Energy Science & Engineering最新文献

Classifying emission reduction zones on different scales has important implications for the ecological protection and high-quality development of the Yellow River Basin. Based on remote sensing data and a light-carbon conversion model, carbon footprints at provincial, municipal, and county scales in the Yellow River Basin are measured. The spatiotemporal evolution critical paths of carbon footprints at the three spatial scales are compared and classified into different zones using spatiotemporal evolution analysis methods. The conclusions are as follows: (1) The carbon footprint increased over the years. The spatial distributions of carbon footprints at the three scales are not only consistent but also different. The study of carbon footprints at the county scale is more conducive to the summary of the spatiotemporal evolution and the formulation of detailed emission reduction schemes. (2) Four provinces, 48 cities, and 373 counties are designated as a “core protected zone”; three provinces, 29 cities, and 177 counties are designated as a “strictly governed zone”; one province, 12 cities, and 47 counties are designated as a “key restricted zone”; four cities and 39 counties are designated as an “alert diffusion zone.” (3) The agglomeration expansion trend and the spillover effect of high-carbon footprint units at the county scale are more obvious. Further enhancement of the path-locking characteristics of the carbon footprints of counties will make governance more difficult. Effective governance of carbon footprint at the county scale is of urgent concern. These results provide scientific evidence for multiscale carbon emission control and zoning policy formulation in the Yellow River Basin.

This article investigates the evolution of vortex structures in the impeller channel of a multiphase pump. By capturing the vortices in the impeller channel using the vorticity and Q-criterion, the generation location of the vortex structures is analyzed, and the pressure fluctuations induced by vortices in the main flow passage of the impeller are studied in terms of their time- and frequency-domain characteristics. The relationship between the vorticity and the amplitude of pressure fluctuations at the main frequency of the impeller is further investigated. This study develops a method of identifying vortices in the impeller channel of the multiphase pump, and reveals the intrinsic connection between the vortices and pressure fluctuations in the main flow passage. These findings offer some suggestions for eliminating the influence of vortices and enhancing the pressurizing capabilities of multiphase pumps.

As the global momentum for wind power generation accelerates, the industry faces substantial challenges due to premature failures in wind turbine components. These failures, particularly in critical elements like the high-speed shaft bearing, lead to significant operational losses, including unplanned downtime and elevated maintenance costs. To mitigate these issues, it's crucial to have precise predictions of the remaining useful life (RUL) of these components, enabling timely interventions and more efficient maintenance schedules. This article proposes advanced, data-driven approaches for estimating the RUL of wind turbine high-speed shaft bearings, utilizing cutting-edge techniques such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent units (GRU), and random forest (RF) algorithms. Our analysis leverages vibration data from a 2 MW wind turbine equipped with a 20-tooth pinion gear, providing a thorough validation and comparison of these methodologies against traditional models. Our results reveal that the LSTM and BiLSTM models excel in both accuracy and computational efficiency for predicting RUL and enhancing system prognosis, surpassing the performance of conventional RF and GRU methods. This research underscores the potential of our innovative data-driven strategies to develop effective RUL estimation algorithms, significantly advancing wind turbine proactive operation and maintenance operations.

This study investigates the impact of hybrid nanoparticles on the temperature of nuclear reactor coolant, with a focus on graphene nanoplatelet (GNP)-based hybrid nanoparticles. Sixteen different hybrid nanofluids were analyzed, and their performance was compared with a standard water-based coolant. The criticality values were obtained through MCNP modeling, revealing that higher nanoparticle ratios led to increased criticality, with the highest value of 1.3239 observed in GNP-Fe₃O₄ + Al₂O₃ nanofluids (0.05 wt%) and the lowest value of 1.2935 in GNP–Fe₃O₄ + SiO₂ nanofluids (0.001 wt%). Temperature variations showed that increasing nanoparticle concentrations resulted in slightly higher temperatures, with a maximum of 611.97 K for 0.05 vol.% GNP nanoparticles. Additionally, the departure from nucleate boiling ratio values were consistently above the safety threshold of 2.08, with the lowest value of 3.657 for GNP–Fe₃O₄ + SiO₂ nanofluids (0.05 vol.%). These findings suggest that hybrid nanofluids, particularly those with higher nanoparticle ratios, can enhance the thermal performance and safety margins of nuclear reactor coolants, offering a promising avenue for future research and application.

In view of problems such as the narrow efficiency area, large hydraulic vibration area, pressure pulsation, and serious sediment wear of turbines at the Futang hydropower station, the technical transformation of turbine runners was carried out by modifying the blade shape and increasing the blade thickness, and a combination of numerical simulations based on shear stress transport k–ω turbulence model and tests was adopted to improve the operational stability of power station units. Calculation and testing demonstrate an enlargement of the high-efficiency zone. Specifically, the optimal efficiency of the runner increases by 0.37%, while the rated efficiency rises by 0.19%. Significant reductions are observed in pressure pulsation within the draft tube and vaneless area decrease of approximately 50%. There is a high-frequency pressure pulsation in the vaneless zone and the runner under low-load conditions, and the influence of dynamic and static interference gradually weakens with the increase of opening. The draft tube is prone to eccentric vortex bands under partial working conditions, which causes the unit to be affected by low-frequency pulsation. This optimization also leads to a notable decrease in runner blade wear, with the maximum sand and water velocity reduced from 45 to 40 m/s, resulting in a 30% reduction in sand wear. Moreover, there is a substantial enhancement in the runner's stiffness, with the thickness of the blade near the high stress area of the upper crown and lower ring increasing by over 50%, and the weight of each individual blade increasing by more than 50%. These research findings validate that modifying the runner blade effectively improves flow patterns, reduces eddy current generation, minimizes pressure pulsation, widens the high-efficiency zone, decreases wear, and enhances the operational stability of the unit. The technical transformation method and research results of this study have important guiding significance for similar technical transformation of other power stations

Jordan's energy sector faces significant challenges due to rising fuel prices, making the exploration of local energy resources crucial. The abundant oil shale deposits in Wadi Thamad present a promising opportunity. Since Wadi Thamad oil shale has never been studied before, this research focuses on the Wadi Thamad basin near Madaba, Jordan, aiming to comprehensively characterize its oil shale using advanced analytical techniques. Using X-ray diffraction, Fourier transform infrared spectroscopy, X-ray fluorescence, scanning electron microscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry, this study assesses the mineralogical, chemical, and thermal properties of Wadi Thamad oil shale. The findings reveal calcite and quartz as the primary minerals, with significant aliphatic, CO₂, hydroxyl, and carboxyl groups. Elemental analysis highlights essential oxides, such as CaO and SiO₂. Fischer assay results indicate an oil content of 5.3–10.1 wt%, a gross-calorific value of 4.56–7.69 MJ/kg, and a sulfur content of 1.77–2.10 wt%. The peak pyrolysis temperature is 432.4°C from TGA. This research's novelty lies in its comprehensive approach to characterizing the underexplored Wadi Thamad oil shale basin. The findings enhance the understanding of Wadi Thamad's geological composition and underscore its potential as a local energy resource, contributing valuable data to Jordan's energy portfolio and offering economic benefits.

The time-varying external environment is one of the main variables influencing heating energy consumptions, so that its influence should be rectified when energy savings of different heating modes are calculated. This paper proposes an energy consumption rectification method based on Bayesian linear regression and heating degree-days, to obtain heating energy consumptions without the influence of different outdoor temperatures. The proposed method consists of three main steps. First, a physical model of heating houses is used to prove a relationship between energy consumptions and heating degree-days. Second, Bayesian linear regression is exploited to estimate uncertainty ranges of heating energy consumptions. Finally, heating energy consumptions are rectified, and energy savings with their uncertainty ranges for different heating modes under the same outdoor temperature are obtained. The proposed method does not require the physical parameters of heating houses to facilitate practical implementation. Additionally, it provides uncertainty ranges of heating energy consumptions to measure the estimation accuracy. Numerical and experimental examples show that the proposed method provides more accurate estimates of heating energy consumptions than existing methods.

A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel-based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional-integral-derivative controller, a neural network-equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions.

The instability of solar energy is the biggest challenge to its successful integration with modern power grids, and accurate prediction of long-term solar radiation can effectively solve this problem. In this study, we proposed a novel long-term solar radiation prediction model based on time series imaging and bidirectional long short-term memory network. First, inspired by the computer vision algorithm, the recursive graph algorithm is used to transform the one-dimensional time series into two-dimensional images, and then convolutional neural network is used to extract the features from the images, thus, the deeper features in the original solar radiation data can be mined. Second, to solve the problem of low accuracy of long-term solar radiation prediction, a hybrid model BiLSTM-Transformer is used to predict long-term solar radiation. The hybrid prediction model can capture the long-term dependencies, thereby further improving the accuracy of the prediction model. The experimental results show that the hybrid model proposed in this study is superior to other single models and hybrid models in long-term solar radiation prediction accuracy. The accuracy and stability of the hybrid model are verified by many tests.

To intuitively and systematically grasp the development status and trend of safety evaluation research in China's coal mining operations, we consulted relevant literature in the fields of “coal mine” and “safety evaluation” collected by China Knowledge Network, and deployed CiteSpace V software to summarize and analyze research pertaining to safety evaluation in China's coal mines from three aspects: research institutions, authors, and hot keywords. With respect to research institutions, the results show that although many researchers have conducted in-depth evaluations of coal mine safety, most of these studies were independent, and two-way information exchange and cooperation between research institutions remains scarce. With respect to authors, most cooperation between authors has been limited to team members, and relatively stable research teams have been formed. However, most of these research teams are independent. With respect to hot keywords, the trends of coal mine safety evaluation studies exhibit continuous change, with an overall increase in richness. According to the frequency burst times of important keywords, “entropy weight method” and “mining with pressure” are expected to remain important keywords in future evaluations of coal mine safety in China.