modeling in Rhino/Grasshopper, applying Gaussian and mean curvature analysis to optimize surface subdivision. Finite element method (FEM) calculations and CFD simulations complemented the process by assessing structural and environmental parameters. Based on the study, a panel classification system was developed, distinguishing flat, singly curved, and doubly curved elements. This classification enables the optimization of production costs and serves as a tool for balancing geometric, structural, and economic requirements. The presented theoretical research indicates that the relationship between geometry, structure, and economic efficiency is a key factor in the design of glass architecture. The proposed methodology supports informed decision-making in the design process. Full article

The deterioration of rocks’ mechanical properties during the late stage of water injection development significantly reduces the rock-breaking efficiency of PDC bits. In this study, X-ray diffraction mineral composition analysis and triaxial compression mechanics tests were used to systematically characterize the weakening mechanism of water injection on reservoir rocks. Based on an analysis of mechanical experimental characteristics, this study proposes a multi-scale collaborative optimization method: establish a single tooth–rock interaction model at the micro-scale through finite element simulation to optimize geometric cutting parameters; at the macro scale, adopt a differential bit design scheme. By comparing and analyzing the rock-breaking energy consumption characteristics of four-blade and five-blade bits, the most efficient rock-breaking configuration can be optimized. Based on Fluent simulation on the flow field scale, the nozzle configuration can be optimized to improve the bottom hole flow field. The research results provide important theoretical guidance and technical support for the personalized design of drill bits in the later stage of water injection development. Full article

Selective laser melting (SLM) builds metal parts layer by layer by locally melting powder with a fine laser beam, generating complex, geometry-dependent temperature gradients that govern density, microstructure, defects, and residual stresses. Resolving these gradients with high-fidelity finite-element (FE) models is prohibitively slow because the temperature field must be evaluated at dense points along every scan track across multiple layers, while the laser spot is orders of magnitude smaller than typical layer dimensions. This study replaces FE analysis with a deep neural network that predicts the end-of-build temperature field orders of magnitude faster. A benchmark part containing characteristic shape features is introduced to supply diverse training cases, and a novel control-volume-based geometry-abstraction scheme encodes arbitrary workpiece shapes into compact, learnable descriptors. Thermal simulation data from the benchmark train the network, which then predicts the residual temperature field of an unseen, geometrically dissimilar part with a mean absolute error of ~10 K and a mean relative error of ~1% across 500–1300 K. The approach thus offers a rapid, accurate surrogate for FE simulations, enabling efficient temperature-driven optimization of SLM process parameters and part designs. Full article

Powder bed fusion of copper has been extensively investigated using both laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) additive manufacturing technologies. Each technique offers unique benefits as well as specific limitations. Near-infrared (NIR) laser-based LPBF is widely accessible; however, the high reflectivity of copper limits energy absorption, thereby resulting in a narrow processing window. Although optimized parameters can yield relative densities above 97%, issues such as keyhole porosity, incomplete melting, and anisotropy remain concerns. Green lasers, with higher absorptivity in copper, offer broader process windows and enable more consistent fabrication of high-density parts with superior electrical conductivity, often reaching or exceeding 99% relative density and 100% International Annealed Copper Standard (IACS). Mechanical properties, including tensile and yield strength, are also improved, though challenges remain in surface finish and geometrical resolution. In contrast, Electron Beam Powder Bed Fusion (EB-PBF) uses high-energy electron beams in a vacuum, eliminating oxidation and leveraging copper’s high conductivity to achieve high energy absorption at lower volumetric energy densities (~80 J/mm³). This results in consistently high relative densities (>99.5%) and excellent electrical and thermal conductivity, with additional benefits including faster scanning speeds and in situ monitoring capabilities. However, EB-PBF faces its own limitations, such as surface roughness and powder smoking. This paper provides a comprehensive review of the current state of laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) powder bed fusion processes for the additive manufacturing of copper, summarizing key trends, material properties, and process innovations. Both approaches continue to evolve, with ongoing research aimed at refining these technologies to enable the reliable and efficient additive manufacturing of high-performance copper components. Full article

To address the ill-posedness of the Hessian matrix in monocular visual-inertial SLAM (Simultaneous Localization and Mapping) caused by unobservable depth of feature points, which leads to convergence difficulties and reduced robustness, this paper proposes a Pre-Dog-Leg feature optimization method based on an adaptive preconditioner. First, we propose a multi-candidate initialization method with robust characteristics. This method effectively circumvents erroneous depth initialization by introducing multiple depth assumptions and geometric consistency constraints. Second, we address the pathology of the Hessian matrix of the feature points by constructing a hybrid SPAI-Jacobi adaptive preconditioner. This preconditioner is capable of identifying matrix pathology and dynamically enabling preconditioning as a strategy. Finally, we construct a hybrid adaptive preconditioner for the traditional Dog-Leg numerical optimization method. To address the issue of degraded convergence performance when solving pathological problems, we map the pathological optimization problem from the original parameter space to a well-conditioned preconditioned space. The optimization equivalence is maintained by variable recovery. The experiments on the EuRoC dataset show that the method reduces the number of Hessian matrix conditionals by a factor of 7.9, effectively suppresses outliers, and significantly improves the overall convergence time. From the analysis of trajectory error, the absolute trajectory error is reduced by up to 16.48% relative to RVIO2 on the MH_01 sequence, 20.83% relative to VINS-mono on the MH_02 sequence, and up to 14.73% relative to VINS-mono and 34.0% relative to OpenVINS on the highly dynamic MH_05 sequence, indicating that the algorithm achieves higher localization accuracy and stronger system robustness. Full article

This research paper presents the results of an analysis conducted on a microstrip patch antenna designed to operate within the 1.559–1.591 GHz frequency band, which encompasses three major satellite constellations: GPS, Galileo and BeiDou. The objective of this study is to perform a comparative evaluation of the materials used in the antenna design, assess the geometric configuration and analyze the key performance parameters of the proposed microstrip patch antenna. Prior to the numerical modeling and simulation process, a preliminary assessment was conducted to evaluate how different substrate materials influence antenna efficiency. For instance, a comparison between FR-4 and RT Duroid 5880 dielectric substrates revealed signal attenuation differences of approximately −1 dB at the target frequency. The numerical simulations were carried out using Ansys HFSS design. The antenna was mounted on a dielectric substrate, which was also mounted on a ground plane. The microstrip antenna was fed using a coaxial cable at a single point, strategically positioned to achieve circular polarization within the operating frequency band. The aim of this study is to design and analyze a microstrip antenna that operates within the previously specified frequency range, ensuring optimal impedance matching of 50 Ω with a return loss of S₁₁ < −10 dB at the operating frequency (with these parameters also contributing to the definition of the antenna’s operational bandwidth). Furthermore, the antenna is required to provide a gain greater than 3 dB for integration into GNSS’ receivers and to achieve an Axial Ratio value below 3 dB in order to ensure circular polarization, thereby facilitating the antenna’s integration into GNSSs. Full article

The present work numerically investigates the mechanical behavior of six 6061-T6 aluminum profiles during roll bending, considering, in two specific cases, the application of the process in different bending directions (vertical and horizontal), totaling eight cases analyzed, with emphasis on the influence of multiple bending radii. Notably, two of the profiles are characterized by high geometric complexity, making their analysis particularly relevant within the scope of this study. Using the finite element method in ANSYS^® (version 2022 R2) (SOLID187 element), the study expands the previously validated model to a broader range of geometries and includes an additional validation and verification stage. The results reveal: (i) an inverse relationship between bending radius and von Mises stress, with critical values close to the material’s strength limit at smaller radii; (ii) characteristic displacement patterns for each profile, quantified through specific curve fittings; and (iii) a systematic comparison among the six profiles, highlighting stress concentrations and deformations differentiated by geometry. The simulations provide criteria for predicting forming defects and optimizing process parameters, expanding the database for industrial designs with multiple extruded profiles. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

In recent years, convolutional neural network-based object detectors have achieved extensive applications in remote sensing (RS) image interpretation. While multi-scale feature modeling optimization remains a persistent research focus, existing methods frequently overlook the symmetrical balance between feature granularity and morphological diversity, particularly when handling high-aspect-ratio RS targets with anisotropic geometries. This oversight leads to suboptimal feature representations characterized by spatial sparsity and directional bias. To address this challenge, we propose the Parallel Interleaved Convolutional Kernel Network (PICK-Net), a rotation-aware detection framework that embodies symmetry principles through dual-path feature modulation and geometrically balanced operator design. The core innovation lies in the synergistic integration of cascaded dynamic sparse sampling and symmetrically decoupled feature modulation, enabling adaptive morphological modeling of RS targets. Specifically, the Parallel Interleaved Convolution (PIC) module establishes symmetric computation patterns through mirrored kernel arrangements, effectively reducing computational redundancy while preserving directional completeness through rotational symmetry-enhanced receptive field optimization. Complementing this, the Global Complementary Attention Mechanism (GCAM) introduces bidirectional symmetry in feature recalibration, decoupling channel-wise and spatial-wise adaptations through orthogonal attention pathways that maintain equilibrium in gradient propagation. Extensive experiments on RSOD and NWPU-VHR-10 datasets demonstrate our superior performance, achieving 92.2% and 84.90% mAP, respectively, outperforming state-of-the-art methods including EfficientNet and YOLOv8. With only 12.5 M parameters, the framework achieves symmetrical optimization of accuracy-efficiency trade-offs. Ablation studies confirm that the symmetric interaction between PIC and GCAM enhances detection performance by 2.75%, particularly excelling in scenarios requiring geometric symmetry preservation, such as dense target clusters and extreme scale variations. Cross-domain validation on agricultural pest datasets further verifies its rotational symmetry generalization capability, demonstrating 84.90% accuracy in fine-grained orientation-sensitive detection tasks. Full article

Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction based on Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) models optimized by the Improved Sparrow Search Algorithm (ISSA). Nine conventional hydrochemical indicators are selected, including Ca²⁺, Mg²⁺, Na⁺+K⁺, HCO₃⁻, Cl⁻, SO₄²⁻, total hardness, alkalinity, and pH. KPCA can realize the dimensionality reduction to eliminate the redundancy of information between discriminant indices, simplify the model structure, and enhance the calculation speed of the predicted model. The penalty factor C and kernel parameter g of the SVM model are optimized by the Sparrow Search Algorithm (SSA). In addition, comparative analysis with the SVM, SSA-SVM, and ISSA-SVM models demonstrates that the KPCA and ISSA significantly enhance the classification performance of the SVM model. The KPCA-ISSA-SVM model outperforms three contrastive models in terms of accuracy, precision, recall, Kappa coefficient, Matthews Correlation Coefficient, and geometric mean values of 90.75%, 0.90, 0.88, 0.89, 0.87, and 0.89, respectively. These outcomes underscore the superior performance of the KPCA-ISSA-SVM hybrid model and its potential for effectively identifying mine water sources. This research can serve to identify the mine water sources. Full article

Global Navigation Satellite Systems (GNSSs) are widely used in many applications but can be out of use in some critical conditions. Hybrid pseudolite systems utilize ground and aero stations as pseudolites to provide positioning signals for users within the covered area. The positioning accuracy is an important performance parameter for the pseudolite system and is decided by the layout of the pseudolites. This paper proposes a layout optimization method based on an Incremental Geometric Dilution of Precision (IGDOP) model. The IGDOP considers the GDOP value into two parts. One is the fixed part corresponding to the ground stations, and the other is the varying part related to the movable aero pseudolite stations. Thus, when the aero pseudolites’ position changes, the new GDOP value could be obtained only by calculating the varying part. Then, a Monte-Carlo Genetic Algorithm (MC-GA) is proposed for the IGDOP calculation for a minimum value. This algorithm comprises two main components: first, it leverages the random sampling capability of the Monte-Carlo Algorithm to provide sample points that satisfy the sample space for the subsequent Genetic Algorithm, which serve as individuals of the initial population; subsequently, it searches for the minimum value of IGDOP via the Genetic Algorithm and determines the optimized layout of the hybrid pseudolite system. Simulations are carried out using a hybrid pseudolite system with four fixed stations and n movable stations. The results validate the developed IGDOP model and show that the approach enables scalable optimization of n − 1 movable stations via four fixed stations, providing an efficient, low-complexity solution to the system layout optimization. Full article

As Virtual Try-On (VTON) technology matures, 2D VTON methods based on diffusion models can now rapidly generate diverse and high-quality try-on results. However, with rising user demands for realism and immersion, many applications are shifting towards 3D VTON, which offers superior geometric and spatial consistency. Existing 3D VTON approaches commonly face challenges such as barriers to practical deployment, substantial memory requirements, and cross-view inconsistencies. To address these issues, we propose an efficient 3D VTON framework with robust multi-view consistency, whose core design is to decouple the monolithic 3D editing task into a four-stage cascade as follows: (1) We first reconstruct an initial 3D scene using 3D Gaussian Splatting, integrating the SMPL-X model at this stage as a strong geometric prior. By computing a normal-map loss and a geometric consistency loss, we ensure the structural stability of the initial human model across different views. (2) We employ the lightweight CatVTON to generate 2D try-on images, that provide visual guidance for the subsequent personalized fine-tuning tasks. (3) To accurately represent garment details from all angles, we partition the 2D dataset into three subsets—front, side, and back—and train a dedicated LoRA module for each subset on a pre-trained diffusion model. This strategy effectively mitigates the issue of blurred details that can occur when a single model attempts to learn global features. (4) An iterative optimization process then uses the generated 2D VTON images and specialized LoRA modules to edit the 3DGS scene, achieving 360-degree free-viewpoint VTON results. All our experiments were conducted on a single consumer-grade GPU with 24 GB of memory, a significant reduction from the 32 GB or more typically required by previous studies under similar data and parameter settings. Our method balances quality and memory requirement, significantly lowering the adoption barrier for 3D VTON technology. Full article