Search Results (14,364)

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. Results highlight a strong emphasis on energy efficiency, with China leading due to its market size, industrial base, and supportive policies. Major research directions tied to range extension include energy storage, motion control, thermal regulation, cooperative driving, and grid interaction. Among these, hybrid energy storage systems and motor control stand out for their measurable impact and industrial relevance, while thermal management, regenerative braking, and systemic approaches (V2V and V2G) remain underexplored. Beyond mapping contributions, the study identifies ongoing gaps and calls for integrated strategies that combine electrical, thermal, and mechanical aspects. As EV adoption accelerates and battery demand increases, the findings emphasize the need for battery-aware, multi-objective energy management strategies. This synthesis provides a vital framework to guide future research and support the development of robust, integrated, and industry-ready solutions for optimizing EV energy use and extending driving range. Full article

As trade in services continues to play an increasingly important role in international trade, effectively integrating its advancement with green development has become a key issue for China in shaping a new development paradigm. This study treats the service trade pilot city policy as a quasi-natural experiment, employing the Difference-in-Differences (DID) method to investigate the policy’s impact on urban green energy use. The findings indicate that the policy significantly boosted green energy consumption in pilot cities. Heterogeneity analysis reveals more pronounced policy effects in eastern regions and provinces with smaller populations. Furthermore, synergistic effects emerge when this policy interacts with artificial intelligence (AI) technology policies and urban environmental policies, further amplifying green energy consumption outcomes. Consequently, this study proposes recommendations including strengthening institutional innovation in green services trade within pilot zones, establishing cross-regional green collaboration networks, and promoting multi-policy coordination. These findings offer valuable insights for developing countries seeking to achieve sustainable development through services trade liberalization. Full article

The aim of the study is to investigate the influence of the physicochemical characteristics of the molecular and supramolecular structure of polymers on electroadhesive interactions and their change under the action of a constant electric field. Currently, this effect is modeled in electroadhesion studies, but the range of variable parameters is limited and includes permittivity, moisture content, and surface roughness. It is important to consider other physicochemical parameters, such as material crystallinity and surface characteristics, changes in which can affect the magnitude of electroadhesive forces. In this study, the electric field strength was varied by altering the constant voltage in the range of 3–8 kV. Polyethylene, ethylene-vinyl acetate copolymers, and polyvinyl acetate were used as substrates for adhesive systems. The influence of the concentration of vinyl acetate groups, which determine the energy characteristics of the surface, and the degree of crystallinity on electroadhesive interactions under conditions of an external constant electric field and without it was traced. The degree of crystallinity was varied both by the cooling rate and the orientation during drawing. It was shown that by changing the polar component of the surface energy and the proportion of the crystalline phase in the substrate, electroadhesive interactions can be increased by 4 times to 120 Pa compared to polyethylene. The obtained laws are explained by the local dipoles induced by polar functional groups, which enhance the polymer’s surface interactions with other materials and external fields. At the same time, the fixation of macromolecules in crystalline regions complicates polarization under the influence of an electric field. Full article

As a major energy producer and consumer, China has witnessed rapid growth in carbon emissions, which are closely linked to changes in regional climate and the environment. Water, energy, and food (W-E-F) are the three most critical components of human production and daily life, and achieving the coordinated development of these three resources and connecting them with climate change through the carbon emissions generated during their utilization processes has become a key issue for realizing regional ecological sustainable development. This study constructs a dynamic two-stage network slack-based measure-data envelopment analysis (SBM-DEA) model, which integrates the water–energy–food (W-E-F) system with the climate change process to evaluate China’s comprehensive ecological efficiency from 2011 to 2022, and adopts the Dagum Gini coefficient decomposition, kernel density estimation, hierarchical clustering, and geographical detector model to analyze provincial panel data, thereby assessing efficiency patterns, regional differences, and driving mechanisms. The novelty and contributions of this study can be summarized in three aspects. First, it establishes a unified framework that incorporates the W-E-F nexus and climate change into a dynamic network SBM-DEA model, enabling a more systematic assessment of ecological efficiency. Second, it uncovers that interregional overlap effects and policy-driven factors are the dominant sources of spatial and temporal disparities in ecological efficiency. Third, it further quantifies the interactive effects among key driving factors using Geodetector, thus offering practical insights for regional coordination and policy design. The results show that China’s national ecological efficiency is at a medium level. Southern China has consistently maintained a leading position, while provinces in northwest and southwest China have remained relatively backward; the efficiency of the water–energy–food integration stage is relatively high, whereas the efficiency of the climate change stage is medium and exhibits significant temporal fluctuations. Interregional differences are the main source of efficiency gaps; ecological quality, environmental protection efforts, and population size are identified as the primary driving factors, and their interaction effects have intensified spatial heterogeneity. In addition, sub-indicator analysis reveals that the efficiency related to total wastewater, air pollutant emissions, and agricultural pollution shows good synergy, while the efficiency associated with sudden environmental change events is highly volatile and has weak correlations with other undesirable outputs. These findings deepen the understanding of the water–energy–food-climate system and provide policy implications for strengthening ecological governance and regional coordination. Full article

Under the background of “dual-carbon”, the development of energy internet is an inevitable trend for China’s low-carbon energy transition. This paper proposes a hydrogen-coupled electrothermal integrated energy system (HCEH-IES) operation mode and optimizes the source-side structure of the system from the level of carbon trading policy combined with low-carbon technology, taps the carbon reduction potential, and improves the renewable energy consumption rate and system decarbonization level; in addition, for the operation optimization problem of this electric–gas–heat integrated energy system, a flexible energy system based on electric–gas–heat is proposed. Furthermore, to address the operation optimization problem of the HCEH-IES, a deep reinforcement learning method based on Soft Actor–Critic (SAC) is proposed. This method can adaptively learn control strategies through interactions between the intelligent agent and the energy system, enabling continuous action control of the multi-energy flow system while solving the uncertainties associated with source-load fluctuations from wind power, photovoltaics, and multi-energy loads. Finally, historical data are used to train the intelligent body and compare the scheduling strategies obtained by SAC and DDPG algorithms. The results show that the SAC-based algorithm has better economics, is close to the CPLEX day-ahead optimal scheduling method, and is more suitable for solving the dynamic optimal scheduling problem of integrated energy systems in real scenarios. Full article

Traditional single-layer water-lubricated rubber or plastic bearings suffer from water film rupture, excessive frictional losses, and insufficient load-carrying capacity, which limit performance and service life in marine propulsion and ocean engineering. To address these issues, this study introduces an innovative laminated bearing consisting of a rubber composite layer and an ultra-high-molecular-weight polyethylene (UHMWPE) layer. A three-dimensional dynamic model based on fluid–structure interaction theory is developed to evaluate the effects of eccentricity, rotational speed, and liner thickness on lubrication pressure, load capacity, deformation, stress–strain behavior, and frictional power consumption. The model also reveals how thickness matching governs load transfer and energy dissipation. Results indicate that eccentricity, speed, and thickness are key determinants of lubrication and structural response. Hydrodynamic pressure and load capacity rise with eccentricity above 0.8 or higher speeds, but frictional losses also intensify. The rubber layer performs optimally at a thickness of 5 mm, while excessive or insufficient thickness leads to stress concentration or reduced buffering. The UHMWPE layer exhibits optimal performance at 5–7 mm, with greater deviations resulting in increased stress and deformation. Proper thickness matching improves pressure distribution, reduces local stresses, and enhances energy dissipation, thereby strengthening bearing stability and durability. Full article

Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots × 1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations. Full article

Reliable predictions of (anti)neutrino spectra and luminosities are essential for assessing the feasibility of detecting pre-supernova neutrinos. Using the stellar evolution code MESA, we calculate the (anti)neutrino spectra and luminosities under realistic conditions of temperature, density, and electron fraction. Our study includes (anti)neutrinos produced by both thermal processes and nuclear weak-interaction reactions. By comparing the results of the thermal quasiparticle random-phase approximation with the standard technique based on the effective Q-value method, we investigate how thermal effects influence the spectra and luminosities of emitted (anti)neutrinos. Our findings show that a thermodynamically consistent treatment of Gamow–Teller transitions in hot nuclei enhances both the energy luminosity and the average energies of the emitted (anti)neutrinos. Full article

Capacitor voltage imbalance among submodules in energy storage modular multilevel converters (MMCs) can lead to current distortion, power oscillations, and even system instability. Traditional voltage control strategies, inherited from non-storage MMCs, offer limited regulation capabilities and are insufficient to address the complex balancing requirements across phases, arms, and submodules in distributed Energy-Storage MMCs (ES-MMC). This paper proposes a self-synchronized common-mode current strategy to achieve capacitor voltage rebalancing in Carrier Phase-Shifted PWM (CPS-PWM) modulated ES-MMCs. The proposed method establishes both phase-level and arm-level power rebalancing pathways by utilizing the common-mode current in the upper and lower arms. Specifically, the DC component of the common-mode current is employed to regulate common-mode power between the arms, while the fundamental-frequency component, through its interaction with the fundamental modulation voltage, is used to adjust differential-mode power. By coordinating these two power components within each phase, the method enables effective capacitor voltage rebalancing among submodules in the presence of power imbalance caused by a nonuniform distributed energy storage converter. A comprehensive analysis of differential- and common-mode voltage regulation under CPS-PWM is presented. The corresponding control algorithm is developed to inject adaptive common-mode voltage based on capacitor voltage deviations, thereby inducing self-synchronized balancing currents. Simulation and experimental results verify that the proposed strategy significantly improves power distribution uniformity and reduces capacitor voltage deviations under various load and disturbance conditions. Full article

The driving force behind protein translocation across the cell membrane is not yet fully understood. In bacteria, there is an electrochemical potential across the cell membrane, which can interact with charged residues in the translocation substrate. In this study, the protonation states of lysine and glutamate, serving as test residues in a peptide translocating across the bacterial channel SecYEG, are investigated by applying Poisson–Boltzmann continuum electrostatic free energy calculations and Monte Carlo titrations to snapshots of molecular dynamics (MD) simulations. A clear shift in protonation probability towards the uncharged state is found for both test residues as they move deeper into the channel. Thus, charge neutralization occurs irrespective of whether the original charge of the test residue is positive (lysine) or negative (glutamate). Electrostatic interactions of acidic and basic residues of SecYEG with the peptide cancel out. The main determinants of the test residue’s protonation state are the dielectric properties of its surroundings and interactions with non-titrating charges in the channel. Crucially, the membrane protein—including its water-filled pore—is assigned a low dielectric constant. The results are discussed in the context of the limitations inherent to continuum electrostatics and MD simulations with fixed protonation states. Full article

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial development. The evolution of this network is jointly shaped by both supply chain networks (SCNs) and executive networks (ENs), representing formal and informal relational structures, respectively. To systematically explore these dynamics, this study analyzes panel data from Chinese A-share-listed NEV firms covering the period 2003–2024. Employing social network analysis (SNA) and Quadratic Assignment Procedure (QAP) regression, we investigate how SCNs and ENs influence the formation and structural evolution of innovation networks. The results reveal that although all three networks exhibit sparse connectivity, they differ substantially in their structural characteristics. Moreover, both SCNs and ENs have statistically significant positive effects on innovation network development. Building on these findings, we propose an integrative policy framework to strategically enhance the innovation ecosystem of China’s NEV industry. This study not only provides practical guidance for fostering collaborative innovation but also offers theoretical insights by integrating formal and informal network perspectives, thereby advancing the understanding of multi-network interactions in complex industrial systems. Full article

Cryogenic propellant sloshing presents significant challenges in aerospace systems, inducing vehicle instability, structural fatigue, energy losses, and complex thermal management issues. This review synthesizes experimental, analytical, and numerical advances with an emphasis on energy dissipation and conversion efficiency in propellant storage and transfer. Recent developments in computational fluid dynamics (CFD) and AI-driven digital-twin frameworks are critically examined alongside the influences of tank materials, baffle configurations, and operating conditions. Unlike conventional fluids, cryogenic propellants in microgravity and within composite overwrapped pressure vessels (COPVs) exhibit unique thermodynamic and dynamic couplings that remain only partially characterized. Prior reviews have typically treated these factors in isolation; here, they are unified through an integrated perspective linking cryogenic thermo-physics, reduced-gravity hydrodynamics, and fluid–structure interactions. Persistent research limitations are identified in the areas of data availability, model validation, and thermo-mechanical coupling fidelity, underscoring the need for scalable multi-physics approaches. This review’s contribution lies in consolidating these interdisciplinary domains while outlining a roadmap toward experimentally validated, AI-augmented digital-twin architectures for improved energy efficiency, reliability, and propellant stability in next-generation aerospace missions. Full article

Lignin is one of the most abundant natural biopolymers and plays a crucial role in the development of safe and sustainable alternatives for healthcare products. In this study, we employed molecular dynamics simulations and free energy calculations to investigate lignin derivatives’ interactions with skin-like membranes. Specifically, we designed a small lignin derivative composed of syringyl and guaiacyl subunits. Our results reveal that molecular size, concentration, and thermal conditions critically influence the insertion, interaction dynamics, and localization behavior of lignin derivatives. Notably, variations in these parameters induce distinct behaviors, including rapid membrane insertion, hydrogen bonding, clustering, and surface adhesion. The findings provide insights into the molecular mechanisms governing lignin derivatives’ interactions with skin-like membranes, with implications for developing bio-based skincare formulations and transdermal delivery systems. Our results highlight the importance of molecular size and concentration in optimizing lignin-derived compounds for dermatological and therapeutic applications. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article