Search Results (194)

In this research, the nonlinear bending behavior of sandwich beams with auxetic re-entrant cores and carbon nanotube-reinforced (CNT) face sheets are investigated using the von Kármán strain theory and the Generalized Differential Quadrature (GDQ) method. The results demonstrate the high accuracy of the GDQ method in solving nonlinear problems with a minimal number of grid points. Validation is performed by comparing the obtained results with those reported in previous studies. The findings indicate that CNT-reinforced composite beams exhibit superior bending performance compared to sandwich beams with re-entrant cores and conventional composite face sheets. Furthermore, a parametric study on the core geometry reveals that optimal bending performance is achieved when the interior angle (θ) of the core is approximately 0 to 2 degrees, and the nondimensional auxetic parameter η_1 is minimized. This study highlights the significance of nonlinear analysis, particularly for long and slender beams. Full article

Unstructured mesh deformation is an effective way to automatically generate mesh after geometric shape changes such as fluid–structure interaction simulation or aerodynamic shape optimization. The radial basis function method is one of the best mesh deformation methods, which takes into account both computational time and deformation ability. However, the current existing methods are confronted by the contradiction between computational efficiency and deformation accuracy. In this paper, a hybrid deformation method combining the radial basis function and distance-weighted function is proposed, which can effectively reduce computing cost and eliminate deformation error. Firstly, based on the radial basis function method with data reduction scheme, an efficient equidistant sampling method for points selection independent of the specific form of deformation is proposed, and a sampling algorithm based on bisection is devised to make the number of sample points quickly approach the expected value. Secondly, a compact distance-weighted function deformation method is developed, which is used to diffuse the deformation errors of boundary mesh points directly to interior mesh points in order to completely eliminate the deformation errors. Finally, two configurations, AGARD 445.6 wing and HIRENASD wing, are used to test the deformation capability of the hybrid method and the computing time of several key processes. The results show that the hybrid method can accurately realize large mesh deformation with a maximum displacement up to 50% span length, and at the same time, the mesh deformation can be completed with a single core in about 100 s for millions of mesh points, which indicates that the hybrid method in this paper has the ability to be applied to complicated configurations in real engineering. Full article

The AC Optimal Power Flow (AC-OPF) problem remains a major computational bottleneck for real-time power system operation. Conventional solvers are accurate but time-consuming, while Graph Neural Networks (GNNs) offer faster approximations yet struggle to capture long-range dependencies and handle topological variations. To address these limitations, we propose a Heterogeneous Graph Transformer with bus-centric Local–Global Message Passing (LG-HGNN). The model performs type-specific local message passing over heterogeneous power graphs and applies a global Transformer only on bus nodes to capture system-wide correlations efficiently. Effective-resistance positional encodings and resistance-biased attention enhance electrical awareness, whereas bounded decoders and physics-informed regularization preserve operational feasibility. Experiments on IEEE 14-, 30-, and 118-bus systems show that LG-HGNN achieves near-optimal results within a few percent of the AC-OPF optimum and generalizes to thousands of unseen N-1 contingency topologies without retraining. Compared with interior-point solvers, it attains up to $190\times$ speedup before power-flow correction and over $10\times$ afterward on GOC 2000-bus systems, providing a scalable and physically consistent surrogate for real-time AC-OPF. Full article

In response to the growing demand for efficient and eco-friendly golf carts, this paper presents an optimized design of a permanent magnet synchronous machine (PMSM) for multiple operating conditions. The application scenarios of the golf cart were first analyzed, identifying the power requirements under three driving conditions such as unloaded on flat roads, fully loaded on flat roads, and fully loaded on slopes. Then, a 36-slot 8-pole interior PMSM is developed, and a systematic two-stage optimization strategy using a Multi-Objective Genetic Algorithm (MOGA) is applied to enhance both no-load and rated-load performance. By adjusting key rotor parameters to balance competing objectives, the optimized machine demonstrates notable improvements in cogging torque reduction, output torque, torque ripple minimization, and operational efficiency. Specifically, the results show that the optimized machine achieves a cogging torque reduction of over 60%, an increase in maximum output torque by 7.3%, and a peak efficiency improvement of 1.2 percentage points under high-load conditions. Experimental results validate the effectiveness of the design and confirm its suitability for the complex operating conditions of golf carts. Full article

This study situates washed sheep-wool fibres as a sustainable reinforcement candidate for epoxy matrices and evaluates their mechanical response under tensile, flexural, compressive, and Charpy impact loading. The objective of this work is to assess whether short, washed sheep-wool fibres can function as a sustainable reinforcement for epoxy matrices, and to identify optimal fibre length–content windows that improve mechanical behaviour for engineering applications. Moulded–machined specimens were produced with fibre lengths of 3, 6, and 10 mm and contents of 1.0–5.0 wt.%, depending on the test; neat epoxy served as the reference. In tension, selected formulations—particularly 10 mm/1.5 wt.%—showed simultaneous increases in ultimate stress and modulus relative to the neat resin, corresponding to gains of about 10% in ultimate tensile stress and 50% in tensile modulus, at the expense of ductility. In flexure, the modulus decreases by roughly 15–35% compared with the matrix, whereas configurations with 3–6 mm at 2.5–5 wt.% raise the fracture stress by about 35–45% and improve post-peak resistance. In compression, reinforcement markedly elevates yield stress, with increases of up to about 160% at 3 mm/2 wt.%, while the ultimate strain decreases moderately. In Charpy impact, all reinforced materials underperform the resin, with absorbed energy reduced by roughly 75–93% depending on fibre length and content, with 3 mm/1 wt.% being the least affected. A two-factor analysis of variance (ANOVA) indicates that fibre length primarily governs tensile and compressive behaviour, while fibre content dominates flexural and impact responses. Overall, the findings support wool fibres as a viable reinforcement when length and content are optimized, pointing to their use in non-structural to semi-structural industrial components such as interior panels, housings, casings, protective covers, and other parts where moderate tensile/compressive performance is sufficient and material sustainability is prioritised. Full article

Vehicle–road–cloud collaborative perception improves perception performance via multi-agent information sharing and data fusion, but it faces coupled trade-offs among perception accuracy, computing resources, and communication bandwidth. Optimizing agents’ intelligence or underlaying resources alone fails to resolve this conflict, limiting collaboration efficiency. We propose C⁴I-JO, a joint resource and intelligence optimization method for vehicle–road–cloud collaborative perception. We employ slimmable networks to achieve intelligent elasticity. Based on these, C⁴I-JO jointly optimizes four key dimensions to minimize resource consumption while meeting accuracy and latency constraints, including collaborative mechanisms to cut redundant communication, resource allocation to avoid supply–demand bottlenecks, intelligent elasticity to balance accuracy and resources, and computation offloading to reduce local burden. We propose a two-layer iterative decoupling algorithm that addresses the optimization problem. Specifically, the outer level leverages Second-Order Cone Programming (SOCP) and the interior-point method, while the inner level utilizes a Genetic Algorithm (GA). Simulations show that C⁴I-JO outperforms baselines in both resource efficiency and perception quality. Full article

This paper presents a multiphysics, Life Cycle Assessment (LCA)-based design optimization framework for an interior permanent-magnet traction motor tailored to electric-vehicle duty. The workflow couples driving cycle realism, electromagnetic–thermal analysis, and life cycle assessment within a unified, computationally efficient process. Representative operating points are extracted from WMTC and ECE cycles using clustering, after which a multi-level Taguchi refinement searches the design space from coarse to fine. A weighted composite objective balances machine cost and life cycle cumulative emissions under hard constraints on torque capability and hotspot temperature. The optimized design satisfies performance and thermal limits while simultaneously reducing both cost and life cycle burden, as confirmed through phase-wise assessment of raw material, use-phase, and end-of-life contributions. Iterative improvements are accompanied by rising signal-to-noise ratios and reduced parameter-level spread, indicating greater robustness to operating variability. Overall, the study demonstrates that an LCA-driven, multiphysics-constrained optimization can deliver sustainable, high-performance IPM designs that are aligned with realistic vehicle operating conditions and readily adaptable to alternative motor and drive architectures. Full article

Glass furnaces are a key component of the energy-intensive glass industry. Therefore, optimization of their energy performance is crucial for both economic and environmental sustainability. This study focused on optimizing the performance of an electric-boosted natural gas glass furnace. For this purpose, firstly, raw operational data were collected from a glass furnace. Next, reconciled data were obtained via a modelling process, data reconciliation, and gross error detection to establish a reliable dataset. Two linear regression models were developed and tested using both raw and reconciled data and compared with each other. The constrained optimization problem was constructed using a linear regression model and other process constraints and solved via the interior-point method to minimize specific energy consumption. The findings indicate that the reconciled data-based linear regression model yielded more reliable results. The specific energy consumption can be reduced to a minimum of 3660.088 kJ/kg-glass under an optimal setpoint for raw material, cullet, water, raw material temperature, electric boosting, and fuel. Furthermore, the analysis reveals that energy performance is enhanced with increased glass production and greater utilization of electric boosting. These results emphasize that the integrated statistical modelling approach provides valuable and actionable insights for energy performance improvements in the glass industry. Full article

This paper investigates the role of reactive power support from Distributed Generation (DG) units in improving voltage compliance and maximizing active power injection in medium-voltage distribution networks. Using the IEEE 34-Node Test Feeder as a case study, a simplified single-phase equivalent model was developed, excluding voltage regulators, shunt capacitors, and step-down transformers to focus on the intrinsic voltage behavior of the feeder. An AC Optimal Power Flow (OPF) model was formulated in Pyomo and solved with Interior Point Optimizer (IPOPT) to evaluate two operational scenarios: (i) DG injecting a fixed 1 MW of active power without reactive power support, and (ii) DG injecting the same active power with optimized reactive power dispatch within $\pm 0.5$ MVAr, subject to apparent power constraints. Simulation results show that allowing reactive power flexibility increases the number of feasible DG connection points, improves minimum bus voltages, and reduces the occurrence of voltage limit violations. The findings suggest that modest reactive power capabilities can significantly enhance the hosting capacity of radial distribution feeders without requiring costly network reinforcements. Full article

We present a head-to-head evaluation of the Improved Inexact–Newton–Smart (INS) algorithm against a primal–dual interior-point framework for large-scale nonlinear optimization. On extensive synthetic benchmarks, the interior-point method converges with roughly one-third fewer iterations and about one-half the computation time relative to INS, while attaining marginally higher accuracy and meeting all primary stopping conditions. By contrast, INS succeeds in fewer cases under default settings but benefits markedly from moderate regularization and step-length control; in tuned regimes, its iteration count and runtime decrease substantially, narrowing yet not closing the gap. A sensitivity study indicates that interior-point performance remains stable across parameter changes, whereas INS is more affected by step length and regularization choice. Collectively, the evidence positions the interior-point method as a reliable baseline and INS as a configurable alternative when problem structure favors adaptive regularization. Full article

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages. Full article

This paper addresses the simultaneous feeder routing and conductor sizing problem in unbalanced three-phase distribution systems, formulated as a nonconvex mixed-integer nonlinear program (MINLP) that minimizes the equivalent annualized expansion cost—combining investment and loss costs—under voltage, ampacity, and radiality constraints. The model captures nonconvex voltage–current–power couplings, $\Delta /Y$ load asymmetries, and discrete conductor selections, creating a large combinatorial design space that challenges heuristic methods. An exact MINLP formulation in complex variables is implemented in Julia/JuMP and solved with the Basic Open-source Nonlinear Mixed Integer programming (BONMIN) solver, which integrates branch-and-bound for discrete variables and interior-point methods for nonlinear subproblems. The main contributions are: (i) a rigorous, reproducible formulation that jointly optimizes routing and conductor sizing; (ii) a transparent, replicable implementation; and (iii) a benchmark against minimum spanning tree (MST)-based and metaheuristic approaches, clarifying the trade-off between computational time and global optimality. Tests on 10- and 30-node rural feeders show that, although metaheuristics converge faster, they often yield suboptimal solutions. The proposed MINLP achieves globally optimal, technically feasible results, reducing annualized cost by 14.6% versus MST and 2.1% versus metaheuristics in the 10-node system, and by 17.2% and 2.5%, respectively, in the 30-node system. These results highlight the advantages of exact optimization for rural network planning, providing reproducible and verifiable decisions in investment-intensive scenarios. Full article

Accurate volumetric modeling of indoor spaces is essential for emerging 3D cadastral systems, yet existing workflows often rely on manual intervention or produce surface-only models, limiting precision and scalability. This study proposes and validates an integrated, largely automated workflow (named VERTICAL) that converts classified indoor point clouds into topologically consistent 3D solids served as materials for land surveyor’s cadastral analysis. The approach sequentially combines RANSAC-based plane detection, polygonal mesh reconstruction, mesh optimization stage that merges coplanar faces, repairs non-manifold edges, and regularizes boundaries and planar faces prior to CAD-based solid generation, ensuring closed and geometrically valid solids. These modules are linked through a modular prototype (called P2M) with a web-based interface and parameterized batch processing. The workflow was tested on two condominium datasets representing a range of spatial complexities, from simple orthogonal rooms to irregular interiors with multiple ceiling levels, sloped roofs, and internal columns. Qualitative evaluation ensured visual plausibility, while quantitative assessment against survey-grade reference models measured geometric fidelity. Across eight representative rooms, models meeting qualitative criteria achieved accuracies exceeding 97% for key metrics including surface area, volume, and ceiling geometry, with a height RMSE around 0.01 m. Compared with existing automated modeling solutions, the proposed workflow has the ability of dealing with complex geometries and has comparable accuracy results. These results demonstrate the workflow’s capability to produce topologically consistent solids with high geometric accuracy, supporting both boundary delineation and volume calculation. The modular, interoperable design enables integration with CAD environments, offering a practical pathway toward an automated and reliable core of 3D modeling for cadastre applications. Full article

Reservoir operation serves as a critical non-structural measure for real-time flood management, aimed to minimize downstream flood damage while ensuring dam safety. This study develops and evaluates a Gated Recurrent Unit (GRU)-based reservoir operation model with data integration (DI) to enhance flood management capabilities. Optimal reservoir outflows are first determined for historical flood events using the Interior Point Optimizer (IPOPT), a deterministic optimization model designed to minimize peak outflows. The optimized hydrographs are compared with observed outflows to assess the benefits of improved operational strategies. GRU models are then trained and validated using inflow hydrographs and resulting optimal reservoir storage and release data. Various input configurations are tested, incorporating DI of lagged observations and forecasted values to evaluate their influence on model accuracy. The study also examines multiple hyperparameter settings to identify the optimal configuration. The methodology is applied to the Namgang Dam in South Korea, simulating hourly operations during flood events. Results indicate that historical reservoir inflow and storage are the most influential inputs, while adding precipitation (historical or forecasted) and/or forecasted inflows does not improve model performance. The GRU model with DI successfully replicates optimized reservoir operations, demonstrating its reliability and efficiency in flood management. This framework supports timely and informed decision-making and offers a promising approach for enhancing flood risk mitigation through improved reservoir operations. Full article

This paper proposes an innovative hybrid optimization framework for the optimal installation and operation of thyristor-switched capacitors (TSCs) within medium-voltage distribution networks, targeting both energy losses reduction and cost efficiency. The core of the approach combines the exploratory capabilities of the atan-sinc optimization algorithm (ASOA), a recent metaheuristic inspired by mathematical functions, with the local refinement power of the IPOPT solver within a master–slave architecture. This integrated method addresses the inherent complexity of a multi-objective, mixed-integer nonlinear programming problem that seeks to balance conflicting goals: minimizing annual system losses and investment costs. Extensive testing on IEEE 33- and 69-bus systems under fixed and dynamic reactive power injection scenarios demonstrates that our framework consistently delivers superior solutions when compared to traditional and state-of-the-art algorithms. Notably, the variable operation case yields energy savings of up to 12%, translating into annual monetary gains exceeding USD 1000 in comparison with the fixed support scenario.The solutions produce well-distributed Pareto fronts that illustrate valuable trade-offs, allowing system planners to make informed decisions. The findings confirm that the proposed strategy constitutes a scalable, and robust tool for reactive power planning, supporting the deployment of smarter and more resilient distribution systems. Full article