Search Results (5,209)

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

Background/Objectives: Cyclotides are plant-derived macrocyclic peptides distinguished by their head-to-tail cyclized backbone and cystine knot motif, which confer remarkable stability against thermal, enzymatic, and chemical degradation. These features, combined with a compact and rigid structure, position cyclotides as promising scaffolds for future antibacterial agents in response to the escalating threat of multidrug-resistant (MDR) pathogens and the stagnation of conventional antibiotic discovery pipelines. This review summarizes the structural features, antibacterial mechanisms, bioengineering strategies, and translational potential of cyclotides against MDR infections. Methods: A narrative review of the literature was conducted using recent original research articles and reviews on cyclotide structure, antibacterial activity, bioengineering, computational modeling, and pharmaceutical applications. Results: Cyclotides exhibit potent antimicrobial activity, primarily through membrane disruption mediated by amphipathic surfaces and affinity for anionic bacterial membranes. Some variants also demonstrate anti-virulence and antibiofilm properties, broadening their therapeutic relevance for difficult-to-treat infections. Bioengineering approaches, including epitope grafting and rational design, have improved selectivity and potency while reducing cytotoxicity. Advances in computational modeling, molecular dynamics, and artificial intelligence have accelerated the prediction and optimization of antimicrobial activity, toxicity, and pharmacokinetic properties. Conclusions: Innovations in synthesis, including recombinant expression and enzymatic ligation, are helping overcome translational barriers related to cost and scalability. Although challenges remain in oral bioavailability and systemic delivery, strategies such as lipidation and scaffold modification support the development of cyclotide-based therapeutics as adaptable platforms for peptide drug discovery. Full article

This study investigates the nonlinear dynamic response and chaos evolution of a shape memory alloy hybrid composite (SMAHC) actuator beam under coupled thermal, harmonic, and aerodynamic excitations. A reduced-order nonlinear dynamic model was developed by combining Euler–Bernoulli beam theory, von Karman geometric nonlinearity, the Brinson SMA constitutive relation, and first-order piston-theory aerodynamics. The governing equations were derived from Hamilton’s principle, discretized by the weighted residual method, and solved using the Newmark-beta algorithm. Chaotic evolution was quantified using a largest Lyapunov exponent-based chaos intensity indicator rather than the exact Kolmogorov–Sinai entropy. The reduced-order model was compared with ABAQUS finite element simulations under representative coupled aerodynamic and harmonic loading. The MATLAB prediction and ABAQUS response gave a dominant frequency of approximately 9.50 Hz, close to the prescribed excitation frequency of 9.55 Hz, with peak displacement amplitudes of approximately 0.0285 mm and 0.0324 mm, respectively. A supplementary ABAQUS modal-frequency separation check supported the use of the two-mode reduced-order model for the dominant low-frequency response, while also clarifying its limitation for high-dimensional chaotic modal interactions. The parametric results showed that an increasing excitation amplitude and aerodynamic load promoted frequency broadening and chaotic transitions. The Lyapunov-based indicator rose near γ = 65 under λ* = 100 and near λ* = 328 under γ = 30. Temperature-dependent SMA recovery stress further shifted the transition threshold by modifying the effective stiffness and internal restoring action of the beam. These results provide a reduced-order framework for interpreting nonlinear response transitions in SMAHC actuator beams in thermo-aeroelastic environments. Full article

Scandium (Sc) is well recognized as a potent grain refiner, yet optimizing its addition amount in the Al-Cu-Mg-Ni-Fe (A242) system remains a longstanding challenge, critically important for material performance in high-temperature automotive and aerospace applications. The present work, therefore, presents a study of low-Sc modified A242 alloys, demonstrating that 0.2 wt.% Sc microalloying of the system has a pronounced effect on its solidification-driven microstructural evolution, improving the high-temperature formability of the alloy over a 20–200 °C temperature range. The study demonstrates that this addition triggers a dramatic columnar-to-equiaxed grain transition, reducing the average grain size by 90.8% (from 400 ± 100 μm to 37 ± 10 μm) and fragmenting the brittle, continuous intermetallic network into a highly uniform architecture. Uniaxial compression testing revealed that, while the as-cast solid-solution alloy slightly reduces room-temperature strength due to solute trapping, it delivers an exceptional 142% increase in strain-to-failure at 200 °C (exceeding 0.8 mm) compared to the base alloy. This significant enhancement in ductility is driven by thermally stable Al₃Sc dispersoids that exert Zener pinning pressure, halting thermal grain coarsening and activating superplastic deformation mechanisms. These findings support the development of advanced thermoforming applications, with the finite element (FE) model predicting process improvements that enhance manufacturing efficiency. This work presents a validation and simulation-ready material framework that substantiates the viability of low-Sc-modified A242 alloys for such operations. Full article

This study investigates the effects of Cobalt Oxide (Co₃O₄) nanoparticles on engine performance as well as emission characteristics under various engine load situations in test fuel (MB10). Response Surface Methodology (RSM) was used to examine the experimental results to assess the impact of nanoparticle concentration (0–150 ppm) on combustion behavior. Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) were performance metrics, and CO, HC, CO₂, and NO_x were emission characteristics. The findings demonstrated that the inclusion of nanoparticles and biodiesel had a major impact on emission behavior and performance. Because biodiesel contains more oxygen than diesel fuel, it reduces CO emissions while increasing CO₂ and NO_x emissions. By boosting heat transmission, the use of nanoparticles increased combustion efficiency; however, fuel atomization was adversely affected by high concentrations. With error rates under 10% for every response, RSM models showed excellent prediction accuracy. To achieve 21% BTE, 458.21 g/kWh BSFC, and minimum emission levels of 0.048% CO, 9.478 ppm HC, 5.415% CO₂, and 601.09 ppm NO_x, the optimization study identified the optimal operating condition with a 1.31 kW engine load and 80.36 ppm Co₃O₄ addition. The results verify that the proper dosage of nanoparticles can enhance the combustion performance of biodiesel while preserving acceptable emission levels. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

To address the time-consuming and labor-intensive procedures associated with traditional approaches for evaluating the integrated burning rate of wire-embedded propellants in solid rocket motors (SRMs), this study proposes an efficient and reliable prediction method. This new method is based on an improved burning-rate–initial-temperature correlation, achieved through Abaqus-Python secondary development that enables fully automated geometric modeling, transient heat-transfer analysis, and temperature-field extraction for wire-embedded propellants. The relative error between the present method and the experimental results is less than 5%. The accuracy and engineering applicability of the present method are verified. The effects of the material parameters and wire diameters on the integrated burning rate is investigated. The results indicate that wires of different materials exhibit substantial variations in burning-rate enhancement efficiency, with smaller diameters and higher thermal diffusivity producing stronger enhancement effects. When the specific heat capacity and density are held constant, the integrated burning rate increases monotonically with the wire’s thermal conductivity, though the growth trend gradually approaches saturation. In contrast, the influences of the wire’s specific heat capacity and density are comparatively weak. The integrated burning rate prediction framework developed in this study demonstrates strong versatility and scalability. It enables rapid performance evaluation of propellants embedded with wires of various sizes and thermophysical properties, providing valuable theoretical guidance and practical tools for the design and optimization of wire-embedded solid rocket motors. Full article

The use of sophisticated software for greenhouse microclimate analysis often requires advanced modelling expertise and significant computational effort, which may not always be available to greenhouse designers. This study proposes an integrated and modular workflow aimed at supporting greenhouse design through coupled thermal and evapotranspiration simulations. The design methodology is based on three steps. In the initial phase, the greenhouse environmental conditions are evaluated through the implementation of a dynamic thermal analysis, which is conducted by the DesignBuilder software (version 4.2). Subsequently, a plant evapotranspiration model is employed in MATLAB/Simulink (version R2025b) to evaluate crop transpiration, moisture production, and irrigation water consumption. In the final phase, the simulated moisture production is used to estimate the required ventilation rates and to support the sizing of greenhouse systems, including irrigation and HVAC components. Plant moisture production is a crucial factor in determining the sizing of greenhouse subsystems, such as the irrigation system, the ventilation rate, and the HVAC system. Nonetheless, the implementation of the evapotranspiration model necessitates a bespoke calibration to a case study. Indeed, the proposed models are more generally applicable and must be adapted to real-world applications. The methodology was applied to a small greenhouse used for the cultivation of aeroponic lettuce (Lactuca sativa cv. Romana) in a Mediterranean environment. The aim of the study was to explore the potential of the proposed integrated modelling framework to estimate annual irrigation water demand and the minimum ventilation rate required to mitigate excess moisture production, using a coupled MATLAB/Simulink implementation. The proposed approach should be interpreted as an exploratory design-support methodology rather than a fully validated predictive model, intended to investigate system behaviour under the specific conditions of the case study. Full article

Hydrogen fuel cells offer strong potential for decarbonizing aviation, yet their megawatt-scale integration is limited by thermal management system (TMS) challenges. In low-temperature Proton Exchange Membrane Fuel Cell (PEMFC) systems, the heat exchanger (HEX) is the key TMS component influencing thermal efficiency, mass, and reliability. While prior work has focused on thermo-hydraulic optimization, structural behavior under flight conditions remains insufficiently addressed. This study introduces a coupled CFD–FEA methodology for a nacelle-integrated, megawatt-class plate–fin HEX. The model captures the effects of non-uniform thermal loads, constrained thermal expansion, and dynamic excitation. Local flow-induced vibrations are assessed through pre-stressed modal analysis, and global dynamic behavior is predicted using a homogenized approach. Results show that thermally induced stresses dominate over pressure loads, and the introduction of coolant-fin geometries with suitable expansion tolerances mitigates stress and resonance risks. The approach provides design guidance for structurally robust, vibration-tolerant, and aero-thermally efficient HEXs for next-generation PEMFC-powered aircraft. Full article

Infrared small-UAV detection remains difficult because the target often appears as a weak thermal point rather than a clear object. This problem is clear in the SIDD dataset, where most test targets are smaller than 32 × 32 pixels. To address this case, this paper proposes S-Drone-YOLO, a compact YOLO-based detector that maintains a high-resolution P2 prediction path and leverages it carefully during classification. The model starts from a lightweight YOLOv5-style detector. It adds a stride-4 P2 path and replaces the C3 neck blocks with C2fAttn to improve feature reuse before prediction. Two components are then added to the Architecture II design. The Coordinate-Aware Residual C2f Block, CAR-C2f, strengthens the P2 branch using coordinate attention and residual scaling. The P2-Guided Quality-Aware Detection Head (P2-QADH) combines local P2 details with nearby P3 context. It produces a quality map that adjusts the classification logits. The regression branch, output tensor format, and training loss interface remain unchanged. On the SIDD infrared drone dataset, S-Drone-YOLO reaches 0.988 precision, 0.939 recall, 0.699 mAP50-95, and 0.962 F1-score. It uses 6.45 M parameters and 31.3 GFLOPs. Compared with the Architecture I model, recall increases by 0.8 percentage points and mAP50-95 increases by 0.4 percentage points. At the same time, the parameter count decreases by 20.3%, and GFLOPs decrease by 43.7%. Fine-tuning on five RGB UAV datasets and a second thermal dataset (ThermalUAV2UAV) yields F1 scores ranging from 0.941 to 0.999, with an mAP50-95 of 0.843 on the thermal dataset. The background analysis also shows stable F1-scores across sky, sea, city, and mountain scenes. These results suggest that controlled P2 guidance can improve infrared small-UAV detection while keeping the model size practical. Full article

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

Accurate Remaining Useful Life (RUL) prediction of TFT-LCD modules is critical for industrial predictive maintenance, yet it remains heavily challenged by complex degradation mechanisms in different climates. Traditional purely data-driven models (SVR, LSTM) often lack physical interpretability, struggling to filter out environmental noise or predict irreversible failures. To address this, we propose a highly reliable prognostic tool based on a Physics-Informed Gaussian Process Regression (PI-GPR) framework, by embedding cumulative thermal load and thermo-mechanical stress into the model’s prior function. Evaluated using one-year field exposure data, the physical constraints empower the model to accurately predict device lifetime under highly variable environments, including luminance fluctuations in tropical hygrothermal conditions and device failures in cold environments. Quantitative results demonstrate that the unified PI-GPR framework achieves an outstanding coefficient of determination (R² = 0.93) and reduces the RUL prediction error to merely 7.5 days, significantly outperforming conventional shallow learning, deep sequence, and standard probabilistic baselines. Ultimately, this study provides a robust, physically grounded methodology for the health monitoring and life cycle management of display modules in practical industrial applications. Full article

This study presents a novel investigation into the coupled effects of ambient temperature and compressor frequency on frosting behavior and thermal performance of inverter-driven air-source heat pumps (ASHPs) under low-temperature, high-humidity conditions. Unlike previous studies that focused on single environmental parameters, this work systematically explores temperature–frequency coupling. Experiments were conducted on a 3-HP DC inverter low-ambient-temperature ASHP unit using a multi-climate simulated enthalpy difference test bench. Single-factor analysis shows that frosting is most severe at 0 °C, where the frost growth rate peaks. Regarding compressor frequency, the coefficient of performance (COP) initially increases and then decreases with frequency. The maximum COP occurs near 45 Hz, representing the optimal energy efficiency balance in this experimental system. Sensitivity analysis demonstrates that relative humidity contributes less than 5% to performance degradation at the critical 10% COP reduction point. Thus, ambient temperature and compressor frequency are the core determinants of defrosting timing. A dual-factor prediction model for the critical defrosting air-to-coil temperature difference (∆T) is developed using temperature (t) and frequency (f) as independent variables. Validation confirms that the model maintains prediction error within 10% under both single-factor and multi-factor coupling conditions. Collectively, this research quantifies the coupled effects of ambient temperature and compressor frequency on frosting performance and provides a novel theoretical framework for precise defrosting control in inverter ASHPs based on performance attenuation. Full article

With the increasing integration of grid-friendly concentrated solar power (CSP) plants into high-proportion new energy power systems, the system is confronted with challenges such as insufficient regulation capability and power balance difficulties. To address these issues, this paper proposes a multi-stage optimal regulation strategy for CSP–wind power systems based on adaptive step-size model predictive control (ASMPC), from the perspectives of tapping transmission line current-carrying capacity and coordinating system regulation resources. This strategy first establishes an electro–thermal–mechanical coupling dynamic line rating (DLR) model to characterize line safety margins, then constructs an optimization decision-making model aiming at minimizing the total multi-stage coordinated scheduling cost and adopts ASMPC to dynamically adjust the control step size, effectively improving scheduling accuracy and real-time correction capability. Simulation results based on the modified IEEE 39-bus system show that the proposed method reduces the total system cost by 26.8% (nearly 30%), increases the CSP unit output ratio by 27.9%, and decreases the average grid load rate by 12.6 percentage points. The proposed strategy can effectively mitigate the impact of source-load uncertain fluctuations and significantly improve the economic operation level of the CSP–wind power combined system. Full article

Aluminum electrolysis is an energy-intensive process in which the cathode voltage drop (CVD) and horizontal current in the molten aluminum layer directly affect energy consumption and cell stability. In this study, a three-dimensional electro-thermal model of a 400 kA prebaked aluminum electrolysis cell was established to optimize copper-embedded cathode collector bars. Using a staged parameter-screening and integrated optimization strategy, the effects of copper rod longitudinal position, diameter, and embedded length on CVD, horizontal current density, cathode surface current uniformity, and thermal response were systematically evaluated. Under the present modeling conditions, the configuration with a longitudinal position of 1.0 m, diameter of 0.05 m, and embedded length of 1.0 m provided a favorable balance between electrical performance and copper consumption. This design reduced the equivalent voltage drop by 142.7 mV and decreased the average horizontal current density in the molten aluminum layer to approximately 4900 A/m². The peak cathode surface current density was also reduced, corresponding to a predicted cathode service-life increase of approximately 13.2% based on a relative wear model. A preliminary economic analysis indicated that an initial investment of CNY 424,000 could yield conservative annual electricity cost savings of approximately CNY 114,000, with a simple payback period of about 3.7 years. These results provide quantitative guidance for the structural design and industrial evaluation of copper-embedded cathode collector bars. Full article