Search Results (1,090)

A zero-dimensional, non-isothermal analytical framework was developed to assess solid
oxide fuel cell (SOFC) performance across a broad range of operating conditions. The model
integrates the anode, electrolyte, interlayers, and cathode, while resolving the distinct
physicochemical processes within each layer. Electrochemical impedance spectroscopy
(EIS), followed by distribution of relaxation times (DRT) analysis, was implemented to
probe relevant cell polarization resistances under open-circuit and load conditions. The
modeling framework couples mass and charge transport, electrochemical reactions, and
non-isothermal heat transfer, with multilayer discretization applied to capture localized
material properties and operating conditions. It enables the estimation of electrolyte ionic
conductivity and total ohmic resistance by accounting for microstructural and geometric
parameters, while also quantifying activation energies, exchange current densities, and
gas-diffusion-related polarization resistances. Simulations were conducted for an SOFC
operating on pure hydrogen with varying oxygen concentrations at 700 °C, 660 °C, 620 °C,
and 580 °C. The results were validated against experimental data. The analysis revealed that
ohmic overpotential dominates total cell losses, even at high current densities, underscoring
the importance of minimizing ionic resistance to improve overall SOFC performance. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

The extraction of land gas resources requires efficient methods to address the issue of pipeline obstruction due to the accumulation of natural gas hydrates. The existing ground heating, downhole throttling, and decompression measures are energy-intensive. The metal-reinforced composite heat-insulation pipe serves as the production string for terrestrial natural gas wells, effectively minimizing temperature loss of natural gas within the wellbore. This innovation eliminates the need for ground heating equipment and downhole throttling devices in large-scale gas well production, thereby fundamentally achieving environmentally sustainable natural gas extraction, energy conservation, and cost reduction. This research simulates the operational circumstances and environmental characteristics of the Sulige gas field. Utilizing predictions and analyses of the formation characteristics of natural gas hydrate, the gas–solid two-phase flow DPM model, RNG k-ε turbulence model, heat transfer characteristics, and population balance model are employed to examine the concentration distribution, pressure distribution, velocity distribution, and heat transfer characteristics of natural gas hydrate within the vertical tube of the structure. The findings indicate that a reduction in natural gas production or an increase in hydrate volume fraction leads to significant accumulation of hydrate adjacent to the tube wall, while the concentration distribution of hydrate is more uniform at elevated production conditions. The pressure distribution of hydrate under each operational state exhibits a pattern characterized by a high central concentration that progressively diminishes towards the periphery. The unit pressure drop of hydrate markedly escalates with an increase in flow rate. As the ambient temperature of the formation rises or the flow rate escalates, the thermal loss of the hydrate along the pipeline diminishes, resulting in an elevated exit temperature. Minimizing the thermal conductivity of the composite pipe can significantly decrease the temperature loss of the hydrate along the pipeline, greatly aiding in hydrate inhibition during the extraction of natural gas from terrestrial wells. This paper’s research offers theoretical backing for the enduring technical application of metal-reinforced composite insulating pipes in terrestrial gas fields, including the Sulige gas field. Full article

In Internet of Things (IoT) systems, data collected by geographically distributed sensors is often incomplete due to device failures, harsh deployment conditions, energy constraints, and unreliable communication. Such data gaps can significantly degrade downstream data processing and decision-making, particularly when failures result in the loss of all locally redundant sensors. Conventional imputation approaches typically rely on historical trends or multi-sensor fusion within the same target environment; however, historical methods struggle to capture emerging patterns, while same-location fusion remains vulnerable to single-point failures when local redundancy is unavailable. This article proposes a correlation-aware, cross-location data fusion framework for data imputation in IoT networks that explicitly addresses single-point failure scenarios. Instead of relying on co-located sensors, the framework selectively fuses semantically similar features from independent and geographically distributed gateways using summary statistics-based and correlation screening to minimize communication overhead. The resulting fused dataset is then processed using a lightweight KNN with an Iterative PCA imputation method, which combines local neighborhood similarity with global covariance structure to generate synthetic data for missing values. The proposed framework is evaluated using real-world weather station data collected from eight geographically diverse locations across the United States. The experimental results show that the proposed approach achieves improved or comparable imputation accuracy relative to conventional same-location fusion methods when sufficient cross-location feature correlation exists and degrades gracefully when correlation is weak. By enabling data recovery without requiring redundant local sensors, the proposed approach provides a resource-efficient and failure-resilient solution for handling missing data in IoT systems. Full article

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation. Full article

Efficient cooling systems are crucial for achieving high efficiency and power density in electric vehicle motors. To enhance motor cooling performance, a novel oil spray cooling system was developed, referred to as the centripetal-inclined oil spray (CIOS) cooling system. The CIOS cooling system features axial oil channels evenly distributed on the surface of the stator core, with each channel connected at both ends to stepped oil channels. This configuration allows for direct oil spraying towards the center at specific inclined angles without the need for additional components such as nozzles, oil spray rings, and oil spray tubes, which reduces costs, minimizes the risk of oil leakage, and enhances motor reliability. Electromagnetic and computational fluid dynamic simulations were conducted on the motor with the CIOS cooling system. The results indicated that the CIOS cooling system adversely impacted core losses and torque, while these effects were minimized after optimization, with losses increasing by up to 0.29% and torque decreasing by up to 0.45%. The CIOS cooling system achieved stable oil spraying, forming oil films on the end-winding with a maximum formation rate of 49.4% and an average thickness of 1.56 mm. Compared to the motor with oil spray rings, the motor with the CIOS cooling system exhibited lower temperatures across all components and more uniform cooling. Finally, the cooling performance of the CIOS cooling system was verified through experiments, and the results showed that the measured temperature closely matched the simulated results, with a maximum error of 5.9%. The findings in this study are expected to provide new insights for optimizing oil cooling systems in electric vehicle motors. Full article

The high penetration of renewable energy brings significant power fluctuations and operational uncertainties to distribution networks. Traditional power allocation methods for hybrid energy storage systems (HESSs) exhibit strong parameter dependency, limited frequency-domain recognition accuracy, and poor dynamic coordination capability. To overcome these limitations, this study proposes an adaptive power allocation strategy for HESSs under renewable energy integration scenarios. The proposed method employs the Grey Wolf Optimizer (GWO) to jointly optimize the mode number and penalty factor of the Variational Mode Decomposition (VMD), thereby enhancing the accuracy and stability of power signal decomposition. In conjunction with the Hilbert transform, the instantaneous frequency of each mode is extracted to achieve a natural allocation of low-frequency components to the battery and high-frequency components to the supercapacitor. Furthermore, a multi-objective power flow optimization model is formulated, using the power commands of the two storage units as optimization variables and aiming to minimize voltage deviation and network loss cost. The model is solved through the Particle Swarm Optimization (PSO) algorithm to realize coordinated optimization between storage control and system operation. Case studies on the IEEE 33-bus distribution system under both steady-state and dynamic conditions verify that the proposed strategy significantly improves power decomposition accuracy, enhances coordination between storage units, reduces voltage deviation and network loss cost, and provides excellent adaptability and robustness. Full article

This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

Rice–crab co-culture systems (RC) represent promising sustainable intensification approaches, yet their nitrogen (N) cycling and optimal fertilization strategies remain poorly characterized. In this study, we compared RC with rice monoculture system (RM) across four N gradients (0, 150, 210, and 270 kg N·hm⁻²), assessing N dynamics in field water and N distribution in soil. The results showed that field water ammonium nitrogen (NH₄⁺-N) concentrations increased nonlinearly, showing sharp increases beyond 210 kg N·hm⁻². Notably, crab activity in the RC altered the N transformation and transport processes, leading to a prolonged presence of nitrate nitrogen (NO₃⁻-N) in field water for two additional days after tillering fertilization compared to RM. This indicates a critical window for potential nitrogen loss risk, rather than enhanced retention, 15 days after basal fertilizer application. Compared to RM, RC exhibited enhanced nitrogen retention capacity, with NO₃⁻-N concentrations remaining elevated for an additional two days following tillering fertilization, suggesting a potential critical period for nitrogen loss risk. Post-harvest soil analysis revealed contrasting nitrogen distribution patterns: RC showed enhanced NH₄⁺-N accumulation in surface layers (0–2 cm) with minimal vertical NO₃⁻-N redistribution, while RM exhibited progressive NO₃⁻-N increases in subsurface layers (2–10 cm) with increasing fertilizer rates. The 210 kg N·hm⁻² rate proved optimal for the RC, producing a rice yield 12.08% higher than that of RM and sustaining high crab yields, while avoiding the excessive aqueous N levels seen at higher rates. It is important to note that these findings are based on a single-site, single-growing season field experiment conducted in Panjin, Liaoning Province, and thus the general applicability of the optimal nitrogen rate may require further validation across diverse environments. We conclude that a fertilization rate of 210 kg N·hm⁻² is the optimal strategy for RC, effectively balancing productivity and environmental sustainability. This finding provides a clear, quantitative guideline for precise N management in integrated aquaculture systems. Full article

Background: Nuclear cataracts cause visual degradation through light scattering by aggregated proteins and particles within the crystalline lens. Existing computational models mainly consider submicron scatterers, while the optical impact of micrometer-scale particles observed in human nuclear cataracts remains underexplored. Objective: This study extends a LOCS III–based computational cataract model by incorporating micrometer-scale particles and quantitatively evaluates their effects on forward and backward light scattering across nuclear cataract grades. Methods: A physics-based scattering model was implemented using optical simulation software (LightTools). Three particle populations—nanometer-scale (S-type), submicron-scale (M-type), and micrometer-scale (L-type)—were uniformly distributed within the lens. Retinal luminance reduction was analyzed for forward scattering, while slit-lamp-based backward scattering simulations were used to evaluate luminance distributions and chromaticity changes. Particle concentrations were varied within clinically reported ranges corresponding to LOCS III grades. Results: Micrometer-scale particles had minimal impact in early nuclear cataract grades but significantly increased forward scattering and luminance loss in advanced grades (NO5–NO6). Backward scattering simulations revealed pronounced luminance enhancement and yellow chromaticity shifts with increasing micrometer-scale particle concentration. One micrometer-scale particle produced a luminance-reduction effect equivalent to approximately 6–7 submicron particles, depending on cataract severity. Conclusions: Including micrometer-scale particles enables a more complete optical representation of nuclear cataracts, linking retinal image degradation with slit-lamp appearance. The model provides a physically grounded framework for offline analysis and reference data generation to support clinical interpretation of cataract grading. Full article

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article

Auxiliary power demand in battery electric vehicles continues to increase as manufacturers transition toward multi-low-voltage architectures that combine 48 V and 12 V buses to improve load distribution flexibility and overall system efficiency. This paper evaluates several auxiliary power module (APM) architectures in terms of scalability, efficiency, complexity, size, and cost for supplying two low-voltage buses (e.g., 48 V and 12 V) from the high-voltage battery. Based on this assessment, a cascaded APM configuration is adopted, consisting of an isolated dual active bridge (DAB) converter followed by a non-isolated synchronous buck converter. A multi-objective optimization framework based on the NSGA-II algorithm is developed for the DAB stage to maximize efficiency and power density while minimizing cost. The optimized 13 kW DAB stage achieves a peak efficiency of 95% and a power density of 4.1 kW/L. For the 48 V/12 V buck stage, a 2 kW commercial GaN-based converter with a mass of 0.5 kg is used as the reference design, achieving a peak efficiency of 96.5%. Dedicated PI controllers are designed for both the DAB and buck stages using their respective small-signal models to ensure tight regulation of the two LV buses. The overall system stability is verified through impedance-based analysis. Experimental validation using a DAB prototype integrated with a multi-phase buck converter confirms the accuracy of the DAB loss modeling used in the design optimization framework as well as the control design implemented for the cascaded converters. Full article

High-arch dams are usually built in high-ground stress distribution areas. The deformation and stability of the abutment slope are directly related to the safety of the construction and operation of these dams. At present, there are few studies on deformation monitoring and analysis of ultra-high-arch dam abutment slopes. In this study, the surface displacement, anchor stress, and anchor cable’s anchoring force of the dam abutment slope of Wudongde Hydropower Station before and after impounding were monitored, and the safety and deformation mechanism of the dam abutment slope were analyzed, focusing on its change amplitude and change trends. Our results indicate that surface displacement and rock mass deformation at the abutment slopes on both banks are minimal, with stability being maintained following excavation and support works and no abnormal deformation occurring during impoundment. Most anchor bolt stresses remained below 50 MPa, with stable readings exceeding 200 MPa at monitored points. The loss rates of the anchor cable’s anchorage force generally fell within ±15%, with variations primarily occurring prior to excavation and support works. Minimal changes were observed before and after impoundment, indicating overall slope stability. The deformation and stress of the dam abutment slope did not exhibit abnormal changes before or after impounding, and the entire slope is in a stable state. These research results provide a reference for the safe operation of Wudongde Hydropower Station. Full article

Designing thermal–fluid devices that reduce peak temperature while limiting pressure loss is challenging because high-fidelity (HF) Navier–Stokes–convection simulations make direct HF-driven topology optimization computationally expensive. This study presents a two-dimensional, steady, laminar multifidelity topology design framework for thermal–fluid devices operating in a low-to-moderate Reynolds number regime. A computationally efficient low-fidelity (LF) Darcy–convection model is used for topology optimization, where SEMDOT decouples geometric smoothness from the analysis field to produce CAD-ready boundaries. The LF optimization minimizes a P-norm aggregated temperature subject to a prescribed volume fraction constraint; the inlet–outlet pressure difference and the P-norm parameter are varied to generate a diverse candidate set. All candidates are then evaluated using a steady incompressible HF Navier–Stokes–convection model in COMSOL 6.3 under a consistent operating condition (fixed flow; pressure drop reported as an output). In representative single- and multi-channel case studies, SEMDOT designs reduce the HF peak temperature (e.g., ~337 K to ~323 K) while also reducing the pressure drop (e.g., ~18.7 Pa to ~12.6 Pa) relative to conventional straight-channel layouts under the same operating point. Compared with a conventional RAMP-based pipeline under the tested settings, the proposed approach yields a more favorable Pareto distribution (normalized hypervolume 1.000 vs. 0.923). Full article