Search Results (226)

Achieving sustainability in the manufacturing sector calls for systemic reductions in energy consumption and carbon emissions without compromising productivity. In the global energy consumption landscape, the manufacturing sector accounts for a significant proportion and is a major source of carbon emissions, with manufacturing systems and HVAC (Heating, Ventilation, and Air Conditioning) systems being the principal energy consumers. Existing research typically optimizes these two systems independently, neglecting their dynamic coupling; production scheduling determines equipment power and heat dissipation, which alters building thermal loads and consequently affects HVAC energy consumption. To address this problem and advance sustainable manufacturing practices, this study proposes an energy-aware scheduling framework integrating manufacturing and HVAC control. A WOA-XGBoost energy consumption prediction model is constructed, employing the Whale Optimization Algorithm to tune XGBoost hyperparameters, achieving a prediction accuracy of R² = 0.937 on the Shanghai typical meteorological year dataset. The HVAC decision variables are defined as five operational control variables—supply air flow rate, fan total pressure, ERV sensible/latent heat recovery effectiveness, and ventilation air flow rate—ensuring the physical realizability of scheduling solutions. An integrated scheduling-and-control model incorporating production capacity constraints and electricity demand response is then formulated and solved using a hybrid Particle Swarm Optimization algorithm. Validation on a five-machine, four-buffer flow shop demonstrates that the proposed framework reduces total electricity cost by 8.85% and total energy consumption by 14.88% in summer compared with a physics-based coupling baseline, with all metrics exhibiting coefficients of variation below 4% across ten independent runs. These results demonstrate that the proposed data-driven framework provides a practical and scalable pathway toward sustainable manufacturing by jointly reducing energy use and associated carbon emissions while maintaining full production throughput. Full article

Drought is a major global hazard, yet critical knowledge gaps persist regarding how the El Niño–Southern Oscillation (ENSO) modulates it in topographically complex equatorial regions. This study characterizes ENSO’s spatiotemporal influence on drought across Ecuador’s four principal Köppen–Geiger climate zones: Amazon, Andean highlands, temperate, and arid coastal. Using meteorological data (1985–2015), we computed the Standardized Precipitation Evapotranspiration Index (SPEI) across multiple timescales. Ten ENSO indices were evaluated using Wavelet Coherence analysis to identify non-stationary, scale-dependent correlations and phase dynamics. Results show that tropical, temperate, and Andean (polar tundra) climates exhibit prolonged climatic memory, with significant ENSO correlations across 1- to 24-month SPEI scales. Conversely, arid regions display shorter memory, with correlations dissipating at longer timescales due to limited moisture storage. Phase analysis reveals two high-coherence intervals (1995–2000 and 2007–2013) at the 3-year return period, in which ENSO indices led drought by 9–18 months, underscoring their predictive potential. At 6- and 11-year periods, ENSO signals generally lag SPEI, indicating prolonged drought retention. The Trans-Niño Index and Southern Oscillation Index proved particularly sensitive for the Amazon–Andes transition. These findings establish a robust framework for improving drought monitoring and climate adaptation in vulnerable equatorial regions. Full article

This paper presents a thermal and power characterization of the NVIDIA Jetson AGX Orin deployed in a realistic outdoor edge AI enclosure, integrated with power supplies, a Long Term Evolution (LTE) module, and a thermostat-controlled fan, across an ambient temperature range from −20 °C to +40 °C. The device was tested in a climate chamber under two workloads: a synthetic CPU and GPU stress test, and a YOLOv8s inference workload (TensorRT FP16, 640 × 640 input). Internal temperatures were recorded using four calibrated platinum Resistance Temperature Detectors (RTD)—PT100/PT1000, while Jetson chip temperatures and power consumption were logged via tegrastats and jtop. At sub-zero ambient temperatures, heat dissipated by the device itself kept all components within their operating ranges down to −20 °C. At the +40 °C setpoint, the stress test triggered Jetson thermal throttling at GPU and CPU temperatures of +95.6 °C and +99.0 °C, respectively. Under the same conditions, the YOLOv8s inference workload sustained 108.8 frames per second (FPS) at 19.1 W average power, approximately half of the 36.5 W consumed under the stress test, with chip temperatures well below the throttling threshold. These findings indicate that synthetic stress tests substantially overestimate the thermal and power demands of the tested inference workload, and that the enclosure retains sufficient thermal headroom for outdoor edge device deployment. Full article

The nitrile butadiene rubber-based viscoelastic damper (NVED) has been proven effective in improving the seismic performance of various types of structures. This study proposes to enhance the hysteretic behavior of traditional Chinese wooden joints using the NVED. The cyclic tests on the NVED are first conducted to derive their mechanical properties. Secondly, two configurations of the mortise-tenon joints are selected as the prototype models to fabricate four specimens, and the hysteretic loading tests are conducted on the specimens to derive their hysteretic behaviors. Comparisons are made between the models with and without the NVED to clarify its reinforcing effects. On the basis of the test results of the mortise-tenon joints and the NVED, a simplified simulation method is proposed to represent the joints with the NVED. The test results show that the installation of the NVED can remarkably improve the hysteretic performance of mortise-tenon joints throughout the entire loading process. Compared with the unreinforced joints, the bearing capacity and energy dissipation of the NVED-reinforced specimens can increase by approximately 40%, particularly under large deformation conditions. The proposed simplified simulation method, which adopts zero-length elements to simulate the rotational response of the joints and the NVED, can adequately capture the pinching effect as well as the stiffness and strength degradation of the NVED-reinforced mortise-tenon joint models. Full article

Achieving climate-neutral aviation requires propulsion systems capable of reducing emissions and noise while maintaining high aerodynamic efficiency. Distributed Electric Propulsion (DEP) represents a promising solution; however, accurately predicting the unsteady aerodynamic interactions between multiple propellers and lifting surfaces remains challenging. This work investigates the aerodynamic performance of two Distributed Propulsion (DP) configurations using FLOWUnsteady, a meshless Computational Fluid Dynamics (CFD) solver based on the reformulated Vortex Particle Method (rVPM) within a Large-Eddy Simulation (LES) framework. The Lagrangian particle formulation eliminates mesh generation and limits numerical dissipation. Two layouts—a twin wingtip-mounted arrangement and a four-propeller configuration including inboard units are analyzed and compared with a clean wing baseline as functions of propeller position, inflow speed (20 and 33 m/s), and angle of attack. Beyond global aerodynamic performance metrics, the rVPM–LES framework provides a time-resolved and spatially resolved characterization of local propeller–wing interference in multi-propulsor configurations, highlighting differences in loading and torque demand between inboard and wingtip propellers that are not typically captured by low- to mid-fidelity modeling approaches. The results show that distributed propulsion increases lift and reduces drag relative to the clean wing by accelerating the local flow, delaying separation, and enhancing wing circulation. Thrust and torque coefficients exhibit a clear dependence on rotational speed and angle of attack: inboard propellers experience stronger aerodynamic interference and higher torque demand, whereas wingtip propellers maintain more uniform loading. These findings confirm the capability of the meshless rVPM approach to accurately and efficiently capture unsteady interactions in distributed propulsion systems, supporting its application to the analysis and design of future DEP aircraft. Full article

The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital twins are a trending tool that can assimilate real-time sensor data to tailor a virtual representation to its physical counterpart. The online faithful virtual representation of the physical system provided by digital twins can be used for real-time system optimizations and proactive fault detection, diagnostics, and control adjustments, alleviating the stress of component aging. To support these complex power systems throughout their lifecycles, data-driven solutions for digital twin tuning will become essential. This paper investigates the application of a parameter-tuning digital twin framework to enhance the performance of a multi-physics model. The digital twin framework comprises a digital twin tuning scheme, a physical testbed designed to emulate the cooling system of a ship, and a multi-physics representation of that system. The digital twin tuning scheme leverages a swarm of particles and online sensor data to evaluate permutations of parameters to update the digital representation periodically. The digital twin framework was applied to a physical system to provide experimental data results demonstrating the usefulness of the tuning system. The physical system was designed and constructed to emulate the heat generation and dissipation from 6 liquid-cooled power converters under loads ranging from 10–15 kW at 99% efficiency. Two scenarios were applied to evaluate the performance of the digital twin framework. Results demonstrate that the digital twin framework can adapt to system changes in real-time and improve the accuracy of the related virtual representation by more than 90% when measured at four points of the system under test. Full article

Accurately estimating the stress thresholds (crack closure stress σ_cc, crack initiation stress σ_ci and crack damage stress σ_cd) is of great significance to the study of failure mechanisms for hard rock. Uniaxial compression tests were conducted on four types of hard rock to investigate the rationality and applicability of four different identification methods. The stress thresholds obtained by different methods were compared and analyzed. The main research results are as follows. Both the two distinct energy dissipation rate (EDR) methods underestimate the value of σ_cc, and it is not applicable for hard rock with few primary fractures. Since the method of lateral strain response (LSR) method does not consider the closure process of primary fractures, it underestimates the value of σ_ci. The method of crack volume strain (CVS) or moving point regression (MPR) is recommended to calculate the σ_ci of hard rock. The EDR method overestimates the value of σ_cd. The method of CVS or MPR is recommended to identify the σ_cd of hard rock. Full article

Magnetic hysteresis strongly influences energy dissipation and efficiency in power magnetic components under time-varying excitation. This work proposes a compact dynamic hysteresis model using a Hammerstein structure, consisting of a closed-form arctangent static operator followed by a first-order relaxation dynamic stage. The formulation enables direct datasheet-based parameterization and avoids iterative differential solvers or distributed hysteron representations, resulting in low calibration effort and computational cost. The static hysteresis behavior is characterized using four static parameters directly identified from manufacturer B-H datasheets, while dynamic effects are captured using two global calibration parameters derived from datasheet loss curves. This formulation enables accurate reconstruction of major and minor hysteresis loops, while introducing frequency-dependent phase lag and dynamic loop opening. Model performance is evaluated under diverse excitations, including sinusoidal, amplitude-modulated, FORC and chirp signals, showing waveform deviations below 7.2% peak-to-peak NRMSE relative to classical hysteresis models. Energy-loss predictions are validated against manufacturer datasheet curves for ferrite material 3C90 across multiple frequencies, yielding a root-mean-square relative error of 8.3% with 89% of operating points within ±20% deviation. The proposed model provides a datasheet-driven framework for hysteresis and energy-loss prediction in power magnetic components. Full article

The aerodynamic characteristics of wingsails on unmanned surface vessels (USVs) play a crucial role in enhancing propulsion performance. Two-dimensional wingsail airfoils of owl wings, merganser wings, seagull wings, and teal wings were obtained through biomimetic design. Then a numerical investigation was conducted on the four biomimetic airfoils using the SST k-ω turbulence model to evaluate their aerodynamic performance. The results demonstrate that the bionic merganser airfoil exhibits the most superior lift performance, achieving a maximum lift coefficient of 3.21 across angles of attack ranging from 0° to 60° among the four biomimetic wingsails, and the bionic seagull airfoil is second, while the bionic teal airfoil shows the weakest lift characteristics. As the angle of attack increases, flow separation emerges at the trailing edge of the biomimetic airfoils, leading to the formation of separation vortices. For example, the backflow zone on the suction surface of the biomimetic merganser wingsail, caused by unsteady flow, persists at an angle of attack of 16 degrees. The vortex structure at the trailing edge of the biomimetic merganser wingsail periodically generates, develops, detaches, and dissipates, which affects the backflow of the suction surface of the wingsail and interferes with its lift coefficient. The study provides an excellent reference for selecting high-performance USV wingsails. Full article

Dispensing-based in situ casting offers a practical route for introducing dense or mechanically distinct polymer regions into fused deposition modeling (FDM) parts during fabrication. This study investigates the curing-dependent process constraints governing stable integration of in situ casting within an FDM workflow. In the proposed process, FDM is used to fabricate thermoplastic confinement geometries, after which liquid polymer is dispensed into selected cavities and cured before printing resumes. Two representative curing systems were examined: a UV-curable photopolymer and a two-component epoxy resin. The experimental program included UV curing characterization under perpendicular 405 nm exposure, infrared thermal imaging of curing-induced heat generation and dissipation, confined curing of epoxy resin, layer-wise integration within an FDM-printed cavity, and a representative mechanical linkage demonstration. The results show that UV-based in situ casting is constrained by the coupled effects of curing depth, peak temperature, and visible deformation, making staged curing with intermediate thermal relaxation necessary for stable operation. In contrast, the epoxy system enabled bulk cavity filling with lower peak temperature, but required substantially longer curing time, introducing a different process limitation. A layer-wise UV curing strategy enabled successful stacking of four cast layers within an FDM-printed confinement without visible leakage or shell collapse. Mechanical testing of hybrid linkage specimens further showed that localized casting can modify structural stiffness through selective reinforcement. These findings demonstrate that dispensing-based in situ casting can be integrated into FDM when thermal, temporal, and filling constraints are explicitly managed, and they provide practical process guidance for hybrid polymer fabrication involving confined casting during printing. Full article

This study evaluated the effects of different light-emitting diode (LED) spectral qualities on the early growth of kale at the baby-leaf harvest stage in a plant factory with artificial lighting (PFAL) by integrating morphological traits, biomass accumulation, plant quality indices, vegetation indices, and chlorophyll a fluorescence. Two kale (Brassica oleracea L.) cultivars, ‘Jellujon’ and ‘Manchoo Collard’, were grown for four weeks under monochromatic red, green, and blue LEDs, a purple composite LED with far-red wavelengths, and three white LEDs with different correlated color temperatures (3000, 4100, and 6500 K). Blue LED increased shoot height by approximately 14–28%, depending on cultivar and comparison among the white LED treatments, but this elongation did not translate into superior biomass production. In contrast, white LEDs, particularly at 3000–4100 K, increased leaf area to 24.2–24.9 cm² and SPAD units to 47.3–50.2, whereas blue or green LEDs generally resulted in smaller leaves and lower SPAD units. Shoot dry weight under 3000–4100 K white LEDs reached 0.25–0.26 g in ‘Jellujon’ and 0.26–0.29 g in ‘Manchoo Collard’, approximately twofold higher than under blue or green LEDs. Compactness, Dickson quality index, root investment ratio, and leaf efficiency index were also more favorable under white LEDs, indicating improved plant sturdiness and structural stability. Green LED light was associated with lower maximum photochemical efficiency (Φ_Po) and greater energy dissipation (Φ_Do and DI_o/RC), whereas photochemical reflectance index and PI_ABS tended to be more favorable under selected white LED treatments, although these responses were partly cultivar- and treatment-dependent. Taken together, among the LED spectral quality treatments tested, 3000–4100 K white LEDs provided the most consistently favorable conditions for producing structurally robust, high-quality kale at the early growth stage in PFAL systems. The purple LED showed partial advantages in leaf development and selected physiological responses, but these effects were less consistent across cultivars and indices. Full article

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems. Full article

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article

The integration of shape-memory alloy (SMA) wires into composite laminates offers a promising approach for active vibration damping. Towards this goal, this study investigates the damping behavior of hybrid random mat E-glass/epoxy composite plates with embedded SMA wires under electrically active and inactive conditions. The composites are tested using a Laser Doppler Vibrometer (LDV) and an impact hammer to assess the effect of SMA wire activation on the natural frequencies and vibration behavior of composites. For a fixed number of active SMA wires, differences in vibration behavior are evaluated between outer- and inner-wire activation configurations in both two-ply and four-ply composite plates. The results show that SMA wire activation significantly affects damping behavior, while the mode shapes remain unchanged. The magnitude and frequency of the first natural frequency as well as the quality factor (Q-factor) decrease in composites with activated SMA wires compared to the inactive configuration, indicating enhanced energy dissipation. Under the fully active condition, a reduction in vibrational amplitude of approximately 42–60% and a frequency shift of approximately 10–17% are observed. Compared to outer-wire activation, inner-wire activation results in greater reductions in vibration magnitude, reaching approximately 7–13%. Full article

Artificial intelligence (AI) is rapidly reshaping firms’ production and organisational processes, yet whether it can serve as a driving force for corporate green transformation remains an open question. Using a sample of Chinese listed manufacturing firms from 2012 to 2023, this study systematically examines the relationship between AI and firms’ green total factor productivity (GTFP), and explores potential underlying mechanisms. At the theoretical level, drawing on the task-driven nature of AI as a form of technological innovation, this study proposes that AI may enhance GTFP through two channels, namely the structural labour reallocation effect and the managerial dissipation reduction effect. The empirical results show the following: (1) Firms’ AI technical level is significantly associated with improvements in GTFP. (2) Mechanism tests indicate that AI is significantly related to an increasing share of creative task employees and a declining share of structural task employees, thereby providing empirical evidence for the structural labour reallocation effect. Moreover, from four dimensions, including information dissipation, resource allocation dissipation, process coordination dissipation, and incentive and learning dissipation, this study provides supportive evidence that AI is linked to reduced managerial dissipation. (3) Heterogeneity analysis suggests that this association is more pronounced among firms with greater scope for green improvement, such as non-heavily polluting firms and those characterised by managerial myopia. Overall, this study deepens the understanding of the relationship between AI and GTFP from the perspectives of labour structure and corporate organisation, and emphasises that AI’s contribution to firms’ GTFP is more likely to arise as a systemic facilitation embedded in production and organisational processes, rather than through the direct substitution of specialised green technologies. Full article