Energy Science & Engineering最新文献

This study numerically investigates thermal transport and fluid dynamics in a triangular cavity filled with a MgO–Ag–H₂O hybrid nanofluid containing an undulating porous fin under electromagnetic field and thermal radiation influences. The governing equations are solved numerically using the Galerkin finite element methodology with Darcy–Forchheimer formulation for porous media representation. A comprehensive parametric study examines the effects of Rayleigh number (Ra, 10³–10⁶), Darcy number (Da, 10⁻⁵–10⁻²), Hartmann number (Ha, 0–80), magnetic field orientation angle (γ, 0°–90°), nanoparticle concentration (φ, 0.005–0.02), heat generation coefficient (λ, 1–5), fin waviness parameter (n_w, 0–6), and radiation intensity factor (Rd, 1–5). The numerical model is validated against established benchmark solutions, demonstrating excellent agreement. Findings demonstrate that increasing Ra substantially improves thermal transport and flow intensity, with the average Nusselt number rising by up to 65% and maximum velocity magnitudes increasing by over 500 times. Electromagnetic field application inhibits thermal transport, with (Nu_av) decreasing by 55.6% as Ha increases from 0 to 80. Magnetic field angle optimization shows that γ = 60° provides better heat transfer than γ = 0° at high Ha values. Nanoparticle addition provides moderate thermal enhancement, with an 11.1% increase in Nu_av as φ increases from 0.005 to 0.02, particularly in low-Ra regimes. Radiation effects become most significant at elevated Ra values, with (Nu_av) nearly tripling as Rd increases from 1 to 5 at Ra = 10⁶. Entropy generation analysis reveals that the Bejan number decreases by 98.7% as Ra increases, indicating fluid friction dominance at higher Ra values. These results offer essential guidance for optimizing thermal management systems involving porous structures, nanofluids, and electromagnetic fields.

In recent years, infrared image-based insulator defect detection technology has been widely applied in the field of online monitoring for power equipment due to its noncontact and high-efficiency characteristics; however, existing algorithms still face issues such as insufficient detection accuracy and low computational efficiency in multistate insulator classification tasks, making it difficult to meet practical engineering requirements; to address these challenges, this paper proposes an improved small-target multidefect detection algorithm Porcelain insulator Small-target Heating defect detection You Only Look Once (PSH-YOLO): based on YOLOv8, it employs a hybrid model of self-attention and convolution to aggregate both convolutional and self-attention features; then applies the MobileViT network to enhance the model's training speed and parameter efficiency, ensuring the overall lightweight nature of the model; additionally incorporates a bidirectional feature pyramid network to improve accuracy through multilevel feature pyramids and bidirectional information flow; finally, utilizes the Inner-WIoU loss function to effectively reduce oscillations during training while further enhancing the model's accuracy; to obtain test data, this paper conducted infrared imaging experiments on defective insulators to capture images under varying conditions; experimental validation confirms that the proposed multidefect small-target YOLO algorithm, PSH-YOLO, achieves an average accuracy improvement of 6.17%, with Giga Floating-point Operations Per Second reduced to 7.1, fulfilling the requirements for identifying small-target insulator defects, while ablation and comparative studies demonstrate the effectiveness and superiority of the proposed algorithm.

Traditional agricultural practices significantly contribute to soil degradation, water pollution, and greenhouse gas emissions, posing substantial challenges to environmental sustainability and global food security. Addressing these issues necessitates the adoption of low-carbon strategies and the integration of advanced technological innovations. This review emphasizes the need to transition from conventional, environmentally harmful farming systems to sustainable models that can meet the demands of population growth and climate change. The literature review synthesizes agri-environmental engineering principles with precision agriculture, the Internet of Things (IoT), Artificial Intelligence (AI), Big Data analytics, and renewable energy applications. The findings indicate that low-carbon strategies and innovative technologies can reduce the carbon footprint of agricultural systems, minimize soil erosion, decrease water pollution, and lower greenhouse gas emissions. Additionally, these practices promote resource conservation, optimize energy use, and sustain productivity. Transitioning to technologically advanced, low-carbon agricultural systems is therefore critical for environmental protection, energy efficiency, and long-term resilience. Integrating sustainable practices and smart technologies enables agriculture to become a more adaptable and environmentally responsible sector, preserving natural ecosystems and supporting global food security.

The unsatisfactory engine performance can be enhanced by the fuel reformulation technique in which the nano additives are included in the B20 (20% of methyl ester mixed in 80% of diesel) sample. In the present study, a novel nano additive such as Aluminium oxide (Al₂O₃), Graphene Oxide (GO), and Carbon Nanotubes (CNTs) are added in B20 mix (20% Vol. of Yellow Oleander Methyl Ester (YOME) is blended in 80% Vol. of standard diesel) and employed on a single cylinder, four stroke, diesel engine. The study is focused on evaluating the Energy (E), Exergy (ex), and sustainability index (SI) through the energy and exergy distributions using first and second laws of Thermodynamics (TD) for the prepared fuel samples, including D100, B20, B20A50, B20GO50, and B20CNT50. The engine operated with the prepared blends at standard conditions such as Compression Ratio (CR) (17.5:1), Rated speed (1500 rpm), Injection Timing (IT) (23° bTDC), and Injection Pressure (IP) (220 bar). The nano-assisted fuel samples showed enhanced performance characteristics (Brake Thermal Efficiency (BTE) increased by 15.94%, and Brake Specific Fuel Consumption (BSFC) reduced by 20.5%) Energy, and Exergy efficiencies (η_E, η_ex), SI, and Exergy Performance Coefficient (EPC) by 33.6, 23.6, 7.14, and 13.7, %, respectively, for B20CNT50 blend at higher Brake Power (BP). The blend B20CNT50 proved to be a more promising fuel sample than the remaining fuel mixtures in a significant variation in engine performance, Energy (E), exergy (ex), and SI. It is not just a promising alternative but also a more sustainable and effective energy source to use with nano-assisted biodiesel-diesel blends. This article recommends more investigations and research into engine optimization and the development of sustainable energy alternatives.

Electric vehicle (EV) is a resurging technology with a promising future. However, range anxiety and lack of charging infrastructure remain challenges for the mass-scale adoption of EVs. Nevertheless, with technological advancements and rapid development of charging infrastructure, EV adoption has increased massively. On the one hand, the adoption of modern EVs has dramatically increased. On the other hand, retrofitting of conventional vehicles to EVs has significantly gained attention, especially in developing countries. One of the alarming concerns related to retrofitting is less awareness related to the retrofitting challenges that may raise safety issues along with the range anxiety. This research project identifies the challenges of retrofitting conventional gasoline engines to EVs while assessing battery bank capacity, drive train motor performance, and charging impact. A three-wheel gasoline vehicle is converted into an EV to identify design, operational, and mass-scale charging impacts. A three-wheeled petrol-engine vehicle was selected for the conversion. The geographic location of Karachi Pakistan was selected for testing the retrofitted vehicle. In the first phase, a simulation study is conducted using drive train simulation software for the selection of the electric motor and the sizing of the battery bank. In the second phase, the converted vehicle is tested on the road to analyze operational characteristics, that is, battery drain time, speed, and performance of the traction motor. In the third phase, mass-scale charging power requirements are quantified. The results revealed that conventional car transformation into an EV can pose challenges in all three phases, that is, design, operation, and mass-scale charging. It was analyzed that a low space constraint for the battery reduces the battery bank, eventually restricting the vehicle operation to only 15–32 min with a speed of 10 and 20 km/h. On the other hand, with the higher mass vehicles charging, the total power required is 125 kW with a 0.7 demand factor, whereas 117 kW of charging is required in the nighttime during peak hours, which can put a load on the grid with the increasing number of vehicles and less travel time.

Investigating the relationship between factors affecting the output power of photovoltaic (PV) cells is crucial for enhancing the efficiency and stability of PV power generation. Traditional PV models have problems such as many parameters, strong nonlinearity, and difficulty in numerical solution. In addition, there is a lack of precise quantitative methods to determine the relationship between different influencing factors. To address this problem, the traditional PV model is simplified and parameters are optimized by taking a single-diode monocrystalline silicon PV cell as an example. The grey correlation theory is introduced to analyze the factors affecting the performance of PV cells, and the correlation between each factor and the maximum output power point is calculated. The results show that the proposed PV model is sensitive to each parameter. The grey correlation method is used to quantitatively calculate the correlation, effectively revealing the relative importance of different factors and the maximum output power, and clarifying the influence of each parameter on the maximum power point. It provides a strong support for the optimization design of large-scale PV power generation systems.

This study focused on the production of hydrocarbon fuels from bio-slurry through an innovative electrolytic process powered by solar energy. The bio-slurry, a byproduct of anaerobic digestion, presents disposal challenges, especially in areas without farmlands for use as organic biofertilizer. To address this issue and contribute to cleaner energy production, the study aimed to catalyze bio-slurry degradation into hydrocarbon fuels using an electrolytic biomass solar cell (EBSC). Powered by a 40 W solar panel, the setup employed a 9000 mL bio-slurry capacity, alongside geo-catalysts and iron oxide catalysts to enhance the efficiency of degradation and gas production. The experiment yielded significant volumes of biofuels, including bio-methane (20.42%), bio-ethane (24.00%), and propane (35.10%), with gas composition analyzed via GC-MS. The use of the “Ebarra” (a geo-catalyst) electrocatalyst significantly increased methane and ethane production. This process could be scaled up for industrial applications with the use of solar panels of higher capacity in large bio-slurry systems, as well as proportionate catalysts to enhance the process. This process presents a sustainable method for converting bio-slurry into valuable hydrocarbon fuels, contributing to environmental conservation and renewable energy development. This method not only converts bio-slurry into valuable hydrocarbon fuels but also minimizes harmful byproducts, contributing to a lower carbon footprint compared to traditional energy production methods, such as the use of water to produce Hydrogen energy, among others.

In today's modern world, people spend most of their time indoors, making indoor air quality (IAQ) a critical concern, particularly in educational buildings, where densely occupied classrooms demand clean and healthy environments. This study enhances the IAQ of an existing college building in West London by aiming to reduce carbon dioxide (CO₂) concentrations and SARS-CoV-2 infection risk, while maintaining or improving energy efficiency and thermal comfort, assessed using the predicted percentage of dissatisfied (PPD). A multi-objective optimisation was conducted using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). A novel approach combining optimisation with EnergyPlus and CONTAM co-simulation was proposed to obtain the final results. Various scenarios were developed, reflecting different priorities. Energy-saving scenarios increased PPD by 15.3% to 17.9%, while IAQ- and comfort-focused scenarios raised energy consumption by 26.95% to 53.91% but maintained or improved comfort. EC45 as a mixed-priority scenario, along with IAQ-priority scenarios, achieved the lowest average SARS-CoV-2 infection risks (9.6%–10.7%). Meanwhile, other mixed-priority (EP45-ECP33) scenarios reduced PPD by 13.9% and maintained a 17% infection risk with only a 29% increase in energy use. This comprehensive approach demonstrates the potential for achieving healthier indoor environments in educational buildings without excessively compromising energy efficiency or occupant comfort.

This study addresses the significant discrepancies in traditional methods for predicting the height of water-conducting fracture zones in deep-mining hard roofs, which can lead to catastrophic water inrush events. The 110,504 working face of Banji Coal Mine was chosen as the research site to systematically investigate the development characteristics of these fracture zones through a combination of theoretical analysis, field measurements, and numerical simulations. A key stratum identification model was proposed, based on the temperature-compensated elliptical stress arch theory, to better account for high ground temperatures in the overlying strata. The theoretical calculations predicted a water-conducting fracture zone height of 61.32 m and a fracture zone height of 21.25 m. The development of the fracture zone exhibited a three-stage evolution: a slow development stage, followed by a rapid expansion stage, and finally a stable penetration stage. The findings suggest that the fracture zone height is primarily governed by the fracturing of key strata within an ellipsoidal stress arch, with overburden failure influenced by mining-induced stress concentration and the structural characteristics of the overlying rock. These results provide both theoretical insights and empirical data for improving predictions of water hazards and enhancing the stability of overburden in deep mining environments.

This paper presents GRATE–DRL–AI, an Artificial Intelligence (AI)–driven algorithm designed to enhance the efficiency and accuracy of distribution system planning. Leveraging advanced AI methodologies, including graph learning, transfer learning, deep reinforcement learning (DRL), and physics-guided neural networks, this model efficiently addresses the growing complexity and uncertainties in modern distribution grids with high penetration of distributed energy resources. Case studies on the Institute of Electrical and Electronics Engineers 33-bus and 123-bus systems show that GRATE–DRL–AI reduces planning cost by up to 8.5%, achieves 99%–100% feasibility, and significantly lowers computation time (e.g., 580 s vs. 1610 s for the 342-bus system). Even under ±30% uncertainty in demand and renewable generation, feasibility remains above 99%. In addition to strong performance gains, the study also highlights limitations, such as data availability, computational requirements, and regulatory considerations, which must be addressed for real-world deployment of AI-driven planning frameworks.