Search Results (191)

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

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

In recent years, the integration of flexible resources into active distribution networks (ADNs) has been significantly enhanced. By coordinating a variety of such resources, the economic efficiency, operational security, and overall stability of ADNs can be improved. In this study, a bi-level planning model is proposed for active distribution networks. The upper-level model aims to minimize the annual comprehensive cost, while the lower-level model focuses on reducing network losses. To solve the upper-level problem, a hybrid whale optimization algorithm (HWOA) is developed. The algorithm integrates adaptive mutation based on Gaussian–Cauchy distributions, a nonlinear cosine-based control strategy, and a dual-population co-evolution mechanism. These enhancements allow HWOA to achieve faster convergence, higher accuracy, and stronger global search capabilities, thereby reducing the risk of falling into local optima. The lower-level problem is addressed using the interior point method due to its nonlinear and continuous nature. The proposed model and algorithm are validated through simulations on the IEEE 33-bus system. The results show that DG consumption increases by 88.77 MWh, network losses decrease by 6.8 MWh, and the total system cost is reduced by CNY 3.62 million over the entire project lifecycle. These improvements contribute to both the economic and operational performance of the ADN. Compared with the polar fox optimization algorithm (PFA), HWOA improves algorithmic efficiency by 18.92%, lowers network loss costs by 6.22%, and reduces the total system costs by 0.71%, demonstrating its superior effectiveness in solving complex bi-level optimization problems in active distribution networks. These findings not only demonstrate the technical efficiency of the proposed method but also contribute to the long-term goals of sustainable energy systems by improving renewable energy utilization, reducing operational losses, and supporting carbon reduction targets in active distribution networks. Full article

This paper investigates a simultaneous wireless information and power transfer (SWIPT) decode-and-forward (DF) relay network, where a source node transmits data to a destination node through the assistance of multi-hop passive relays. We employ the time-switching (TS) protocol, enabling the relays to harvest energy from the received previous hop signal to support data forwarding. We first prove that the system throughput monotonically increases with the transmit power of the source node. Next, by employing logarithmic transformations, we convert the non-convex problem of obtaining optimal TS ratios at each relay to maximize the system throughput into a convex optimization problem. Comprehensively taking into account the convergence rate, computational complexity per iteration, and robustness, we selected the log barrier method—a type of interior point method—to address this convex optimization problem, along with providing a detailed implementation procedure. The simulation results validate the optimality of the proposed method and demonstrate its applicability to practical communication systems. For instance, the proposed scheme achieves 1437.3 bps throughput at 40 dBm maximum source power in a 2-relay network—278.6% higher than that of the scheme with TS ratio fixed at 0.75 (379.68 bps). On the other hand, it converges within a 1.36 ms computation time for 5 relays, 6 orders of magnitude faster than exhaustive search (1730 s). Full article