Search Results (17,346)

Efficient management of heat transfer and entropy generation in nanofluid enclosures is essential for the development of high-performance thermal systems. This study employs the finite element method (FEM) to numerically analyze the effects of wall corrugation and base inclination on magnetohydrodynamic (MHD)-assisted natural convection of Cu–H₂O nanofluid in a trapezoidal cavity containing internal heat-generating obstacles. The governing equations for fluid flow, heat transfer, and entropy generation are solved for a wide range of Rayleigh numbers (10³–10⁶), Hartmann numbers (0–50), and geometric configurations. Results show that for square obstacles, the Nusselt number increases from 0.8417 to 0.8457 as the corrugation amplitude rises (a = 0.025 L–0.065 L) at Ra = 10³, while the maximum heat transfer (Nu = 6.46) occurs at Ra = 10⁶. Entropy generation slightly increases with amplitude (15.46–15.53) but decreases under stronger magnetic fields due to Lorentz damping. Higher corrugation frequencies (f = 9.5) further enhance convection, producing Nu ≈ 6.44–6.47 for square and triangular obstacles. Base inclination significantly influences performance: γ = 10° yields maximum heat transfer (Nu ≈ 6.76), while γ = 20° minimizes entropy (St ≈ 0.00139). These findings confirm that optimized corrugation and inclination, particularly with square obstacles, can effectively enhance convective transport while minimizing irreversibility, providing practical insights for the design of energy-efficient MHD-assisted heat exchangers and cooling systems. Full article

Ammonia is a highly promising zero-carbon fuel for engines. However, it exhibits high ignition energy, slow flame propagation, and severe pollutant emissions, so it is usually burned in combination with highly reactive fuels such as hydrogen. An accurate understanding and modeling of ammonia–hydrogen combustion is of fundamental and practical significance to its application. Deep Neural Networks (DNNs) demonstrate significant potential in autonomously learning the interactions between high-dimensional inputs. This study proposed a deep neural network-based method for optimizing chemical reaction mechanism parameters, producing an optimized mechanism file as the final output. The novelty lies in two aspects: first, it systematically compares three DNN structures (Multi-layer perceptron (MLP), Convolutional Neural Network, and Residual Regression Neural Network (ResNet)) with other machine learning models (generalized linear regression (GLR), support vector machine (SVM), random forest (RF)) to identify the most effective structure for mapping combustion-related variables; second, it develops a ResNet-based surrogate model for ammonia–hydrogen mechanism optimization. For the test set (20% of the total dataset), the ResNet outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R²) of 0.9923 and root mean square error (RMSE) of 135. The surrogate model uses the trained ResNet to optimize mechanism parameters based on a Stagni mechanism by mapping the initial conditions to experimental IDT. The results show that the optimized mechanism improves the prediction accuracy on laminar flame speed (LFS) by approximately 36.6% compared to the original mechanism. This method, while initially applied to the optimization of an ammonia–hydrogen combustion mechanism, can potentially be adapted to optimize mechanisms for other fuels. Full article

This study presents a comprehensive review of the CLH index, a predictive tool developed to estimate the wear of tunnel boring machine (TBM) disc cutters operating in hard rock conditions. The CLH index provides a simplified, time-efficient, and cost-effective alternative to conventional wear prediction methods by employing a statistically derived empirical formula. The methodology is based on the identification and quantitative assessment of key rock properties that influence cutter wear. A detailed statistical analysis was conducted to validate the index, quantify potential errors, and determine confidence levels. As part of this review, updated reference tables are proposed to facilitate cutter wear estimation without the need for preliminary laboratory testing. These tables are derived from empirical data obtained at the Rock Mechanics Laboratory of the Higher Technical School of Mining and Energy Engineers (ETSIME-UPM), using operational records from TBM excavation in multiple Spanish high-speed railway tunnels, with a total length exceeding 120 km. The results confirm the reliability and practical applicability of the CLH index as a decision-support tool in TBM performance forecasting and maintenance planning. Full article

Thailand’s mango export industry faces significant challenges in meeting stringent international quality standards, particularly the costly phytosanitary X-ray irradiation process. Current fixed-dose irradiation methods result in substantial energy waste due to variations in fruit size. This research presents a low-cost, real-time system that integrates computer vision and artificial intelligence (AI) to optimize this process. By capturing a single top-view 2D image, the system accurately estimates the three-dimensional characteristics (width, height, and depth) of ‘Nam Dok Mai Si Thong’ mangoes. This dimensional data is crucial for dynamically adjusting the radiation dose for each fruit, leading to significant reductions in energy consumption and operational costs. Our novel approach utilizes a Linear Regression combined with Co-Kriging (LR + CoK) model to precisely estimate fruit depth from 2D data, a key limitation in previous studies. The system demonstrated high efficacy, achieving a dimensional estimation error (RMSE) of less than 0.46 cm and a size grading accuracy of up to 93.33 percent. This technology not only enhances sorting and grading efficiency but also offers a practical solution to lower the economic and energy burden of phytosanitary treatments, directly improving the sustainability of fruit export operations. Full article

With growing global demand for sustainable decarbonization, hydrogen energy systems have emerged as a key pillar in achieving carbon neutrality. This study assesses the greenhouse gas (GHG) reduction efficiency of Republic of Korea’s hydrogen ecosystem from a life-cycle perspective, focusing on production and utilization stages. Using empirical data—including the national hydrogen supply structure, fuel cell electric vehicle (FCEV) deployment, and hydrogen power generation records, the analysis compares hydrogen-based systems with conventional fossil fuel systems. Results show that current hydrogen production methods, mainly by-product and reforming-based hydrogen, emit an average of 6.31 kg CO₂-eq per kg H₂, providing modest GHG benefits over low-carbon fossil fuels but enabling up to a 77% reduction when replacing high-emission sources like anthracite. In the utilization phase, grey hydrogen-fueled stationary fuel cells emit more GHGs than the national grid. By contrast, FCEVs demonstrate a 58.2% GHG reduction compared to internal combustion vehicles, with regional variability. Importantly, this study omits the distribution phase (storage and transport) due to data heterogeneity and a lack of reliable datasets, which limits the comprehensiveness of the LCA. Future research should incorporate sensitivity or scenario-based analyses such as comparisons between pipeline transport and liquefied hydrogen transport to better capture distribution-phase impacts. The study concludes that the environmental benefit of hydrogen systems is highly dependent on production pathways, end-use sectors, and regional conditions. Strategic deployment of green hydrogen, regional optimization, and the explicit integration of distribution and storage in future assessments are essential to enhancing hydrogen’s contribution to national carbon neutrality goals. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

Lithium-ion batteries have been extensively utilized as a high-power, rechargeable, and dischargeable energy storage medium. Accurate estimation of the battery state of charge (SOC) in the battery management system (BMS) is imperative for ensuring the safe and stable operation of electric vehicles. This paper proposes an SOC estimation method based on the equivalent circuit model as well as the ampere-time integration method with a physical informed neural network. The network enhances the estimation of SOC by introducing two mechanistic information sources: the equivalent circuit model (ECM) and the ampere-time integration method (Ah-I method). These are utilized as a priori knowledge to constrain the estimation of SOC. Initially, the Rint model is selected as the physical analysis model of the lithium-ion battery, and subsequently, the Ah-I method is chosen as the auxiliary model for SOC output estimation. A deep learning network is then employed to establish the mapping between the battery input parameters and the SOC output. Finally, the SOC is estimated by fusing the physical model and the data-driven model. The results demonstrate the efficacy of the method in accurately estimating the state of charge of lithium batteries, with a root mean square error within 1%. The validity of the research methodology was further validated through comparison with other approaches. Full article

Against the backdrop of the rapid development of the energy internet, the role of energy storage systems in grid stability, energy balance, and renewable energy integration has become increasingly important. Among these systems, estimating the state of health (SOH) of battery storage systems, particularly lithium batteries, is crucial for ensuring system reliability and safety. While data-driven methods have poor interpretability and physics-based models are computationally expensive, physics-informed neural networks (PINNs) offer a compromise but struggle with high-dimensional inputs and dynamic variable coupling. This paper proposed a novel Kolmogorov–Arnold networks with physics-informed neural network (KAN-PINN) framework for lithium-ion battery SOH estimation. By leveraging KANs’ superior high-dimensional approximation capabilities and embedding the Verhulst model as a physical constraint, the framework enhances nonlinear representation while ensuring predictions adhere to degradation physics. Experimental results on a public dataset demonstrate the model’s superiority, achieving an RMSPE of 0.300 and MAE of 1.342%, along with strong interpretability and robustness across battery chemistries and operating conditions. Full article

Sustainable mine geothermal energy causes high-temperature hazards in mine roadways, severely endangering miners’ lives. There is an urgent need to enhance research on the performance of composite phase change material (CPCM) roadway cooling systems, as they can effectively control ambient temperatures. However, existing research on CPCM roadway cooling system performance remains limited. This study innovatively establishes a numerical model for a novel cascade CPCM roadway cooling system and employs the control variable method to investigate the influence of multi-parameter regulation on system performance. The study reveals that the ring pipe radius ratio significantly impacts the system’s heat exchange efficiency and temperature distribution. The optimal comprehensive system performance is achieved at an annular tube radius ratio of 2:3, where the CPCM solid phase percentage for 89.03% and the average temperature of the monitoring surface decreases by 9.54 °C. Increasing the cascaded tube spacing enhances the overall cooling effect, but cooling efficiency diminishes when the spacing exceeds 0.5 m. The CPCM phase change temperature must align with the mine’s geothermal conditions, with CPCM utilization and cooling efficiency peaking at 25 °C. The air deflector structure effectively mitigates cooling lag in the lower roadway section. At an installation angle of 30°, the expansion distance of the lower low-temperature zone increased by up to 48.89% without compromising cooling efficiency in the upper roadway section, while also delaying the recovery rate of heat damage. Full article

Reservoirs in the Orinoco Heavy Oil Belt, Venezuela, typically hold natural foamy oil. Gas liberation during depletion leads to a sharp increase in viscosity, adversely impacting development efficiency. Therefore, this paper proposes a natural gas (CH₄)–chemical synergistic huff-and-puff method (CCHP). It utilizes the synergism between a stable foam plugging system and natural gas to supplement reservoir energy and promote the generation of secondary foamy oil. To evaluate the performance of 20 types of foam stabilizers (polymers and surfactants), elucidate the influence on production and properties of key parameters, and reveal the flow characteristics of produced fluids, 24 sets of foam performance evaluation tests were conducted using a high-temperature foam instrument. Moreover, 15 sets of core experiments with production fluid visualization were performed. The results demonstrate that, in terms of individual components, XTG and HPAM-20M demonstrated the best foam-stabilizing performance, achieving an initial foam volume of 280 mL and a foam half-life of 48 h. Conversely, the polymer–surfactant composite of XTG-CBM-DA elevated the initial foam volume to 330 mL while maintaining a comparable half-life, further enhancing the performance of foaming capacity for a stable foam system. For further application in the CCHP, oil production shows a positive correlation with both post-depletion pressure and chemical agent concentration; however, the foam gas–liquid ratio (GLR) exhibits an inflection point, with the optimal ratio found to be 1.2 m³/m³. During the huff-and-puff process, the density and viscosity of the produced oil decrease cycle by cycle, while resin and asphaltene content show a significant reduction. Furthermore, visualization results reveal that the foam becomes finer, more stable, and more uniformly distributed under precise parameter control, leading to enhanced foamy oil effects and improved plugging capacity. Moreover, the foam structure transitions from an oil-rich state to a homogeneous and stable configuration throughout the CCHP process. This study provides valuable insights for achieving stable and sustainable development in natural foamy oil reservoirs. Full article

To address the challenges of high-dimensional complexity and increasing heterogeneity in expert opinions, this study proposes a novel numerical aggregation model for multi-expert decision making based on triangular intuitionistic fuzzy numbers (TIFNs) and the Plant Growth Simulation Algorithm (PGSA). The proposed framework transforms experts’ fuzzy preference information into five-dimensional geometric vectors and employs the PGSA to perform global optimization, thereby yielding an optimized collective decision matrix. To comprehensively evaluate the aggregation performance, several quantitative indicators—such as weighted Hamming distance, correlation sum, information intuition energy, and weighted correlation coefficient—are introduced to assess the results from the perspectives of consensus, stability, and informational strength. Extensive numerical experiments and comparative analyses demonstrate that the proposed method significantly improves expert consensus reliability and aggregation robustness, achieving higher decision accuracy than conventional approaches. This framework provides a scalable and generalizable tool for high-dimensional fuzzy group decision making, offering promising potential for complex real-world applications. Full article

This paper presents a novel hybrid modulation technique for Asymmetrical Cascaded H-Bridge Multilevel Inverters (ACHBMLIs), specifically designed to enhance both efficiency and harmonic performance. Unlike conventional strategies, the proposed method optimizes the switching scheme by operating the high-voltage H-Bridge at the fundamental frequency, thereby significantly reducing switching losses while maintaining low harmonic distortion levels comparable to traditional Pulse Width Modulation (PWM). To assess the effectiveness of the approach, a comprehensive comparison was conducted against two widely adopted modulation techniques for ACHBMLIs: Multicarrier Pulse Width Modulation (MPWM) and the Staircase Modulation Strategy (SMS). The evaluation involved both simulation and real-time Hardware-in-the-Loop (HIL) testing of a 7-level three-phase ACHBMLI, with a focus on key performance indicators such as voltage and current harmonic distortion, as well as converter efficiency. The results demonstrate that the proposed hybrid modulation achieves higher efficiency than PWM and lower current Total Harmonic Distortion (THD) than SMS. These findings highlight the potential of the hybrid strategy as a compelling solution for applications that demand an optimal balance between energy efficiency and waveform quality. Full article

Background: Degenerin proteins, such as Acid-Sensing Ion Channel 2 (ASIC2) and β Epithelial Na⁺ Channel (βENaC), have been implicated in cardiovascular function. We previously demonstrated that mice lacking normal levels of βENaC and ASIC2 are protected from diet-induced obesity, metabolic disruption, and hepatic steatosis. Methods: To investigate the specific role of βENaC proteins in the progression of metabolic disease, we examined the impact of a high-fat diet (HFD) in the βENaC hypomorph mouse model (βMUT). Body composition and metabolic and behavioral phenotypes were examined in male and female and βMUT and WT mice (n = 6–14/group) fed a normal chow diet (NFD) from weaning until 16 weeks of age, then a 60% kcal-fat diet for 5 weeks. Results: Compared to WT mice, βMUT male mice have reduced lean and total body mass. No remarkable differences in energy expenditure, motor activity, or food consumption patterns were detected. HFD-fed male βMUT mice exhibited reduced liver fat content (mass and Oil Red O staining) yet increased abdominal fat depots. HFD-fed female βMUT mice exhibited lower heart mass. Conclusions: These novel findings suggest a role for βENaC in the maintenance of metabolic homeostasis and adipose tissue distribution. Full article

To handle the uncertainties brought by the increasing penetration of renewable energy sources and random loads, we design a stochastic multi-timescale distribution network reconfiguration (SMTDNR) framework to coordinate diverse scheduling resources across different timescales and develop an efficient heuristic algorithm to solve this complex NP-hard combinatorial optimization problem with high efficiency for medium- and high-voltage distribution networks. First, the SMTDNR problem, incorporating distributed renewable generators, fuel generators, energy storage systems, and controllable loads, is simplified through circular constraint linearization, Jabr relaxation, and second-order cone (SOC) relaxation techniques. Then, a one-stage multi-timescale successive branch reduction (MTSBR) algorithm is developed for distribution networks with one redundant branch, which transforms the SMTDNR problem into a stochastic multi-timescale optimal power flow (SMTOPF) problem. This is extended to a two-stage MTSBR algorithm for general networks with multiple redundant branches, which iteratively runs the proposed one-stage MTSBR algorithm. Numerical results on modified IEEE 33-bus and 123-bus distribution networks validate the superior optimality, feasibility, and computational efficiency of the proposed algorithms, particularly in scenarios of high renewable penetration and increased uncertainty, offering robust and feasible solutions where traditional methods may fail. Full article

Photo(electro)-catalysis has increasingly attracted attention from researchers due to its wide applications in green chemical transformation, including organic synthesis and environmental remediation. As a promising candidate, the n-type semiconductor WO₃ possesses a suitable bandgap (~2.6 eV), good visible-light response, high chemical stability, and multi-electron transfer capability, thus endowing it with enormous potential in heterogeneous photocatalysis (PC) and photoelectrocatalysis (PEC) to address environment and energy issues. In this review, the recent research progress of WO₃-based photo(electro)-catalysts is examined and systematically summarized with regard to construction strategies and various application scenarios. To start with, the research background, functionalization methods and possible reaction mechanisms for WO₃ are introduced in depth. Key influencing factors, including light absorption capacity, charge carrier separation, and reusability, are also analyzed. Then, diverse applications of WO₃ for the elimination of organic pollutants (e.g., persistent organic pollutants and polymeric wastes) and green organic synthesis (i.e., oxidation, reduction, and other reactions) are intentionally discussed to underscore their vast potential in photo(electro)-catalytic performance. Finally, future challenges and insightful perspectives are proposed to explore effective WO₃-based materials. This comprehensive review aims to offer profound insights into innovative exploration of high-performance WO₃ semiconductor catalysts and guide new researchers in this field to better understand their vital roles in green organic synthesis and hazardous pollutants removal. Full article