Search Results (261)

Proton exchange membrane fuel cells (PEMFCs) are promising electrochemical energy conversion technologies owing to their high efficiency, rapid dynamic response, and low-emission operation. Their performance is governed by strongly coupled electrochemical, protonic, mass-transport, and species-transport phenomena. In this study, a two-dimensional five-layer membrane electrode assembly (MEA) model was developed using the Hydrogen Fuel Cell interface in COMSOL Multiphysics. The model incorporates electronic and ionic charge transport, multicomponent gas diffusion, Darcy flow, Butler–Volmer kinetics, oxygen transport limitations, and hydrogen crossover. The model was calibrated against experimental polarization data obtained under humidified air and oxygen conditions at 100 °C and subsequently validated against experimental data at 100 °C and at 80 °C and 70% relative humidity. The calibrated parameter values obtained at 100 °C were directly applied to the 80 °C condition without further parameter fitting. Four electrochemical parameters—membrane electrolyte conductivity, ORR reference exchange current density, cathodic charge transfer coefficient, and limiting current density were calibrated to improve agreement with the experimental data. Subsequently, a one-factor-at-a-time (OFAT) analysis was performed by varying temperature (60–100 °C), relative humidity (40–70%), membrane thickness (5–15 µm), electrolyte conductivity (5–15 S m⁻¹), ORR exchange current density (10⁻⁴–10⁻² A m⁻²), and cell voltage (0.40–0.80 V). Electrode potential, electrolyte potential, pressure drop, and local O₂, H₂O, and N₂ mole fractions were evaluated. ORR kinetics and cell voltage exhibited the strongest effects on the electrochemical responses, whereas temperature and relative humidity primarily influenced protonic, pressure, and species-transport behavior. Membrane thickness and electrolyte conductivity had comparatively limited effects within the investigated ranges. The agreement between the model predictions and experimental data at 80 °C and 70% relative humidity further demonstrates the model’s predictive capability across the investigated operating conditions. The results provide a physically interpretable framework for PEMFC model calibration, parametric assessment, and subsequent optimization studies. Full article

The global transition to renewable energy has made photovoltaics a potential primary energy source, due to the abundance of solar radiation worldwide and the technological maturity of conversion systems. In this context, cadmium telluride (CdTe) remains one of the leading thin-film absorber materials due to its near-optimal bandgap (~1.5 eV) and high absorption coefficient; however, conventional CdTe-based devices face three factors that limit their efficiency: parasitic absorption in the standard cadmium sulfide (CdS) window layer, non-radiative recombination at the interfaces, and the economically costly requirement for thick absorber layers (3 to 5 µm) given the high cost of CdTe. This work proposes a dual-interface passivation strategy that simultaneously addresses loss mechanisms at both the front and back surfaces. At the front interface, the conventional CdS buffer layer is replaced by a Cd_(1−x)Zn_(x)S alloy, whose tunable bandgap (2.4–3.7 eV) suppresses parasitic absorption in the ultraviolet and blue regions and improves band alignment with CdTe, thereby increasing the short-circuit current and open-circuit voltage. At the rear interface, a CuInTe₂ (CIT) rear surface field layer is introduced between the CdTe absorber and the molybdenum (Mo) rear contact to counteract the Schottky barrier responsible for degrading hole collection. These two modifications, when implemented together, yield efficiency gains greater than those previously reported in the literature, while also enabling a substantial reduction in the thickness of the CdTe absorber to 1 µm. Using one-dimensional drift-diffusion simulations (wxAMPS) under standard AM 1.5G illumination, this study systematically evaluates the influence of the zinc content in the Cd_(1−x)Zn_(x)S buffer layer and the thickness of the CuInTe₂ layer on the key photovoltaic performance metrics (Jsc, Voc, FF, and efficiency), with the aim of identifying the optimal device configuration for high-performance and cost-effective CdTe thin-film solar cells. Full article

Arapaima gigas is a carnivorous freshwater species with high dietary protein requirements and increasing relevance for aquaculture. However, nutritional studies with this species frequently use experimental diets with varying carbohydrate fractions that are not explicitly controlled. Nitrogen-free extract (NFE), which represents soluble dietary carbohydrates, may therefore influence growth and digestibility responses and act as a confounding nutritional factor. The objective of this study was to evaluate the effects of dietary NFE on growth performance, feed efficiency, and nutrient utilization in A. gigas through meta-analysis. A systematic literature search was conducted using peer-reviewed and grey-literature sources. Studies were included when they provided quantitative information on dietary composition together with growth-performance indicators or digestibility coefficients. Twenty studies met the eligibility criteria and were included in the quantitative synthesis. Specific growth rate (SGR), feed conversion ratio (FCR), and apparent digestibility coefficients (ADC) of dry matter (DM), crude protein (CP), and gross energy (GE) were analysed using standardized mean differences (Hedges’ g) under random-effects models. Digestibility analyses were restricted to plant-based energetic and protein ingredients because animal-derived raw materials contribute minimal NFE. Higher dietary NFE levels were associated with reduced SGR, while the effect on FCR was more sensitive to study structure and within-study dependence. Higher NFE levels were also associated with reduced DM and GE digestibility, whereas the effect on CP digestibility was not statistically significant and showed substantial heterogeneity. Sensitivity analyses supported the robustness of the SGR, ADC DM, and ADC GE responses. The available evidence suggests that dietary NFE represents an important source of variation in nutritional studies involving A. gigas. Although heterogeneity among studies and variation in ingredient composition limit causal inference, improved control and reporting of dietary NFE may reduce confounding effects and improve interpretation of growth, feed efficiency, and digestibility responses in future feeding trials. Full article

Simulation studies of surplus electricity in sugarcane cogeneration almost universally assume stable nominal operation, and the sector literature qualitatively acknowledges that many surplus projects underperform; however, this discrepancy has not been systematically quantified against multiyear operational records. Using daily records from seven harvest seasons (2010–2016; 931 valid days) of a Cuban low-pressure sugar mill and the previously published simulation of its own thermal scheme (Termoazúcar STA 4.1), this study quantifies the discrepancy through the Energy Performance Gap (EPG) framework adapted from building science. The export shortfall relative to the simulated baseline ranged from 19.0% to 49.3% per harvest (38.1% aggregated over 2010–2015; 12,835 MWh unrealized) and reached 28.9% for the five-mill provincial aggregate. The gap does not arise from idle capacity—availability is 0.98–1.00, and industrial demand matches the simulated value—but from conversion, with a cane-weighted generation deficit of 5.46 kWh/t ($-12.7$%). Plant steam records proved to be accounting allocations based on fixed coefficients and cannot support correlation-based inference; what they document independently is a contiguous start-of-season regime with pressure-reducing valves in service (39 days of a single harvest), during which specific generation was 11.9 kWh/t lower at statistically identical milling rates (26.15 vs. 38.06 kWh/t; $p<0.001$). No interannual trend was detected (Mann–Kendall, $p=0.368$), although statistical power is limited, at $n=7$. A Monte Carlo characterization of the parametric uncertainty of the simulated baseline (boiler efficiency $\pm 3$%, turbine isentropic efficiency $\pm 5$%, and bagasse moisture $\pm 2$ percentage points) shows that the existence of the gap is robust—the probability of no gap is, at most, 1.1%, even under worst-case uniform perturbations—while widening the intensity-level discount interval to [0.57; 0.93]; a start-of-season depression in specific generation recurs in four of the six estimable harvests, of which the 2015 reducer regime is the most severe instance. As case-specific correction tools, operational discount factors of 0.71 (95% block-bootstrap CI [0.68; 0.75]) for export intensity and 0.62 for total seasonal energy are derived; the underlying procedure, rather than the numerical values, is proposed as transferable. Full article

This work proposes a real-time wind power estimation approach for a direct-current (DC) wind energy conversion system based on an artificial neural network (ANN). The proposed ANN uses the Levenberg–Marquardt (LM) algorithm and is trained using experimental data collected from a LabVolt wind energy test bench. The objective is to accurately estimate the instantaneous electrical power generated from the measured wind speed under different operating conditions. The proposed methodology comprises experimental data acquisition, ANN training, algorithm comparison, and model validation. The training performance of the LM algorithm was compared with that of the Resilient Backpropagation algorithm using the same dataset and network architecture. The results demonstrate that the LM algorithm provides superior convergence and prediction accuracy for the considered dataset. The selected ANN model achieved an MSE of 0.240601, an RMSE of 0.490511, an MAE of 0.207092, and a coefficient of determination of (R² = 0.999964). These results demonstrate an excellent agreement between the measured and predicted power values. The proposed ANN-based approach provides an accurate and computationally efficient solution for real-time wind power estimation and shows strong potential for integration into intelligent energy management systems and digital twin frameworks for wind energy conversion systems. Full article

Flywheel energy storage systems coupled with planetary transmissions require accurate carrier motion control to support efficient energy exchange and operation with carrier torque close to zero. This paper develops a digital cascaded acceleration and velocity control framework for a permanent magnet DC motor used as the carrier actuator in a planetary-transmission flywheel energy storage system. The controller combines an outer velocity loop, an inner acceleration loop, and a feedforward angular acceleration reference derived from the desired motion profile. Two digital implementation approaches are examined. The first preserves the continuous motor plant and includes A/D conversion, aliasing prevention and D/A reconstruction effects. The second uses a fully discrete acceleration plant for adaptive controller design. Based on this discrete model, two adaptive inner acceleration controllers are developed: adaptive deadbeat control and adaptive pole placement control. Both controllers replace only the inner acceleration PI controller, while the outer velocity loop and the feedforward structure remain unchanged. A normalized gradient estimator with projection updates the discrete plant coefficients online and keeps the estimates within calculated admissible bounds. Scilab/Xcos simulations evaluate the controllers under pulse acceleration disturbance, sinusoidal acceleration disturbance, and segmented reference tracking. The results show that both adaptive controllers reduce angular acceleration and angular velocity tracking errors compared with the digital PI baseline. The adaptive deadbeat controller gives the fastest response, whereas the adaptive pole placement controller provides a tunable compromise between response speed and smoothness. Full article

The mechanisms of plant leaf gas exchange and their relationship to energy transformation remain insufficiently studied quantitatively, limiting the assessment of CO₂ gas exchange processes. In this work, the stomatal system of a plant leaf is analyzed as a micro-, macro-, or nanoscale thermodynamic system, applying modeling based on bionic principles. An idealized thermodynamic cycle is constructed, allowing for the assessment of the conversion of thermal energy into mechanical work. The theoretical upper limit of the thermal efficiency coefficient is estimated at η_t ≈ 0.003, and the mechanical energy flux, at a temperature difference of approximately 1 °C between the leaf and the ambient temperature, reaches up to 0.6 W/m², i.e., about 0.3% of the solar radiation flux absorbed by the leaf. The results obtained show that even with low efficiency, this energy transformation is sufficient to influence the intensity of gas exchange. Based on the model, the plant’s CO₂ sorption potential, depending on canopy area, was also estimated. It is concluded that the stomatal system of a plant leaf can be interpreted as a theoretical bionic energy transformation model, suitable for the analysis of CO₂ exchange processes and the development of bionic micro-, macro-, or nanoscale systems. Full article

Thermoelectric air conditioning (TEAC) systems are gaining attention as refrigerant-free, solid-state cooling technologies due to their compactness, low noise, and environmental benefits. However, their widespread application is constrained by low energy efficiency and the limited experimental understanding of the coupled influence of electrical and aerodynamic operating parameters. Most existing studies address thermoelectric cooling under isolated conditions or rely on theoretical modeling, leaving a clear gap in the experimental quantification of the interactive effects of applied voltage and air velocity on system performance. To address this gap, the present study experimentally investigates a laboratory-scale TEAC system equipped with four thermoelectric cooler (TEC) modules (model TEC1-12706). The system was tested under controlled conditions by varying the input voltage from 6 to 12 V and the air velocity from 1 to 3 m/s. Key performance indicators, including cooling capacity, power consumption, cold-side temperature, and coefficient of performance (COP), were systematically measured and analyzed. The results show that increasing the applied voltage from 6 to 12 V enhances cooling capacity by approximately 50.4%, while significantly increasing electrical power consumption, leading to a 59% reduction in COP due to intensified Joule heating. Conversely, increasing air velocity improves convective heat transfer, resulting in a COP enhancement of about 24% with relatively stable power input. The findings highlight a clear trade-off between cooling capacity and energy efficiency and identify a practical operating region for balanced TEAC performance. Full article

Urban low-carbon transition is increasingly shaped by the interaction between digital infrastructure, intelligent applications, land-space allocation, and regional governance. Existing studies have mainly examined whether the digital economy or smart-city development can reduce emissions, but less attention has been paid to the coordination between digitalization and intelligentization, the carbon cost of digital infrastructure, and the spatial consequences of local gains. This research defines digital–intelligent integration as the coupling coordination between digitalization and intelligentization subsystems. Using panel data for 39 prefecture-level cities in the Middle Reaches of the Yellow River Urban Agglomeration from 2013 to 2022, it applies Global Moran’s I, a spatial Durbin model, partial-derivative effect decomposition, alternative spatial weight matrices, alternative dependent variable tests, and multidimensional heterogeneity analysis. The own-city coefficient of digital–intelligent integration in the carbon-efficiency model is positive (0.0282, p < 0.05), whereas the spatial-equilibrium direct effect is statistically insignificant. These quantities are not short- and long-run estimates: the former is a conditional model coefficient, while the latter incorporates spatial feedback. The indirect effect on neighboring carbon efficiency is negative and remains negative under contiguity, economic-distance, and geo-economic nested matrices. Under an otherwise identical fixed-effects specification, digital–intelligent integration lowers local construction-land carbon intensity but raises neighboring intensity. The structural estimates further show that local conversion is weaker in industrially and energy-intensive cities. Digital–intelligent integration should therefore be interpreted as a governance capacity rather than a net-carbon technology; its regional effect depends on industrial lock-in, infrastructure-energy demand, and cross-city responsibility sharing. Full article

Energy efficiency (EE) is integral to a sustainable energy system and can play a significant role in climate mitigation by reducing energy consumption and the adverse effects of climate change. This paper examines the association between CO₂ emissions, EE, environmentally related taxes (ERTs), renewable energy (RE), and non-renewable energy consumption (EC) in Australia from 1990 to 2020. Using a dynamic ARDL model, the empirical findings show that adopting EE (β = −0.14, p = 0.000), ERT (β = −0.08, p = 0.071), and RE (β = −0.008, p = 0.007) is associated with lower carbon emissions, particularly in the short run. Conversely, EC impedes climate mitigation, as revealed by the substantial positive and significant coefficient of 1.4%. Notably, EE has the largest short-run coefficient among the mitigating variables, indicating that energy efficiency is the most significant mitigator of carbon emissions in Australia in the short run. The ARDL bounds test confirms the existence of a long-run equilibrium relationship among the variables. However, in the long run, EE, ERT, and RE do not mitigate carbon emissions, given their statistically insignificant coefficients, whereas EC remains strongly positively related to CO₂ emissions. Results from the FMOLS and DOLS estimates largely support the ARDL findings, though some sensitivity is observed for ERT. Consequently, this paper proposes a comprehensive policy direction for governments and international organizations, emphasizing the importance of reducing energy intensity and promoting EE as core climate mitigation instruments to foster a green, sustainable environment. Full article

Ocean current energy is abundant, yet its exploitation is severely constrained by the low-velocity conditions typical of most marine environments, where conventional lift-type turbines exhibit poor self-starting capability and low efficiency. This review provides the first comprehensive synthesis of research on free-stream Archimedes spiral hydrokinetic turbines (ASHTs), a class of drag-dominated rotors developed specifically for low-velocity kinetic energy harvesting. A unified classification is introduced, dividing ASHTs into single-blade long-axis (SL-ASHT) and three-blade short-axis (TS-ASHT) configurations. The energy conversion mechanisms, governed by pressure difference and hydrodynamic force synergy within helical passages, are elucidated, and the influence of critical geometric parameters is assessed. For SL-ASHTs, the analysis highlights exceptional self-starting capability (cut-in velocity: 0.1 m/s), a starting torque coefficient of 0.52, a maximum power coefficient of 0.51, and passive yaw adaptability that limits efficiency variation to below 2% over yaw angles of 0–40°. TS-ASHTs feature a compact architecture and higher rotational speed, facilitating direct generator coupling. With variable blade-angle distributions, thin airfoils, and non-uniform gap ratios, the power coefficient reaches 0.312. Performance-enhancement measures, including multi-parameter optimization, ducts, and winglets, deliver power gains of up to 35%, 122%, and 12%, respectively. This review further identifies critical barriers to engineering deployment: sediment erosion, cyclic fatigue, performance degradation under large yaw angles, and wake interactions. Future priorities include multi-objective optimization, advanced materials and flow control, full-scale sea trials, multiphysics coupling, array layout optimization, and hybrid energy system integration. By establishing a coherent classification and performance-evaluation framework, this work demonstrates that ASHTs offer strong potential as core devices for large-scale utilization of low-velocity ocean current and river hydrokinetic energy. Full article

This study investigates the aerodynamic and thermal impacts of tire sidewall cooling fins on a passenger vehicle using high-fidelity computational fluid dynamics (CFD) simulations. Continuous heat accumulation from tire rotation and road friction can degrade structural durability. To address this thermal challenge, a parametric study was conducted on the DrivAer notchback vehicle model across various fin angles from −67.5° to 67.5°. The results revealed a distinct design space that offers simultaneous aero-thermal improvements. Specifically, the 22.5° fin configuration demonstrates a dual-benefit performance, achieving a 3.79% net reduction in overall vehicle drag alongside a 17.36% increase in the average heat transfer coefficient (HTC). Conversely, the −22.5° configuration yields the maximum cooling enhancement with a 30.49% increase in average HTC but incurs a 2.52% drag penalty. Microdrag and Turbulent Kinetic Energy (TKE) analyses successfully explain the underlying fluid mechanisms governing these trade-offs. These findings provide practical design guidelines for flow control on rotating wheels, showing that tire sidewall geometries can enhance full-vehicle aerodynamic efficiency and tire thermal reliability. Full article

This paper presents the design, fabrication, and experimental validation of a 3.45 GHz RF-to-DC voltage-doubler rectifier intended for sub-6 GHz wireless power transfer and RF energy harvesting applications. The proposed rectifier uses low-barrier SMS7630 Schottky diodes, a distributed microstrip impedance-matching network, and an FR4 substrate optimized for low RF input power operation. The design was developed using nonlinear Harmonic Balance simulations in Advanced Design System (ADS) and experimentally characterized through reflection-coefficient measurements, output DC voltage measurements, and RF input power sweeps. A quantitative comparison between simulated and measured results demonstrates good agreement in the impedance-matching characteristics, with a measured resonance frequency of 3.438 GHz and a minimum reflection coefficient of −25.22 dB. The fabricated prototype achieved a maximum measured output DC voltage of 1.58 V and a peak RF-to-DC conversion efficiency of 25.9% at an RF input power of +2.9 dBm. The experimental results validate the practical implementation of the proposed rectifier topology and demonstrate its feasibility as a proof-of-concept RF energy harvesting building block for ultra-low-power IoT and wireless sensing applications. The presented implementation also provides a solid foundation for future optimization using low-loss microwave substrates, improved matching networks, and complete rectenna integration. Full article

Biomass torrefaction represents a highly promising thermochemical pathway for upgrading low-density agricultural residues into high-value solid biofuels. However, optimizing reactor operations requires resolving the conflict between product energy enrichment and mass loss under transient heat transfer limitations. In this work, a transient kinetics-coupled Pareto optimization and techno-economic framework is developed for the torrefaction of rice husk residues (Oryza sativa), with pine wood (Pinus sp.) as a validation reference. The framework connects a transient 1D radial finite-difference heat transfer model in a cylindrical particle to a two-stage sequential chemical kinetics scheme, which was successfully calibrated against experimental thermogravimetric analysis (TGA) data. The physical model outputs (instantaneous species concentrations, temperature profiles, and process thermal demand) are dynamically coupled to an economic module to calculate the Levelized Cost of Torrefaction (LCOT). A grid sweep with Pareto non-dominance filtering is conducted on the active torrefaction design space (using a product quality constraint $Y_{BT}\le 0.96$ to avoid degenerate zero-conversion limits) to identify the Pareto frontier that minimizes LCOT while maximizing the efficiency index ($\eta$). To evaluate the financial and technical stability of the Pareto operating point for rice husk (523 K, 30 min), a global sensitivity and uncertainty analysis (GSA) is executed using 250 Latin Hypercube Sampling (LHS) Monte Carlo simulations coupled with Standardized Regression Coefficients (SRCs). The results show a baseline LCOT of 6.49 USD/GJ for rice husk at its 1 dry t/h pilot Pareto knee point (523 K, 30 min), which is projected to decrease to 4.12 USD/GJ under an industrial-scale techno-economic scenario (50 dry t/h). Under uncertainty, LCOT displays a mean value of $6.486\pm 0.565$ USD/GJ (95% CI: $5.517$–$7.644$ USD/GJ), which is heavily dominated by the raw feedstock acquisition cost ($\beta =0.7430$, $p<0.001$) and CAPEX contingency multiplier ($\beta =0.6129$). The efficiency index exhibited limited variability (mean $91.40\%\pm 0.96\%$, 95% CI: $89.80\%$–$93.26\%$), governed primarily by the particle diameter $d_p$ ($\beta =0.7828$) and secondary convective heat transfer coefficient h ($\beta =-0.5929$, $p<0.001$). This work successfully demonstrates that coupling transient transport phenomena to a techno-economic cash-flow layer provides a physics-informed framework for techno-economic evaluation and scale-up of thermochemical bioreactors. Full article

This study investigates the effects of Ta doping on the elastic, dielectric, piezoelectric, and electromechanical coupling properties of lithium niobate (LiNbO₃, LN) crystals using first-principles calculations. The results show that isovalent substitution of Nb⁵⁺ by Ta⁵⁺ maintains mechanical stability in all doped systems. Ta incorporation enhances the overall stiffness and deformation resistance, while strengthening ionic displacement polarization and the piezoelectric stress response. The piezoelectric strain constant d₃₃ and electromechanical coupling coefficient k₃₃ exhibit different optimal doping concentrations. d₃₃ reaches 9.548 pC/N at 10% Ta doping, corresponding to a 13.6% improvement over intrinsic LN, whereas k₃₃ reaches a maximum of 0.2569 at 3.33% Ta doping and remains high in the 3.33–6.67% range. This separation originates from the competition among polarization enhancement, elastic stiffness hardening, and nonlinear dielectric growth. Enhanced ionic polarization promotes d₃₃, while excessive dielectric energy storage and increased stiffness suppress effective electromechanical energy conversion. These results reveal the microscopic mechanism governing composition-dependent electromechanical tuning in Ta-doped LN crystals. Accordingly, 10% Ta is suitable for improving strain sensitivity, whereas 3.33–6.67% Ta is preferable for optimizing energy conversion efficiency in LN-based sensors, actuators, transducers, and resonators. Full article