Search Results (248)

Topology optimization is a powerful methodology for designing lightweight, economical, and efficient structures. However, traditional approaches often face challenges such as numerical instabilities and high computational costs, limiting their practical applicability. Recently, radial basis function (RBF)-based and neural network-based methods have emerged as promising alternatives through the reparameterization of the density field. Despite their potential, these methods typically rely on isotropic basis functions or static feature encodings, which limit their ability to capture fine-scale structural details, particularly in high-aspect-ratio features such as slender bar-like members and in geometrically symmetric structural patterns. To address this research gap, this paper introduces a novel Geometric-enhanced Neural Network (GeNN) for topology optimization based on Anisotropic Radial Basis Functions (ARBFs). By embedding ARBFs into the neural network framework, the proposed method provides a more geometrically informed density representation and inherently suppresses checkerboard patterns without additional filtering techniques. The proposed GeNN framework is thoroughly validated on benchmark problems, including several representative symmetric structural layouts, demonstrating improved computational efficiency compared to traditional methods and other neural-network-based topology optimization methods. In addition, the proposed method demonstrates strong scalability across various optimization problems. Notably, GeNN successfully optimized a 256 m-long bridge involving millions of degrees of freedom within ten minutes on a standard personal computer. This advancement demonstrates the practical potential of the proposed method for large-scale civil engineering applications. Full article

This paper proposes a unified vision–language framework that translates user instructions into navigation for the mobile base and actions for the manipulator in indoor environments. In general, occupancy grid maps constructed via SLAM capture solely the geometric layout of the environment. This renders the robot incapable of leveraging the semantic information required for object distinction. The proposed method encodes semantic information from vision–language models and the robot’s pose in a textual format, referred to as a semantic topological graph. Specifically, the models including GroundingDINO, LG EXAONE, and SAM2 extract object-level semantic information, which is subsequently used to identify room characteristics. A large language model then interprets user instructions to identify the final destination for navigation within the semantic topological graph, followed by reasoning to determine the suitable action network. Notably, the proposed text-based representation facilitates a substantial reduction in inference time, and its effectiveness is validated through real-world experiments. Full article

Strengthening square reinforced concrete (RC) columns with full ultra-high-performance fiber-reinforced concrete (UHPFRC) jacketing is highly effective, but such complete wrapping is often impractical due to architectural or geometric constraints. Previous studies have not systematically examined the performance of partial-coverage UHPFRC patterns for these sections. This study numerically investigates the axial performance of square RC columns strengthened with strategically arranged UHPFRC elements—including horizontal shortcuts, vertical strips, and hybrid configurations—using finite element analysis in ABAQUS. Key parameters include jacket thickness, element dimensions, column height, and reinforcement details. Results show that a 10 mm full UHPFRC jacket more than doubles axial capacity (+105.9% for 800 mm columns), with significant gains in stiffness. Vertical strips enhance strength but reduce ductility; horizontal shortcuts improve post-peak stability; and hybrids offer a balanced response. With full jacketing, internal steel details have minimal impact on peak capacity, while column height chiefly influences energy dissipation. This work establishes that optimized partial UHPFRC layouts—specifically strips, shortcuts, and their combinations—can achieve tailored performance improvements, introducing a novel, practical, and material-efficient design strategy for strengthening square columns where full wrapping is not feasible. Full article

Time difference of arrival (TDOA) localization enables high-accuracy positioning by analyzing arrival-time differences of target signals at distributed radar nodes, whose performance strongly depends on radar node topology. However, existing studies tend to focus more on improving localization accuracy, while overlooking the impact of radar geometric layout and surveillance coverage on localization performance. To this end, this paper proposes a topology optimization method for a distributed radar system based on an improved non-dominated sorting multi-objective particle swarm optimization (NS-MOPSO) algorithm. A geometric localization model is developed for a distributed TDOA radar system. Based on this model, three optimization objectives are formulated, including minimizing geometric dilution of precision (GDOP), maximizing target coverage, and improving the geometric balance of node placement. These three objective functions are incorporated into the NS-MOPSO framework to achieve a more reasonable radar geometric distribution. To enhance the optimization performance, a series of strategies are adopted, such as non-dominated sorting for Pareto-based solution selection, an improved crowding-distance scheme to encourage balanced multi-objective optimization, and Gaussian mutation to increase solution diversity and reduce the risk of premature convergence. To validate the proposed method, both simulation studies and real-world experiments were conducted under different node deployment scenarios. The results show that the optimized topology achieves a 6.4% reduction in RMSPE and a 4.3% increase in the proportion of high-quality localization regions compared with the best-performing comparative method, while also demonstrating faster convergence and improved stability. These findings confirm the effectiveness and robustness of the proposed approach in enhancing localization accuracy, expanding effective coverage, and improving overall system performance. Full article

Thermal stratification in deep reservoirs can cause ecologically problematic cold-water releases, and many existing selective-withdrawal phenomena rely on a limited set of fixed intake levels, which constrains their ability to follow seasonal shifts in the thermocline. Stepless stratified intakes with continuously adjustable flap gates offer quasi-continuous control of withdrawal depth, but their multi-gate, multi-brace layouts generate complex internal hydraulics whose energy-loss mechanisms are not well captured by conventional head-loss and resistance-coefficient metrics. In this study, physical-model measurements are combined with a validated three-dimensional numerical model, and entropy-production theory is used as a diagnostic to resolve where and by which mechanisms mechanical energy is irreversibly degraded inside a single-unit stepless stratified intake. The analysis shows that turbulent entropy production accounts for more than 98% of total dissipation, concentrated mainly in the flow channel and gate shaft, while the reservoir and outlet pipe contribute only weakly. Local entropy-production-rate fields indicate that dominant irreversibilities are associated with flow turning at the active gate leaves and with separation and wake development around horizontal and vertical braces, which generate low-velocity bands across gate levels and a low-velocity corridor in the shaft. Five geometric modification schemes targeting gate-entrance shaping and brace layout are evaluated; a combined brace-alignment and edge-rounding configuration most effectively weakens dissipation hotspots, improves discharge sharing among gate levels and reduces total entropy production. These findings show that entropy-based diagnostics can complement traditional hydraulic indicators and provide effective guidance for the design and refinement of stepless stratified intake structures. Full article

Harbour sedimentation represents a major challenge to the environmental sustainability and operational efficiency of coastal infrastructure, as frequent dredging activities increase maintenance costs, ecological disturbance, and carbon emissions. Conventional physical and numerical sediment transport models, while widely applied, are computationally intensive and often unsuitable for early-stage, sustainability-oriented design optimisation. To address these limitations, this study proposes a hybrid artificial intelligence-based optimisation framework integrating Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Particle Swarm Optimisation (PSO) for sustainable breakwater and harbour layout design. Hydrodynamic simulations using the Coastal Modelling System (CMS) were conducted to generate a comprehensive dataset describing sediment transport behaviour under varying geometric and structural configurations. An ANN surrogate model was trained to capture nonlinear relationships between breakwater parameters and accumulated sedimentation volume, while GA-based global optimisation and PSO-based validation and local refinement were employed to identify optimal design solutions. Comparative assessment demonstrated consistent convergence of ANN–GA and ANN–PSO solutions within the same design region, with a maximum deviation of 8.46% between design variables and a sedimentation difference of 2.4%. The hybrid ANN–GA–PSO framework achieved the lowest predicted sedimentation volume, representing an improvement of approximately 2.3% relative to the ANN–GA baseline. The proposed framework supports Integrated Coastal Structures Management (ICSM) by enabling proactive, design-stage reduction in long-term sediment accumulation and dredging requirements, offering a scalable pathway toward sustainable and digital-twin-enabled harbour planning. Full article

UV parameterization is a fundamental step in building textured 3D models, but minimizing texture distortion and ensuring seams are placed along meaningful boundaries remains a challenge. This paper proposes UVSegNet, a novel semantic boundary-aware UV parameterization framework that combines part-level segmentation with geometry-aware parameterization. To address the common seam placement issues in parameterization, we introduce a boundary-aware guided UV mapping module that jointly optimizes geometric accuracy and seam layout. Furthermore, to better handle the cylindrical structures common in man-made objects, we introduce a cylindrical supervision strategy to reduce misalignment and unfolding distortion. Experiments on representative object categories show that UVSegNet outperforms other excellent baseline models in both texture quality and seam quality. Compared to Nuvo, UVSegNet improves the angular distortion (conformality) metric by 24.1% and seam compactness by 60.5% by generating a more compact seam layout. Experimental results demonstrate that UVSegNet outperforms baseline methods in both mapping quality and seam quality, thanks to the complementary mechanism of boundary constraints and geometry-driven modeling. Full article

Submarine structures are typically classified into pressure hulls and non-pressure hulls. The pressure hull is a critical component designed to withstand external pressure at operational depths while ensuring internal structural integrity. It is generally composed of ring frames and bulkheads. However, in modern large-scale submarines, bulkheads are often replaced with deep frames to improve equipment layout flexibility. Deep frames serve as essential structural reinforcements, compensating for the loss of stiffness due to the absence of bulkheads. Despite their importance, research on the design of deep frames remains scarce, and in the absence of established design standards, engineers rely on conservative approaches based on practical experience. Therefore, the objective of this study is to propose initial scantling formulas for deep frames in submarine pressure hulls based on finite element analysis (FEA) and parametric studies. To this end, six design cases reflecting actual ship design ranges were selected, and the structural integrity of the pressure hull ring frames was verified through material and geometric nonlinear analysis using ANSYS Mechanical APDL. Subsequently, a total of 82,440 parametric studies were conducted with the reinforced shell thickness, effective length, height and thickness of the deep frame web, and the width and thickness of the deep frame flange as variables. As a result, the proposed formulas satisfied all Validation cases in terms of structural integrity and were found to be applicable within the section length range of 1.5 to 2.0 times the pressure hull diameter. The results of this study are expected to be effectively utilized in the initial design of deep frames for submarine pressure hulls. Full article

Low-frequency oscillatory flow is a long-standing instability in cryogenic jet condensation systems and is closely associated with abnormal pressure fluctuations in propulsion pipelines. While previous studies mainly focused on operating conditions, the role of injector orifice layout in triggering low-frequency oscillations remains unclear. In this work, a three-dimensional numerical investigation was conducted to examine the effect of orifice layout on condensation-induced oscillatory flow in an oxygen jet condensation system. A curvature-coupled mass transfer model is employed, in which the interfacial mass transfer rate is dynamically linked to local vapor–liquid interfacial curvature, enabling accurate representation of interfacial evolution. A series of numerical cases are designed by varying the number, arrangement, and diameter of orifices under different combinations of mass rate, mass flux, and total injection area. Two distinct condensation patterns are identified: suck-back chugging and weak pulsation. Pronounced low-frequency oscillations are observed only for specific orifice layouts. When the total injection area and gaseous oxygen mass rate are maintained, chugging persists under different layouts, producing dominant frequencies of approximately 10~11 Hz and pressure amplitudes of about 80~120 kPa. Once either the total area or mass rate is altered, the system transitions to weak pulsation with pressure fluctuations below 3 kPa. These results demonstrate that low-frequency oscillatory flow is a layout-enabled instability rather than a mass-flux-controlled phenomenon, highlighting the importance of injector geometric design in regulating condensation-induced oscillations. Full article

Developments in electric vehicle (EV) technology are pushing on-board chargers (OBCs) toward higher power density and efficiency, making high-frequency transformer loss prediction a critical design bottleneck. However, the accuracy of commonly used analytical winding-loss models varies strongly with frequency, conductor type (Litz/solid), window fill factor, and winding layout (e.g., interleaved), which can render single-model-based optimization unreliable. In this study, six analytical copper-loss models from the literature were independently reimplemented in a unified Python 3.11.5 workflow with a standardized interface to enable fair comparison under identical geometry and operating conditions. The models were benchmarked against 2D finite-element simulations on test scenarios with increasing physical complexity, including high fill-factor Litz windings and interleaved arrangements. The results confirm a regime-dependent behavior: no single model consistently outperforms others across the full design space, and model dispersion increases in geometrically stressed and higher-frequency regions. To manage this uncertainty, variance maps were generated and model disagreement was quantified using the coefficient of variation (CV). Finally, a reliability-oriented multi-objective optimization framework based on NSGA-II was developed, where a SmartTransformerRouter selects a reference loss estimate per candidate and CV is incorporated via constraints/penalties, with optional FEM triggering in high-uncertainty regions. Full article

This study proposes a design method based on adjoint shape optimization to enhance the heat transfer efficiency of shell-and-tube heat exchangers in oil and gas transportation systems. The primary focus of this work is the design optimization of shell-and-tube heat exchangers through geometric optimization. By simplifying the complex three-dimensional shell-and-tube heat exchanger into a pseudo-three-dimensional reduced-order model, two-dimensional adjoint shape optimization analyses were conducted under unidirectional symmetry about the x-axis and bidirectional symmetry in the x- and y-axes, respectively. The optimized two-dimensional models exhibited a significant increase in the average outlet temperature. Furthermore, the optimized two-dimensional shapes were extruded and reconstructed into three-dimensional models for validation. The results demonstrate that the average air outlet temperature of the three-dimensional models increased by 5.35 K and 3.07 K compared to the original design. Flow field analysis revealed that the heat transfer was improved, since the optimized pipeline layout enhances flow separation and turbulent mixing, leading to a more uniform temperature distribution. This study validates the effectiveness of the adjoint shape optimization method in improving the performance of shell-and-tube heat exchangers. Full article

Injection mold design is a repetitive and time-consuming process with common individual tasks related to each other. This study presents the development of an automatic computer-aided design (CAD) tool for basic injection molds with complete modeling and no other interaction by the user after inserting the part, built on the SolidWorks Application Programming Interface 2022 (API) and Visual Basic for Applications 7.1 2012(VBA). The tool combines user input forms and supplier catalog data as inputs in an algorithm to automatically generate mold structures, cavity blocks, runner system, ejection system and straight drilled cooling channels without further manual modeling. Three case studies with one-, two-, and four-cavity molds demonstrate the approach. The results show that complete mold assemblies can be produced in less than 10 min rather than hours while maintaining standard component dimensions. Although the present version applies to rule-based geometric placement rather than thermal or injection process optimization, it provides a framework for future integration of more complex mold structures and functions such as slides, hot runner system, unscrewing geometries, conformal cooling, heat-transfer-based design, family molds and machine selection. This work demonstrates how API-driven automation can reduce design time, standardize layouts, and lay the groundwork for next-generation injection mold development. Full article

The In-Wheel Motor represents a non-conventional propulsion architecture in which the electric motor is integrated into the wheel, offering advantages such as improved energy efficiency, individual torque control, and drivetrain simplification. In this study, two architectures, inboard and outboard, were developed using an original three-dimensional motor–brake–suspension–steering assembly model, in which disk brake position and In-Wheel Motor integration act as primary design drivers influencing vehicle dynamics. Both architectures were developed in CATIA V5 and exported to Altair Motion 2025 for multibody dynamics simulations. The study evaluates the impact of inboard versus outboard disk brake positioning on vehicle dynamics and provides a qualitative assessment of the associated architectures in terms of mechanical complexity, serviceability, sealing requirements, bearing load asymmetry, and packaging constraints. The results indicate that the inboard architecture exhibits more linear and stable kinematics and compliance (K&C) behavior compared to the outboard configuration, at the expense of increased mechanical complexity and reduced serviceability. By contrast, the outboard architecture preserves a simpler, more conventional MacPherson-like layout with a lower component count and improved service access but is dynamically outperformed under the imposed geometric constraints of the present study. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article