International Journal of Energy Research最新文献

The Yongbyon reactor in North Korea represents a significant global security threat because of its potential for plutonium production, which can be utilized in nuclear weapons. The nuclear tests conducted at the Yongbyon research reactor from 2006 to 2017 highlight the necessity for accurate assessments of its plutonium production capabilities. This study estimated the plutonium production potential of the Yongbyon reactor to be ~51 kg, based on its operational history and analysis using the Monte Carlo code for advanced reactor design (McCARD) code. Sensitivity analysis indicates that the most critical variable for predicting plutonium production capacity is the integrated thermal power release data from the reactor. Factors such as the temperature of fuel and coolant, and the number of neutron samples in the McCARD have a negligible impact (less than 1%) on the estimates of plutonium production. Regardless of how diverse the history of thermal power is, or what value the maximum power reaches (20 or 25 MWt), the integrated thermal energy consistently determines the amount of plutonium produced, emphasizing its significance in the analysis.

This study presents a front-end engineering design (FEED) methodology for an integrated CO₂ transport–injection–storage system, utilizing multiobjective optimization (MOO) and nodal analysis. The methodology’s performance is validated through a carbon capture and storage (CCS) demonstration project in the Gunsan Basin (GB), South Korea. This approach employs the dynamic inflow performance relationship (IPR)−outflow performance relationship (OPR) technique, applying it to the FEED of the CO₂ transport–injection–storage system to enable CO₂ injection into a saline aquifer via a single injection well connected through an onshore hub terminal and a subsea pipeline. By adjusting decision variables (CO₂ discharge pressure at the onshore hub terminal, pipeline diameter, tubing diameter, and CO₂ temperature at the wellhead), three objectives (CO₂ storage capacity, safety, and economic benefit) are optimized through MOO, identifying the Pareto-optimal front (POF) among objective functions. These trade-off solutions provide reliable ranges for the four decision variables used in the nodal analysis, which considers real-time pressure and temperature variations in the system during CO₂ injection, along with the associated facility qualifications and operating conditions. This analysis determines the IPR−OPR at the bottom of the injection well and the corresponding pressure–flowrate, defining the practical FEED scope for the integrated CO₂ transport–injection–storage system. By integrating optimal solutions from both MOO and nodal analysis, the study identifies the final nondominated solutions for efficient and stable CO₂ geological storage. The proposed methodology offers decision-makers robust scenarios for facility qualifications and operating conditions, considering CO₂ storage capacity, safety, and economic efficiency at the FEED stage of a CCS demonstration project.

This article presents a novel hybrid machine learning time series model (MLTSM) for predicting the electrical output of solar photovoltaic (PV) systems, integrating a physics-based theoretical model with an ensemble of data-driven regressors. The study addresses the challenge of solar energy’s variability by enhancing predictability for grid integration. Using a 34-day dataset from two solar power plants in India, we engineer critical features—including irradiation and ambient temperature, transformed via a third-degree polynomial derived from PV system physics—to improve forecasting accuracy. We conduct a comprehensive evaluation of multiple machine learning (ML) models, including linear regression, ridge regression, decision trees (DTree), random forest (RForest), and K-nearest neighbors, and propose a weighted hybrid ensemble that combines the top performers. Among the individual models, linear and ridge regression demonstrated superior performance. The proposed hybrid model achieved a notable R  ² value of 98% for Plant 1 and 91% for Plant 2, with root mean squared errors (RMSEs) of 36–66 and 42–127, respectively. This study contributes a publicly available dataset, a novel physics-informed feature engineering methodology, and a scalable hybrid forecasting framework that offers a practical balance of accuracy, computational efficiency, and interpretability for real-world solar energy forecasting.

This study proposes a preactivated residual neural network (ResNet) with long short-term memory (LSTM) to predict electric vehicle (EV) charging demand at an individual fast-charging station. While fast-charging stations offer convenience to EV users, the use of fast-charging stations can also threaten the stability and quality of the power system. Therefore, it is important to accurately forecast the charging demand at individual fast-charging stations for the operation of the power system. The proposed model incorporates two deep learning models: ResNet and LSTM. The ResNet is used to perform the feature extraction needed for forecasting fast-charging patterns. The LSTM performs forecasting of fast-charging demand based on sequential input. The proposed model ensures superior forecasting performance without vanishing gradient. Furthermore, the structure of the preactivated ResNet enables optimal parameter updates based on the loss function of mean squared error (MSE). The proposed model was evaluated with real-world data from EV fast-charging stations in Jeju Island, South Korea. The maximum prediction performance of the proposed model was attained with 8.04% in the normalized root MSE and a mean absolute error (MAE) of 4.71 kW.

Lithium-sulfur (Li–S) batteries offer exceptional theoretical energy density, yet their practical realization remains limited by polysulfide shuttling and sluggish redox kinetics. Conventional interlayers typically mitigate only one of these bottlenecks, either improving conductivity or providing polysulfide adsorption, which proves insufficient under realistic conditions. Here, we introduce a multifunctional interlayer composed of electrospun polyvinylidene fluoride (PVDF) nanofibers embedded with MXene/transition metal oxide (TMO) hybrids and compacted via hot pressing. This design uniquely integrates MXene’s conductivity, TMO’s strong polar–polar adsorption and catalytic activity, and PVDF’s structural flexibility, producing a single architecture capable of suppressing shuttle effects, accelerating LiPS conversion, and stabilizing the electrode-interlayer interface. Electrochemical evaluation demonstrates high discharge capacities of 1032 mAh g⁻¹ for PVDF-calcined MXene (P-CM) interlayer and 931 mAh g⁻¹ for PVDF-MXene/SnO₂ (P-MS) interlayer at medium sulfur loading (2–3 mg cm⁻²), with outstanding long-term retention of 800 mAh g⁻¹ after 100 cycles and ultralow fading rates of 0.23% and 0.18% per cycle. Strikingly, at high sulfur loadings (5–6 mg cm⁻²), both interlayers sustained similarly low decay rates (0.18% per cycle), highlighting their robustness under practical conditions. Electrochemical impedance spectroscopy revealed a > 94% reduction in polysulfide shuttle resistance, directly confirming the efficient immobilization and conversion of soluble lithium polysulfides (LiPSs). Moreover, Li ⁺ diffusion coefficients were boosted to 9.89 × 10⁻⁸ mg cm² s⁻¹, nearly two orders of magnitude higher than previously reported values, while postmortem X-ray photoelectron spectrometer (XPS) identified Sn–S, Sn–O, and S–Ti–C bonding as evidence of strong chemical interactions. This work presents the first electrospun PVDF-based interlayer integrating MXene/TMO hybrids, establishing a multifunctional strategy that concurrently resolves shuttling and redox kinetics limitations. The ability to maintain high stability at practical sulfur loadings, coupled with scalable electrospinning and hot-pressing fabrication, underscores its potential for enabling next-generation Li–S batteries.

The ensemble learning technologies represented by bagging show notable performance in the field of high-performance electrical load classification researches. However, bagging frequently encounter classifier redundancy issue which significantly impacts classification accuracy. Therefore, to research a suitable base, classifier selection strategy is one of the most important directions to improve the effectiveness of ensemble learning participating in the load classification tasks. Therefore, aiming at solving the redundancy issue of the base classifiers in the bagging-based ensemble learning, this article presents a META learning-based dynamic selective ensemble strategy. First, the class labels of the load data samples can be achieved using the exponential similarity (Esim) distance-based spectral clustering and k-medoids clustering. Second, according to the labeled load samples, an ensemble-based back propagation neural network (BPNN) load classification model can be constructed. Afterward, a META learning-based dynamic selective ensemble strategy of optimizing the base classifiers ensemble is presented. Specifically, META feature sets (MFSs) of base classifiers are defined and extracted. And then, a META discriminator is trained using the MFSs, which is finally able to select suitable base classifiers ensemble for the classification for each individual sample to be classified. Ultimately, case studies are carried out using the UCI Electrical Grid Stability Simulated Dataset (EGSSD) and UCI Electricity Load Diagrams 2011–2014 Dataset (ELDD). According to the experimental result, the effectiveness of presented strategy of improving the classification performance can be identified.

A self-priming pump is a type of centrifugal pump capable of automatically evacuating air from both the pump casing and suction pipe upon startup, enabling water intake without manual priming. Due to this feature, it is widely used in various applications. To investigate the self-priming characteristics, this study first conducted the hydraulic design and manufacturing of an external-mixing self-priming pump. Subsequently, experimental research was carried out on a visualized test rig, focusing on the real-time liquid level changes in the inlet and outlet pipelines during the priming process. Experimental results demonstrate that increasing the inlet pipe length significantly prolongs the self-priming duration, with the most substantial impact observed during the oscillatory exhaust phase. The fundamental mechanism involves delayed liquid replenishment caused by enhanced flow resistance, which induces reciprocating oscillations at the gas–liquid interface. Extended inlet pipes markedly prolong the oscillatory exhaust duration while only marginally increasing the rapid exhaust phase. Shorter pipe configurations result in ambiguous boundaries between these two distinct exhaust stages. As the water level in the tank decreases, the time required to complete self-priming increases significantly. The research findings provide critical guidance for optimizing self-priming system configurations through pipe length optimization and flow resistance management.

Biomass carbon has emerged as a particularly promising candidate for nonprecious metal oxygen reduction catalysts, owing to its advantages as a renewable precursor and its relatively low cost. In this study, a highly active oxygen reduction catalyst (denoted as MN/BSC) based on nonprecious metal heteroatom-doped carbon derived from biomass was prepared using a synergistic modification method involving ball milling and molten salts. This process yielded co-doped MN/BSC catalysts with surface areas reaching up to 952 m² g⁻¹ and total porosities of 0.725 cm³ g⁻¹. The half-wave potential of MN/BSC (0.929 V vs. RHE) is comparable to that of Pt/C, indicating its significant catalytic performance for oxygen reduction. The discharge performance of the self-assembled Mg-O₂ cell also outperformed Pt/C in all aspects. The high activity can be attributed to the synergistic pretreatment effect of ball milling and molten salts, which led to the formation of numerous defects in the carbon matrix. This synergy increases the effective active area involved in catalysis. Furthermore, the low-melting salts act as templating and pore-forming agents, which facilitate the dispersive doping of Fe and N, thereby increasing the number of active sites. Given these results, waste-derived biomass carbon catalysts show considerable promise for use in metal fuel cells and electrocatalytic applications.

Several collections of phosphate glasses loaded with different amounts of neodymium (Nd₂O₃) oxide were prepared in this work using the melt-quench process. FTIR spectroscopy shows that Nd³⁺ is tightly bound through the phosphate structure, with minimal clustering at low doping levels. Two distinct sections—a plateau component over high frequencies and a falling apart over low frequencies—indicate the frequency dependency of ε′. (ε′) and (σ_ac) clearly drop at 0.25 as well as 0.5 mol% Nd₂O₃ doping, and they gradually increase as concentrations grow. Phy-X/PSD software was used to calculate the effective electron density (G_Nef), effective conductivity (G_Cef), equivalent atomic number (G_Zeq), also exposure buildup factor (G_EBF), and energy absorption buildup factor (G_EABF) by using the G-P fitting technique. It has been demonstrated that the reported G_Nef, G_Cef, G_Zeq, G_EBF, and G_EABF are all influenced by penetration depths, photon energy, and the glass sample’s Nd₂O₃ mol% content. These findings confirm that the studied glasses, particularly the Nd-1.0 sample, are suitable for use in the gamma-ray shielding domains. The glass sample (Nd-0.5) has the highest transmission speed among the samples under investigation, making it the ideal material for microelectronic components.

The present study assessed the drift-flux parameter correlations implemented in the TRACE code, the flagship thermal-hydraulic analysis code developed by the United States Nuclear Regulatory Commission (US NRC). The code is architected on the basis of a two-fluid model. The interfacial drag force is formulated by the Andersen–Chu approach to avoid the interfacial area concentration dependence on the interfacial drag force. Thus, the interfacial drag force formulation requires the drift-flux parameters, such as the distribution parameter and the drift velocity. The TRACE code adopts different drift-flux parameters for different flow channel geometries, such as pipes and rod bundles. The implemented drift-flux correlations for dispersed two-phase flows are the Kataoka–Ishii drift-flux correlation for pipes and the combination of the Bestion drift velocity correlation and the distribution parameter of unity for rod bundles. Since these correlations were developed before 1990, this paper discusses the validity of these correlations based on data collected after 1990. First, the assessment confirmed that the Kataoka–Ishii drift-flux correlation was valid for beyond-bubbly flows in pipes. The assessment also demonstrated that the Hibiki–Tsukamoto correlation offered improved accuracy compared to the Kataoka–Ishii correlation in bubbly to beyond-bubbly flow in pipes. The distribution parameter set at unity in the TRACE code tended to underestimate experimental values in rod bundles, whereas the drift velocity calculated by the Bestion drift velocity correlation tended to overestimate the experimental data in rod bundles at low-pressure conditions. The tradeoff between the underestimated distribution parameter and overestimated drift velocity resulted in reasonably good predictions. Finally, the assessment demonstrated that the Hibiki–Tsukamoto correlation improved the data prediction accuracy in rod bundles. Considering the future use of the TRACE code for various new nuclear reactor designs and accident scenarios, including low-pressure and low-flow rate conditions, this study recommended replacing the current drift-flux correlations implemented in the TRACE code with the advanced drift-flux correlations.