International Journal of Energy Research最新文献

This study aimed to improve the design of an automatically controlled sail wind power station (SWPS). The peculiarity of the considered SWPS design is that its working body (WB) is rigidly connected to the upper platform of a Sholkor parallel manipulator that has six degrees of freedom. Six actuators connect the manipulator’s upper platform to the fixed lower platform. Each actuator is multifunctional and converts mechanical energy from wind action into electrical energy while controlling the WB’s movements. This wind energy conversion, by which the SWPS’s structural efficiency is evaluated, largely depends on the actuator’s coefficient of performance (CP). To meet the study objective, a prototype actuator was experimentally investigated to establish its efficiency. For this, a new experimental methodology was proposed, which involved sequentially experimenting on wind characteristics to obtain data, establishing a database, processing and preparing the initial data, and conducting a force analysis of the SWPS. Based thereon, the predicted power of the input load on the actuators was determined using Mathcad software. In the experimental setup, this predicted power was used as the actuator’s input, and the experimental value of the generated electrical energy (the output power) gave the actuator prototype’s efficiency. The actuator’s average experimental CP was $$ = 0.56−0.58, which demonstrates that this geometry’s dimensions and parameters are acceptable. The results of the study will be used to improve the design. The article emphasizes the potential of SWPSs for producing wind energy.

Solar-driven photoelectrochemical (PEC) water-splitting cells coupled with a photovoltaic (PV) cell as a photoanode have become an intriguing topic in solar energy conversion. In this study, for the purpose of developing a system with high efficiency, several photoanode materials were investigated to adjust the oxygen evolution reaction and the hydrogen evolution reaction (HER) energy bands. Among all, MAPbBr₃ with a wide bandgap (2.3 eV) was selected. However, the power conversion efficiency of the PV cell was not desirable due to the low light absorption. Therefore, the NiO–GeSe core–shell was placed inside the perovskite layer to enhance light absorption and carrier generation. In order to achieve a cell with the maximum performance, the core–shell height, and the shell radius were optimized, where the optimum structure was recognized with a core–shell height of 300 nm and a radius of 25–60 nm. The system’s total efficiency, which is represented by the solar to hydrogen efficiency, was then increased from 5.49% to 19.74% for the planar and nanostructure photoanode, respectively. The proposed PEC cell with the optimized photoanode is considered as the most efficient half-tandem and perovskite-based reported coupled system, operating without the need for an external voltage. In this study, three optical, electrical, and electrochemical models were solved using the finite element method to analyze the coupled system.

This study presents a novel method for airflow rate (i.e., wind speed) sensing using a three-dimensional (3D) printing-assisted flow sensor and a deep neural network (DNN). The 3D printing of thermoplastic polyurethane can realize multisensing devices for different flow rate values. Herein, the 3D-printed flow sensor with an actuating membrane is used to simultaneously measure two electrical parameters (i.e., capacitance and resistance) depending on the airflow rate. Subsequently, a data-driven DNN model is introduced and trained using 6,965 experimental data points, including input (resistance and capacitance) and output (airflow rate) data with and without external interferences during capacitance measurements. The mean absolute error (MAE), mean squared error (MSE), and root mean squared logarithmic error (RMSLE) measured using predicted flow rate values by the DNN model with multiple inputs are 0.59, 0.7, and 0.18 for continuous test dataset without interference and 1.16, 3.95, and 0.73 for test dataset with interference, respectively. Compared to the prediction results using single-input cases, the average MAE, MSE, and RMSLE significantly decrease by 70.37%, 88.74%, and 72.26% for test datasets without interference and 51.91%, 53.01%, and 12.20% with interference, respectively. The results suggest a cost-effective and accurate sensing technology for wind speed monitoring in wind power systems.

Energy poverty (EP) has emerged as a major challenge to achieving sustainable development goals, and its significance in social development has increased over time. This paper aims to analyze the spatial autocorrelation between EP and social factors on a global scale. Utilizing the panel data of 116 countries from 2012 to 2019, the Bivariate local Moran index, a representative spatial econometrics tool, has been employed to examine temporal changes and spatial differences of transboundary synergy and tradeoff relations between EP and social factors. The results indicate that EP has synergy relationships with social factors, including life expectancy at birth, access to immunization, CO₂ emission, and forest area, and tradeoff relationship with social factors, such as infant mortality rate, prevalence of undernourishment, forest rents, and gender inequality. Significant spatial differences have been observed that clusters of high-income countries, particularly those in the Global North, tend to have better energy access and are surrounded by areas with favorable social conditions, and clusters of lower-income countries, especially those in South Africa and Southeast Asia, have lower energy access and are surrounded by areas with more severe social conditions. The robustness analysis has been conducted to verify the reliability of the results. The spatial imbalance of findings offers robust evidence by emphasizing the importance of key areas, such as Southeast Asia and South Africa, that should be prioritized to take essential policy measures to address the EP and social issues.

Accurately forecasting electricity demand is crucial for maintaining the balance between supply and demand of electric energy in real-time, ensuring the reliability and cost-efficiency of power system operations. The integration of numerous active loads and distributed renewable energy sources into the grid has led to increased load variability, rendering the traditional point forecasting approach inadequate for meeting the evolving needs of the power system. Probabilistic forecasting, which predicts the complete probability distribution of loads and provides more extensive information on load uncertainty, has emerged as a key solution to address these challenges. The long short-term memory (LSTM) model, known for its strong performance in modeling long series, is commonly utilized in load forecasting. Therefore, this study focuses on short-term electric load probability forecasting for users in a specific park in Yantai. We propose a short-term load probability forecasting model based on integrated feature selection (IFS), genetic algorithm (GA) optimization of LSTM, and quantile regression (QR), referred to as the IFS-GA-QRLSTM model. Initially, the integrated feature selection method is employed to identify the most influential factors affecting electric load, optimizing the model’s input features and reducing data redundancy. To address the subjective nature of parameter selection in the LSTM model, we use a GA to optimize model parameters. The combination of optimized LSTM with QR enables direct generation of quantile load predictions, which are further used in kernel density estimation to construct the probability density distribution. We compare the proposed method with five basic models, QRLSTM, IFS-QRCNN, IFS-QRRNN, IFS-QRLSTM, and IFS-QRGRU, for point prediction, interval prediction, and probability prediction. Experimental results demonstrate that the proposed method in this paper exhibits better prediction performance, smaller prediction errors, and greater effectiveness compared to the aforementioned models.

Graphite is widely used in molten salt reactors (MSRs) because of its excellent properties. However, the irradiation lifespan of graphite in MSR is much shorter than the life of a conventional nuclear power plant, which lowers the load factor and increases the economic burden. In this paper, to extend the graphite irradiation lifespan, one uniform radial fast neutron field was designed by changing the fuel distribution in different radial fuel channels, which is called continuous core zoning. A positive correlation correction algorithm was used to adjust the fuel volumetric fraction (VF) of each zone. The optimization was carried out among different reactor sizes and different thicknesses of the reflector to study flattening characteristics. After optimization, the difference in fast neutron flux between different zones of the flattened region was less than 1%. Compared to the unoptimized case, the peak value of the fast neutron flux can be most reduced by 48.6%. The study employed a single-channel heat transfer model to investigate the temperature distribution within the both pre- and postoptimization core structure. The flow matching inlet condition and the average velocity inlet condition were considered. The results showed that to eliminate hot spots, a flow matching design is needed; otherwise, the temperature will rise greatly. Then the lifetime of graphite was calculated by combining fast neutron flux and temperature. Under average velocity inlet condition, graphite lifespan in CORE550-20 increased from 11.4 years of the unoptimized core to 18.9 years of the optimized core, only representing a 66% enhancement due to higher temperatures. Under flow matching conditions, the lifespan of graphite in CORE450-00 can be extended from 13.3 years of the unoptimized core to 26.8 years of the optimized core, indicating a 102% improvement.

The enhanced surface area and flow mixing offered by porous media are attractive features to augment the thermal performance of jet impingement heat sinks. However, the current jet-to-porous heat sinks demonstrate high pressure drop penalties and poor thermal performance due to the exacerbated jet momentum losses of the porous layer. In this study, jet impingement heat sinks with variable porous height layers are introduced to overcome the above drawbacks. The purpose of this study is using the porous domain in the selectively high-temperature areas of an annular heat sink and avoiding direct jet-to-porous interaction at the jet center. Consequently, the negative impacts of momentum loss are reduced and thus enhanced the thermohydraulic performance of jet-to-porous heat sinks. The realizable k-ε turbulent model for the fluid domain and the Darcy–Brinkman–Forchheimer equations for the porous domain are solved employing ANSYS Fluent for investigating the thermohydraulic characteristics of the system. It was shown that a jet impingement heat sink with a variable porous height layer, increasing in the radial direction, proves a significantly lower pressure drop penalty and thermal resistance than a constant porous height layer or a plain surface, while constant porous height shows lower thermohydraulic performance than the plain one at higher Re numbers. For instance, at a Re = 5,000 and HR = 0.5, the thermal resistance of a variable porous height layer is 9.63 × 10⁻⁵ (m²·K/W), which is 25% and 37% lower than that of the constant porous height surface and plain surface, respectively. The findings of the present study suggest that a variable porous height layer offers new ways to enhance the thermal management of future electronic systems.

Iron- and nitrogen-doped carbon (Fe─N─C) catalysts have garnered attention owing to their high oxygen reduction reaction (ORR) activity, which is comparable to that of Pt/C catalysts. Among the various methods for designing Fe─N─C catalysts, the use of templates has been emphasized as a means to create hierarchical porous structures. This strategy has enabled the achievement of high ORR activity. In this study, we propose a method for manufacturing a catalyst with high ORR activity by maximizing the interactions between commercial silica templates and catalyst precursors. By manipulating the charge on the commercial silica surface and adjusting the pH of the dispersion, the catalyst fabricated through these methods exhibited superior ORR activity compared to Pt/C and recently reported nonprecious metal catalysts. Through diverse physicochemical and electrochemical analyses, we confirmed that this activity stems from the effectively generated hierarchical porous structure and the resulting high density of Fe─N active sites. This catalyst exhibited a kinetic current density of over 2.73 mA cm⁻², which is more than double that of platinum and displayed a high ORR mass activity of 4.49 mA mg⁻¹. This strategy holds significant potential for application in various carbon-based materials, paving the way for the development of highly efficient electrochemical energy devices.

Gas starvation significantly affects the uniformity of stack. To reduce this phenomenon, a three-dimensional, nonisothermal, and two-phase full-size liquid-cooled stack model was developed, and the characteristics of the global and local nonuniformities of oxygen and liquid water in the flow field, membrane temperature, and current density were analyzed under sufficient oxygen supply and three different degrees of gas shortage. Advanced uniformity evaluation criteria (AUEC) were proposed to evaluate the overall uniformity of the stack, which can effectively characterize the local high nonuniformity of the cell units and the differences between edge and intermediate cell units. The results showed that with a decrease in cathode stoichiometric ratio, the AUEC of oxygen, liquid water, membrane temperature, and current density all decreased. The increase in the cooling liquid velocity increased the AUEC of the membrane temperature and current density in the stack, and the AUEC increased further when the oxygen supply was sufficient. In addition, the improvement scheme of GDL gradient porosity was proposed, which can effectively improve the uniformity of each parameter in the stack.

This paper reviews the current state of microbial fuel cell (MFC) technology for energy generation. It begins by exploring clean energy alternatives, focusing on waste-to-energy solutions, and introduces the concept, applications, and advantages of MFCs. The biochemical processes within MFCs are explained, highlighting how microorganisms metabolize substrates through glycolysis, the Krebs cycle, and the electron transport chain to generate electrons. These electrons flow through an external circuit and combine with protons and oxygen at the cathode to produce water or reduced forms of nitrogen and sulfur. This paper also analyzes 10 key parameters affecting MFC performance: coulombic efficiency, pH, temperature, substrates, organic loading rate, electrode potential, open circuit voltage, treatment efficiency, organic removal rate, and hydraulic retention time. Recent advancements in MFC technology are also discussed, including innovations in reactor configuration and scaling, the development of new membrane materials like earthen and ceramic, and improvements in wastewater treatment methods. The advancements also extend to genetic engineering techniques to enhance microbial efficiency and component modifications, such as the use of carbon-based nanomaterials and metal catalysts for improved performance, innovations in proton transfer membranes, and mediator-less MFCs utilizing metal-reducing bacteria. Challenges facing MFC technology, such as cost, scalability, and environmental sensitivity, are mentioned. The paper concludes with future directions, including the use of advanced materials, integration with wastewater treatment infrastructure, and the potential for nutrient recovery and chemical synthesis. This comprehensive review aims to provide knowledge into optimizing MFCs for sustainable energy generation and environmental benefits.