Search Results (6,140)

With the acceleration of urbanization, environmental degradation is increasingly restricting the improvement of residents’ quality of life, and promoting the transformation of old communities has become a key path for sustainable urban development. However, existing buildings generally face challenges, such as the deterioration of the performance of the envelope structure and the rising energy consumption of the air conditioning system, which pose a serious test for the realization of green renovation. Inspired by the application of bionics in the field of architecture, this study innovatively designed five types of bionic envelope structures for outdoor air conditioning units, namely scales, honeycombs, spider webs, leaves, and bird nests, based on the aerodynamic characteristics of biological prototypes. The ventilation performance of these structures was evaluated at three scales—namely, single building, townhouse, and community—under natural ventilation conditions, using a CFD simulation system. The study shows the following: (1) the spider web structure has the best comprehensive performance among all types of enclosures, which can significantly improve the uniformity of the flow field and effectively eliminate the low-speed stagnation area on the windward side; (2) the structure reorganizes the flow structure of the near-wall area through the cutting and diversion of the porous grid, reduces the wake range, and weakens the negative pressure intensity, making the pressure distribution around the building more balanced; (3) in the height range of 1.5–27 m, the spider web structure performs particularly well at the townhouse and community scales, with an average wind speed increase of 1.1–1.4%; and (4) the design takes into account both the safety of the enclosure and the comfort of the pedestrian area, achieving a synergistic optimization of function and performance. This study provides new ideas for the micro-renewal of buildings, based on bionic principles, and has theoretical and practical value for improving the wind environment quality of old communities and promoting low-carbon urban development. Full article

This study systematically investigates the velocity characteristics and coupling mechanisms of tunnel flow fields under the interactions of natural wind, traffic wind, mechanical ventilation, and structural factors (such as transverse passages and relative positions between vehicles and fans). Using CFD simulations combined with turbulence model analyses, the flow behaviors under different coupling scenarios are explored. The results show that: (1) Under natural wind conditions, transverse passages act as key pressure boundaries, reshaping the longitudinal wind speed distribution into a segmented structure of “disturbance zones (near passages) and stable zones (mid-regions)”, with disturbances near passages showing “amplitude enhancement and range contraction” as natural wind speed increases. (2) The coupling of natural wind and traffic wind (induced by moving vehicles) generates complex turbulent structures; vehicle motion forms typical flow patterns including stagnation zones, high-speed bypass flows, and wake vortices, while natural wind modulates the wake structure through momentum exchange, affecting pollutant dispersion. (3) When natural wind, traffic wind, and mechanical ventilation are coupled, the flow field is dominated by momentum superposition and competition; adjusting fan output can regulate coupling ranges and turbulence intensity, balancing energy efficiency and safety. (4) The relative positions of vehicles and fans significantly affect flow stability: forward positioning leads to synergistic momentum superposition with high stability, while reverse positioning induces strong turbulence, compressing jet effectiveness and increasing energy dissipation. This study reveals the intrinsic laws of tunnel flow field evolution under multi-factor coupling, providing theoretical support for optimizing tunnel ventilation system design and dynamic operation strategies. Full article

This study investigates hydrogen storage enhancement through adsorption in porous materials by coupling the Dubinin–Astakhov (D-A) adsorption model with H₂ conservation equations (mass, momentum, and energy). The resulting system of partial differential equations (PDEs) was solved numerically using the finite element method (FEM). Experimental work using activated carbon as an adsorbent was carried out to validate the model. The comparison showed good agreement in terms of temperature distribution, average pressure of the system, and the amount of adsorbed hydrogen (H₂). Further simulations with different adsorbents indicated that compact metal–organic framework 5 (MOF-5) is the most effective material in terms of H₂ adsorption. Additionally, the pair (273 K, 800 s) remains the optimal combination of injection temperature and time. The findings underscore the prospective advantages of optimized MOF-5-based systems for enhanced hydrogen storage. These systems offer increased capacity and safety compared to traditional adsorbents. Subsequent research should investigate multi-objective optimization of material properties and system geometry, along with evaluating dynamic cycling performance in practical operating conditions. Additionally, experimental validation on MOF-5-based storage prototypes would further reinforce the model’s predictive capabilities for industrial applications. Full article

Efficient coalbed methane (CBM) recovery combined with carbon dioxide (CO₂) sequestration is a promising strategy for sustainable energy production and greenhouse gas mitigation. However, the molecular mechanisms controlling pressure-dependent CH₄ displacement by CO₂ in coal nanopores remain insufficiently understood. In this study, molecular dynamics simulations were conducted to investigate CO₂-driven CH₄ recovery in a slit-pore coal model under driving pressures of 15, 20, and 25 Mpa. The simulations quantitatively captured the competitive adsorption, diffusion, and migration behaviors of CH₄, CO₂, and water, providing insights into how pressure influences enhanced coalbed methane (ECBM) recovery at the nanoscale. The results show that as the pressure increases from 15 to 25 Mpa, the mean residence time of CH₄ on the coal surface decreases from 0.0104 ns to 0.0087 ns (a 16% reduction), reflecting accelerated molecular mobility. The CH₄–CO₂ radial distribution function peak height rises from 2.20 to 3.67, indicating strengthened competitive adsorption and interaction between the two gases. Correspondingly, the number of CO₂ molecules entering the CH₄ region grows from 214 to 268, demonstrating higher invasion efficiency at elevated pressures. These quantitative findings illustrate a clear shift from capillary-controlled desorption at low pressure to pressure-driven convection at higher pressures. The results provide molecular-level evidence for optimizing CO₂ injection pressure to improve CBM recovery efficiency and CO₂ storage capacity. Full article

The shell liner is a core component of Semi-Autogenous Grinding (SAG) mills, suffering severe wear from ore impact and friction, and its shape directly affects grinding efficiency and maintenance costs. In this study, the Finnie wear model in EDEM2022 software was improved to predict the wear morphology evolution of shell liners. A Python-based coupled simulation of the Discrete Element Method (DEM, EDEM) and Finite Element Method (FEM, ABAQUS) was established to analyze liner wear mechanisms, stress states, and mill service performance (wear resistance, grinding efficiency, and stress distribution). The simulated wear profile showed high consistency with laser three-dimensional scanning (LTDS) results, confirming the improved Finnie-DEM model’s effectiveness in reproducing liner wear evolution. Shearing in crushing/grinding zones was the main wear cause, with additional contributions from relative sliding among ore, grinding balls, and liners in grinding/discharge zones. DEM-FEM coupling revealed two circumferential instantaneous wear extremes (Max^a > Max^b) and two lifter wear rate peaks (M^a > M^b). In the grinding zone, liner stress distribution matched wear distribution, with maximum instantaneous stress at characteristic points A and B—stress at A reflects liner impact degree, while stress at B indicates mill ore-crushing capacity. Optimizing flat liner shape adjusted wear rate peaks (M^a, M^b), improving overall liner wear. This optimization significantly affected stresses at A/B and ore normal collision but had little impact on mill energy efficiency. Full article

This study introduces a parametric multi-objective optimization framework for urban block morphology. It integrates micro-climate data corrected by the Urban Weather Generator (UWG), energy simulation through EnergyPlus and Honeybee, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within the Wallacei platform. Using Wuhan, China, a city with a representative hot-summer and cold-winter climate, as a case study, the framework simultaneously optimizes three key objectives: Average Sunshine Hours (Av.SH), Energy Use Intensity (EUI), and Average Universal Thermal Climate Index (Av.UTCI). The framework systematically links parametric modeling, environmental simulation, and evolutionary optimization to explore how block typologies and height configurations affect the trade-offs among solar access, energy demand, and outdoor thermal comfort. Among the feasible solutions, Av.SH exhibits the greatest variation, ranging from 4.30 to 7.93 h, followed by Av.UTCI (44.13 to 45.46 °C), while EUI shows the least fluctuation, from 91.69 to 93.36 kWh/m². Key design variables, such as building type and height distribution, critically influence the outcomes. Optimal configurations are achieved by interweaving low-rise (2 to 3 floors), mid-rise (6 to 8 floors), and high-rise (15 to 20 floors) buildings to enhance openness and ventilation. The proposed framework offers a quantifiable strategy for guiding future climate-responsive and energy-efficient neighborhood design. Full article

This paper presents a method to design and optimize a mimetic, multi-band antenna for direction-finding applications based on multiple IoT mobile nodes for protecting sensitive areas. A set of 84 antenna configurations were selected based on possible resonant paths and simulated using a Method of Moments (MoM)-based tool to compute resonant frequencies, VSWR, and gain across three frequency bands centered on 433 MHz, 877.5 MHz, and 2.4 GHz. Compared to a brute-force approach requiring 8¹⁴ full-wave simulations, our technique dramatically reduces computing time by performing only 84 simulations, followed by a fine-tuning procedure targeting the antenna segments with the highest contribution to the error figure. The final design provides good gain and VSWR figures over almost all the frequency ranges of interest. Full article

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints. Full article

Phase-change materials (PCMs) are integral to the thermal energy storage devices used in phase-change storage air-conditioning systems, but their adoption is hindered by slow heat transfer rates and suboptimal energy storage efficiency. In this study, we design and analyze a flat-panel thermal energy storage device based on PCM, using both numerical simulations and experimental testing to evaluate performance under various operating conditions. The simulations, conducted using computational fluid dynamics (CFD) in a steady-state environment with an inlet temperature of 12 °C, demonstrate that the phase-change completion time for cooling storage is 8331 s, while the cooling release process is completed in 3883 s. The fluid distribution within the device is found to be uniform, and the positioning of the inlet and outlet has a minimal effect on performance metrics. However, the lateral stacking configuration of PCM units significantly improves heat transfer efficiency, increasing it by 15% compared to vertical stacking arrangements. Experimental tests confirm that increasing the inlet flow rate accelerates the phase transition process but has a marginal impact on overall energy utilization efficiency. These results provide valuable quantitative insights into optimizing the design of phase-change thermal storage devices, particularly in terms of enhancing heat transfer and overall energy efficiency. Full article

To address the limitations of traditional hospital energy management strategies in responding to real-time medical demands, this study proposes a coordinated optimization approach for multi-timescale scheduling in diversified hospital energy systems. The long-term scheduling problem is first formulated as a Markov Decision Process, with fine-grained short-term energy supply plans embedded in each decision step through an optimal model. Deep reinforcement learning is then employed to reduce the dimensionality of long-term decision variables, while hybrid integer linear programming is integrated to strictly enforce critical load operation constraints. A hybrid data- and model-driven framework is constructed to simultaneously enhance computational efficiency and power supply reliability. Empirical studies demonstrate that, compared with traditional scenario-based and robust optimization methods, the proposed approach significantly improves energy resource utilization—raising the distributed renewable energy utilization rate from 82.45% to 96.72%—and reduces the power interruption rate for critical loads from 2.8% to 0.15%. This ensures the continuity of medical services while minimizing energy waste. The proposed method provides both theoretical and practical guidance for intelligent scheduling and energy management in complex hospital integrated energy systems. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Integrated energy systems (IESs) are increasingly being deployed and expanded, which integrate various energy infrastructures to enable flexible conversion and utilization among different energy forms. To facilitate collaboration among operators of varying scales and fully leverage the economic and environmental benefits of multi-integrated energy systems (MIESs), this study develops a peer-to-peer (P2P) energy sharing framework for MIES based on asymmetric Nash bargaining. First, an IoT-based P2P energy sharing architecture for MIES is proposed, which incorporates coordinated electricity–heat–gas multi-energy synergy within IES models. Carbon capture systems (CCS) and power-to-gas (P2G) units are integrated with carbon trading mechanisms to reduce carbon emissions. Then, an MIES energy sharing operational model is established using Nash bargaining theory, subsequently decoupled into two subproblems: alliance benefit maximization and individual IES benefit distribution optimization. For subproblem 2, an asymmetric bargaining method employing natural exponential functions quantifies participant contributions, enabling fair distribution of cooperative benefits. Finally, the alternating direction method of multipliers (ADMM) is employed to solve both subproblems distributively, effectively preserving participant privacy. The effectiveness of the proposed method is verified by case simulation, demonstrating reduced operational costs across all IESs alongside equitable benefit allocation proportional to energy-sharing contributions. Carbon emission amounts are simultaneously reduced. Full article

In order to meet the dual requirements of low-energy heating and flexible operation, a comprehensive heating system with multi-mode and wide-load capabilities was constructed, incorporating a heat pump, a back-pressure turbine, and two 350 MW coal-fired condensing units. Based on the heat transfer characteristics of this system, the simulation model of this comprehensive thermal system was constructed through a commercial software (EBSILON). A dynamic coal consumption prediction method based on the non-equilibrium state parameters was first proposed, which was primarily designed for system operation optimization. Subsequently, the converted load and load change rate were integrated into the dynamic correction model to refine prediction accuracy. The results showed that while basic coal consumption primarily correlates with heat load and electricity load, dynamic coal consumption is influenced by both the converted load and the load change rate. Based on this, the three-dimensional surface plot of converted load, load charge rate, and dynamic coal consumption offset coefficient was calculated. Then, the accuracy of the prediction model was verified by the variable working condition parameter group, and its reliability was confirmed. Further, by developing online software, theoretical guidance for industrial production was realized. In a heating season case study, it was demonstrated the prediction method can effectively reflect the dynamic parameter deviation in the system, with the annual coal saving being able to reach 841.5 tons. It is expected to provide theoretical guidance for the research on multi-heat sources heating distribution and operation parameter optimization. Full article

Renewable energy sources (RESs) such as photovoltaic (PV) and fuel cells (FCs) introduce variability that complicates reliable, loss-aware operation of distributed power-flow systems (DPFSs) in smart homes. Frequent charge/discharge cycling of energy storage systems (ESSs) can inflate losses and jeopardize user comfort when generation and demand are mismatched. This paper addresses the gap in multi-load, multi-source coordination under fluctuating RESs by proposing a Multiple-Load Power-Flow Assignment (MPFA) framework that explicitly minimizes storage-related losses while maintaining demand satisfaction. We evaluate four logical interconnection scenarios among generators (PGs), loads (PLs), and storage (PSs), and compare three control algorithms—total-demand-based (TDPF), adaptive-demand-based (ADPF), and grid-based (GBPF). Using measured PV/FC data across seasons, MPFA consistently reduces storage-related losses as interconnections increase, with GBPF guaranteeing full daily demand satisfaction by flexibly supplementing local generation with grid power. ADPF performs strongly when grid support is limited by prioritizing critical loads and optimizing storage utilization. The results provide actionable guidance for designing smart-home energy management that emphasizes sustainability, reliability, and user comfort. Full article

Vertical-Axis Wind Turbines (VAWTs) are efficient solutions for renewable energy generation, especially in regions with variable wind conditions. This study presents an optimized design of a small-scale H-type VAWT through the integration of Design of Experiments (DOE) and Computational Fluid Dynamics (CFD), using a fractional factorial 2^k−p approach to evaluate the influence of geometric and operational parameters on power output and power coefficient (Cp), which ranged from 0.15 to 0.35. The research began with a comprehensive assessment of renewable resources in Isla Fuerte, Colombia. Solar analysis revealed an average of 5.13 Peak Sun Hours (PSHs), supporting the existing 175 kWp photovoltaic system. Wind modeling, based on meteorological data and Weibull distribution, showed speeds between 2.79 m/s and 5.36 m/s, predominantly from northeast to northwest. Under these conditions, the NACA S1046 airfoil was selected for its aerodynamic suitability. The turbine achieved power outputs from 0.46 W to 37.59 W, with stabilization times analyzed to assess dynamic performance. This initiative promotes environmental sustainability by reducing reliance on Diesel Generators (DGs) and empowering local communities through participatory design and technical training. The DOE-CFD methodology offers a replicable model for energy transition in insular regions of developing countries, linking technical innovation with social development and education. Full article