Search Results (3,274)

Heat pipe cooled reactors rely on heat pipes for passive heat transfer and exhibit high reliability and compactness. Therefore, they are considered candidate nuclear reactor systems for future deep space exploration missions. To enable a deeper investigation of heat pipe reactor systems, particularly the transient response characteristics of the core, a transient coupled analysis framework is developed based on the multi-physics coupling code MOOSE. This framework includes the core heat transfer module, point kinetics module, heat pipe module, and Stirling engine module. A novel strategy that allows two distinct heat pipe models to be simultaneously invoked within a single simulation in MOOSE is developed. All modules are developed within the MOOSE framework and do not rely on any external programs. The heat pipe module is validated using experimental data from heat pipe startup and operation tests within the maximum relative error of only 0.45%. The entire coupled framework is validated against the KRUSTY operational experiments and is compared with other multi-physics models, demonstrating higher accuracy within the maximum relative error of only 13.7% in core load variation conditions. Meanwhile, transient coupled analyses of the KRUSTY reactor are performed to evaluate its safety performance under accident conditions. In the hypothetical positive reactivity step insertion accident and heat pipe failure accidents, the KRUSTY core exhibits excellent safety performance. And the mechanism of heat pipe power redistribution following heat pipe failure is examined in detail. Full article

Data centers are critical digital infrastructure supporting cloud computing, artificial intelligence, and global information services. Despite their high-reliability design, they remain vulnerable to fire incidents due to continuous operation, high electrical loads, dense power systems, and the increasing use of lithium-ion batteries. Although such events are rare, their consequences can be severe, including service disruption, equipment damage, financial loss, and risks to data integrity. This study presents a systematic literature review of fire safety risk management frameworks in data centers, following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2025 were retrieved from Scopus and Web of Science, screened, and appraised using structured quality criteria. Twelve empirical studies were synthesized and benchmarked against NFPA 75 and NFPA 76 standards. The findings are organized into three domains: Strategic Management, Fire Risk, and Fire Preparedness. The results show a strong focus on technical prevention and electrical hazards, while organizational readiness, emergency response, and recovery remain underexplored. Benchmarking indicates that industry standards adopt a more comprehensive lifecycle approach than the academic literature. This study reframes data center fire safety as a socio-technical reliability system and highlights critical gaps, providing a foundation for future research and improved fire safety governance and resilience. Full article

This study investigates the acceptance of an AI-powered decision-support chatbot among professionals in a marketing and sales context, addressing a gap in technology acceptance research by examining data-intensive decision environments that remain underexplored. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes an extended model incorporating Behavioral Intention, Performance Expectancy, Effort Expectancy, Social Influence, Output Quality, Time Saving, Source Trustworthiness, Cognitive Load, and Chatbot Self-Efficacy. An experimental study was conducted with 106 professionals using a chatbot-enhanced business analytics platform to complete marketing KPI analysis tasks. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that Behavioral Intention to use decision-support chatbots is significantly influenced by Performance Expectancy, Effort Expectancy, and Social Influence. Performance Expectancy is strongly driven by Output Quality, Time Saving, and Source Trustworthiness, while Effort Expectancy is significantly shaped by reduced Cognitive Load and higher Chatbot Self-Efficacy. The findings suggest that chatbot acceptance in professional decision-making depends not only on usability and performance beliefs but also on cognitive relief, trust in information sources, and efficiency gains, highlighting important implications for both theory and the design of AI-based decision-support systems. Full article

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article

This study presents a heterogeneous integration micro-assembly process and circuit board packaging solution for GaN Schottky Barrier Diode (SBD) rectifiers, and innovatively constructs a hierarchical reinforcement learning strategy for optimizing SBD RF parameters. By establishing an optimization framework with the goal of efficiency in the load-input power two-dimensional space, a dual-layer optimization mechanism is employed: the high-level strategy dynamically selects optimization regions and parameter combinations, while the low-level strategy implements specific parameter adjustments. This approach effectively addresses the challenges of device parameter modeling and circuit design. Experimental data shows that the efficiency error for the SBD1 rectifier remains stable within 2%, and the average error for SBD2 is reduced to 1.5%. This method enables efficient and accurate optimization of RF parameters, providing a reliable technical pathway for the engineering application of Wireless Power Transmission systems. Full article

This work introduces a trimming technique based on eTrim technology to minimize both the input-referred offset voltage and its temperature drift in the operational amplifiers. The proposed low-voltage op-amp utilizes the body effect to maintain a constant bandwidth across the rail-to-rail input common-mode range under low supply voltages. During input common-mode transitions, the current in the folded cascode stage remains stable, ensuring a robust output stage. Furthermore, a specialized gain-boosting structure enhances the low-frequency gain while preventing occasional latch-up during low-voltage power-up. A pin-multiplexing scheme is employed for trimming data input, thereby eliminating the need for dedicated trimming pins and mitigating post-package parameter variations. At room temperature, a constant-current injection mechanism reduces the DC offset to microvolt levels. At high temperature, temperature-compensated current injection cancels the first-order drift component. Implemented in a low-voltage operational amplifier, post-layout simulation results demonstrate that with a 100-pF capacitive load, the amplifier achieves a gain–bandwidth product exceeding 10 MHz, a low-frequency gain greater than 140 dB, and an input-referred noise of 2.54 µVp-p for the P-channel input and 3.95 µVp-p for the N-channel input. The trimming process reduces the residual offset to the microvolt range and effectively suppresses offset drift, ensuring accurate offset compensation across the specified temperature range. Full article

At present, most of the research methods for vibration fatigue of welded structures mainly focus on uniaxial stress, ignoring the influence of shear stress. To this end, by combining the ASME structural stress method with the random and vibration analysis theory outlined in the IEC 61373 standard, a new method for evaluating the fatigue life of multi-axis random vibration problems in the frequency domain has been proposed. This method extends the structural stress method to multi-axis scenarios to accurately extract the local multi-axis structural stress state at the weld toe. Its advantage lies in the fact that it not only accounts for the influence of load frequency distribution and structural modal vibrations on fatigue life, but also incorporates the effect of local multiaxial stress conditions in the weld on fatigue life. Additionally, it includes corrections for non-proportional multiaxial stress conditions, resulting in fatigue assessment results that more closely reflect actual conditions. It was validated by comparing the local multiaxial stress, phase difference between shear and normal stress, and equivalent structural stress power spectrum of 0° and 30° fillet welded specimens with test results. Subsequently, it was applied to a multiaxial random vibration fatigue assessment of a vehicle-mounted electrical cabinet with experimental verification. The results indicate that fatigue life estimates based on a multi-axis stress state are lower than those obtained using a uniaxial method. Compared to traditional uniaxial methods, the multi-axis fatigue life estimates show a significant reduction ranging from 4.20% to 88.35%, effectively accounting for damage caused by shear stress. The fatigue assessment results are more closely aligned with experimental data, thereby validating the effectiveness of the proposed new method. The frequency-domain multiaxial random vibration fatigue assessment method proposed in this article provides a new technology for the design and evaluation of welded structures of vehicle-mounted equipment in rail vehicles. This method reduces costs during the design phase of rail vehicles, offering positive economic implications. Full article

The capacitance of the DC link is an important variable for the prediction of remaining useful life and failures in frequency inverters. The direct measurement of the DC-link capacitance in inverters operating under load is technically challenging and generally impractical. Recently, a great focus has been given to data-based soft sensors for estimating this variable. These methods, however, are evaluated based only on the estimate errors, and do not take into account the metrological aspects of these estimators. This paper proposes an uncertainty analysis method based on Monte Carlo simulations and bootstrapping that can be applied to all recently published methods for end-of-life (EOL) estimation based on data-driven regression and neural networks. A state-of-the-art model of EOL monitoring based on capacitance estimation was evaluated using the proposed framework, and an experimental study with a frequency converter drive for a brushless DC motor was performed, considering multiple output frequencies, loads and DC-link capacitance conditions. The output distributions are not symmetrical and show that the variable with the most significant impact in the propagated uncertainty is the DC link voltage. The results show confidence interval widths ranging from 12 μF to 61 μF, with wider confidence intervals obtained at higher power setpoints. Full article

A reliable and well-planned charging infrastructure is an essential pillar for enabling the widespread adoption of electric vehicles (EVs) and realizing their environmental and economic benefits. Car rental companies are increasingly transitioning towards EV fleets to support sustainability objectives, reduce emissions, and lower operational costs. However, EV charging management in rental car facilities presents unique challenges, including limited parking space, strict vehicle availability requirements, and unpredictable charging demand patterns. This study introduces a data-driven and probabilistic framework to estimate EV charging demand in rental car fleets. The proposed model integrates rental mobility data, vehicle technical specifications, and charging standards and employs Monte Carlo simulation to capture uncertainties in user behavior and charging processes. In addition, a priority-based charging management framework is developed to minimize technical disruptions in the power system, reduce infrastructure costs, and ensure efficient load distribution. The results demonstrate that the proposed framework supports sustainable charging infrastructure planning by improving charger utilization, enhancing grid compatibility, and enabling cost-effective EV fleet operations. Full article

Dry-type electrical transformers are essential components in commercial, industrial, and residential power distribution systems, as they adapt voltage levels required by a broad range of load types. Although they are robustly constructed, they are exposed to adverse operational and environmental conditions such as dust, humidity, and electrical disturbances that may cause premature winding damage, such as inter-turn short circuits. This study focuses on the detection of inter-turn short-circuit faults in a 15 kVA commercial dry-type transformer, where a fault equivalent to 11.54% of short-circuited turns was induced in the tap changers. Axial, radial, and rotational leakage magnetic flux signals were captured using a low-cost, non-invasive triaxial Hall-effect magnetic flux sensor. During data processing, Fisher Score feature selection was applied to identify the most relevant indicators. Subsequently, feature extraction techniques, including Linear Discriminant Analysis, Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection, and Isometric Mapping, were evaluated. The technique that best preserved global and local data structures was selected using Trustworthiness, Spearman’s correlation, and Kruskal’s stress metrics. PCA was selected as the optimal technique based on these quality metrics, achieving the highest classification performance. The resulting subspace data were classified using support vector machines and applying K-fold cross-validation. The proposed system achieved classification accuracies above 95%, with high recall and F1-score values, for inter-turn fault detection in each winding, confirming its effectiveness for reliable inter-turn fault detection in each transformer winding. Full article

Against the backdrop of large-scale integration of renewables into the power grid, frequent load-following operation of thermal power units substantially increases the difficulty of controlling boiler NOx emissions. Accurate forecasting of boiler NOx emissions is crucial for guiding efficient and clean operation under such flexible operating conditions. However, under frequent load-following conditions, NOx dynamics are highly nonlinear and non-stationary, making it challenging to achieve accurate prediction using only time-domain information. To address these issues, we propose DTTE-Net, a time–frequency feature fusion framework for predicting SCR-inlet NOx concentration in coal-fired boilers. DTTE-Net consists of three components: a time-domain branch, a frequency-domain branch, and a gated feature fusion module. The time-domain branch captures short-term fluctuations and long-range temporal dependencies, while the frequency-domain branch extracts complementary spectral representations to enhance the characterization of non-stationary fluctuations. The gated feature fusion module then adaptively integrates the two-domain features by using a gated mechanism and produces the NOx concentration forecast. In addition, a Gaussian kernel-based loss is introduced to improve robustness to nonlinear error structures. Experiments on real distributed control system data from a 660 MW ultra-supercritical coal-fired unit show that DTTE-Net outperforms existing baseline models, achieving lower forecasting errors and higher R². Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

The high penetration of distributed energy resources (DERs) poses numerous challenges to community energy management, including intense source-load stochasticity, synchronized load surges triggered by multi-agent gaming, and potential privacy breaches. To tackle these issues, this paper proposes a coordinated energy trading framework driven by an intermediate market-rate pricing mechanism. Within this framework, a novel Multi-Agent Transformer Proximal Policy Optimization (MATPPO) algorithm is developed, adopting an LSTM–Transformer hybrid architecture and the centralized training with decentralized execution (CTDE) paradigm. During centralized training, an LSTM network extracts temporal evolution features from source-load data to handle environmental uncertainty, while a Transformer-based self-attention mechanism reconstructs the dynamic agent topology to capture spatial correlations. In the decentralized execution phase, prosumers make independent decisions using only local observations. This eliminates the need to upload internal device states, significantly enhancing the privacy of sensitive local information during the online execution phase. Additionally, a parameter-sharing mechanism enables agents to share policy networks, significantly enhancing algorithmic scalability. Simulation results demonstrate that MATPPO effectively mitigates power peaks and reduces the transformer capacity pressure at the main grid interface. Furthermore, it significantly lowers total community electricity costs while maintaining high computational efficiency in large-scale scenarios. Full article

Achieving sustainable maritime transport requires comprehensive understanding of port-related emissions and evidence-based mitigation strategies. Maritime shipping significantly contributes to air pollution in port cities, threatening environmental sustainability and public health, yet comprehensive emission inventories remain scarce for major ports in developing economies. This study presents the first bottom-up emission inventory for Ambarlı Port, Turkey’s largest container port, utilizing AIS data from Global Fishing Watch for calendar year 2025. Emissions of CO₂, NO_x, SO₂, PM₁₀, PM_2.5, CO, and NMVOC were quantified using EMEP/EEA activity-based methodology with IMO Tier II emission factors and vessel type-specific load factors (75% for passenger, 45% for cargo) from ENTEC guidelines. Non-commercial vessels (tugs, service craft, fishing vessels) and lay-up vessels exceeding six months continuous berthing were excluded to focus on active commercial shipping operations, resulting in a validated dataset of 10,267 port visits from commercial cargo, passenger, and bunker vessels. Annual emissions from active commercial vessels totaled 404,766 tonnes CO₂, 8487 tonnes NO_x, 6724 tonnes SO₂, 914 tonnes PM₁₀, and 849 tonnes PM_2.5. Passenger vessels dominated the inventory (93.3% of CO₂) due to high auxiliary power demands for hotel services and elevated load factors, while cargo vessels contributed 6.5% despite representing 61.4% of port visits. Turkish-flagged vessels accounted for the majority of domestic ferry traffic. These findings provide baseline data for air quality management in the Istanbul metropolitan area and support policy development regarding shore power implementation, with particular emphasis on reducing emissions from passenger vessels with extended berth times. From a policy perspective, prioritized shore power investment at passenger ferry terminals emerges as the most cost-effective emission reduction strategy, with potential to eliminate over 90% of port-related air pollutant emissions through public-private partnership models. Full article

This study examines operating-point alignment during full-scale sea trials of a controllable pitch propeller (CPP)-equipped vessel by reconstructing engine load from measured shaft power and relating it to engine performance, fuel-consumption behavior, and combustion indicators. Engine-side performance and fuel-oil consumption records were integrated with shaft measurement data for a MAN 5S35ME-B9.5 low-speed two-stroke marine diesel engine to establish a common propulsion-based operating-point framework. The average shaft power at the 100% speed-trial point was 3471.1 kW, differing from the rated power by only −0.11%, and was adopted as the reference for shaft-load reconstruction. The reconstructed speed-trial operating points were aligned at 24.91%, 49.04%, 80.85%, and 100.00%, while the endurance points corresponded to 76.99% at NCR and 95.29% at MCR. Relative to the corresponding speed-trial references, the endurance points showed about 4.7% lower delivered shaft power, indicating that they should not be interpreted as identical to nominal speed-trial load labels. Fuel flow and combustion-related indicators showed physically consistent variation with increasing reconstructed load. These results demonstrate that measured shaft power provides a practical basis for harmonizing sea-trial datasets and for distinguishing propulsion-side operating conditions more consistently than nominal load labels alone. The proposed framework is particularly applicable to representative operating-point alignment in full-scale sea trials of CPP-equipped low-speed two-stroke marine diesel engines under comparable test conditions. Full article