Search Results (1,717)

This study examines the influence of internal infill geometry, infill density, and short-term mineral oil exposure on the tensile and microstructural behavior of Fused Deposition Modeling (FDM) 3D-printed Polylactic Acid (PLA) and Carbon-Fiber-Reinforced PLA (PLA+CF). Standardized ISO 527-2 specimens were fabricated using linear, triangular, and hexagonal infill patterns at 30%, 60%, and 100% densities, followed by seven-day immersion in mineral oil. Mechanical testing and quantitative optical image analysis were performed to correlate porosity characteristics with tensile response. For PLA, the linear 30% infill achieved the highest tensile strength (31.5 MPa), while the hexagonal pattern exhibited the greatest ductility (ε = 4.9%). Oil exposure caused slight reductions in strength (−1.2%) and modulus (−4.1%) but increased elongation by 76%, indicating mild matrix plasticization. For PLA+CF, tensile strength and stiffness increased with density, reaching 33.4 MPa and 500 MPa at 100% infill, while oil exposure enhanced strength by 6.9% and reduced the average pore size from 475 µm² to 146 µm². Overall, the results demonstrate that optimizing infill topology, density, and fiber reinforcement significantly improves load transfer efficiency and environmental stability. These findings establish quantitative correlations between pore morphology and tensile behavior, providing a framework for the predictive design of environmentally resilient FDM polymer–composite components for semi-lubricated or tribological applications. Full article

With the increasing integration of distributed energy resources and the growing variability of multiple loads, distribution networks face significant uncertainties in measurement data, line parameters, and topology. Traditional state estimation methods, such as weighted least squares, rely on accurate network parameters and are therefore highly sensitive to measurement noise and topology variations. To address these challenges, this work proposes a comprehensive data-driven framework for ADN state estimation that features a novel integration of an improved deep residual network (i-ResNet) and transfer learning. An improved deep residual network (i-ResNet) is developed to enable fast and robust state estimation without dependence on online parameters, even under uncertain data conditions. Furthermore, a transfer learning–based model is introduced to accommodate topology changes by leveraging historical data from multiple network configurations. Experimental studies on the IEEE 33-bus and 118-bus test systems are conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method achieves higher accuracy and faster convergence than conventional techniques, with voltage magnitude errors consistently maintained below 1%. Full article

This study proposes a scalable cyber–physical system (CPS) framework utilizing a hierarchical five-layer architecture to enhance indoor environmental quality and energy efficiency. The methodology integrates a Random Forest-based predictive model trained on a 22-month longitudinal dataset (2024–2025) to separate climatic effects from occupancy-driven loads. This study prioritized the development of a high-precision and cost-effective monitoring architecture to address the persistent challenge of sustaining thermal comfort in subtropical academic laboratories. The proposed system achieved a validation mean absolute percentage error (MAPE) of 2.50%, indicating strong predictive reliability. Hardware expenditures were below USD 400, substantially reducing barriers to broader adoption. Field deployment confirmed an operational EUI of 188.6 kWh/m²·year, which is 28.5% lower than prevailing regional benchmarks, while consistently meeting stringent indoor air quality (IAQ) requirements. Additionally, simulation modules calibrated with the validated dataset indicated a further 15–20% reduction potential through the application of active control strategies. Collectively, these findings establish a transferable empirical reference for climate-responsive operational practice. Full article

The complex changes in fluid phase behavior during the CO₂ fracturing process result in significantly different temperature-pressure coupling characteristics compared to hydraulic fracturing. The complex temperature-pressure changes make it difficult to obtain a rapid and effective evaluation between fracturing parameters and string safety. To solve this problem, considering the flow and heat transfer of CO₂ and the change of phase state, and then considering the deformation of string load under the constraint of packer, this study established the thermal fluid mechanical coupling analysis model of CO₂ fracturing process, realized the dynamic analysis of string load in the whole process of fracturing, systematically revealed the evolution law of string stress in the process of fracturing, and provided theoretical basis and technical support for the optimization of CO₂ fracturing process parameters and the safety design of string. The research results show that with the fracturing process, the temperature, pressure, and flow rate distribution of the medium in the wellbore have significant nonlinear characteristics, and the string load increases slowly at first and then increases rapidly. The reduction of CO₂ fracturing temperature or the increase of pressure will significantly increase the string load. The findings provide direct theoretical and technical support for optimizing CO₂ fracturing parameters and ensuring tubing safety in engineering practice. Full article

Wireless power transfer is gaining attention in medium-to-short-range applications such as 1–3 kW-class UAVs and AGVs due to its safety, reliability, and adaptability to complex environments. The LCC-S topology is widely adopted due to its favorable output characteristics and device voltage-stress distribution. However, under fixed coil parameters and operating frequencies, conventional LCC-S achieves high efficiency only near the optimal equivalent load. When the actual load deviates from this value—especially in heavy-load regions—resonant cavity current increases sharply, voltage gain drops significantly, and overall efficiency deteriorates. To overcome this structural limitation without increasing control complexity or adding active regulation stages, this paper proposes a transformer-assisted LCC-S wireless charging topology based on “equivalent load reconstruction.” First, a unified equivalent circuit is constructed to derive analytical expressions for voltage gain, input impedance, and efficiency under arbitrary coupling coefficients and loads for both the traditional LCC-S and the proposed topology, revealing the mechanism behind efficiency degradation under heavy loads. Building upon this foundation, a high-frequency transformer is introduced, with an efficiency-oriented collaborative design method for its turns ratio and excitation inductance. Furthermore, by integrating simplified copper and iron-loss models, the losses in the resonant cavity and the transformer are decomposed and evaluated. Results demonstrate that when transformer parameters are appropriately selected, the newly introduced transformer losses are significantly smaller than the resonant cavity losses reduced through load reconstruction. The constructed 1 kW, 85 kHz prototype demonstrates that within the 0.5–2.5 Ω load range, the proposed topology achieves efficiency exceeding 88%. Under typical heavy-load conditions, its peak efficiency surpasses that of the conventional LCC-S by approximately 20%. The theoretical analysis, simulation, and experimental results are highly consistent, verifying that the transformer-assisted LCC-S topology and its efficiency-oriented design method can effectively expand the high-efficiency operating range across a wide load spectrum without altering the control strategy. This provides a concise and feasible structural optimization solution for wireless charging systems. Full article

Energy transition demands cleaner and more efficient marine engines, accelerating the development of reactivity-controlled compression ignition (RCCI) concepts with multi-fuel capability. However, the coupling between combustion behavior and thermal loading in RCCI engines remains insufficiently understood due to limited experimental capabilities and the absence of integrated modeling tools. This study develops a rapid predictive framework that dynamically couples an in-house chemical-kinetics solver with a GT-Suite engine model and a finite-element wall thermal solver. The framework was calibrated against measurements from a single-cylinder research engine representative of the Wärtsilä 31DF medium-speed NG/LFO RCCI engine. It accurately captured component temperatures and combustion/performance parameters with RMS errors below 5% and cycle times under four minutes. The results show that RCCI operation introduces pronounced component temperature variations across the load range, creating challenges for thermal management and combustion control. Low-load combustion inefficiencies were linked to cylinder head thermal design rather than the conventional flame-quenching explanation. At high load, excessive pressure-rise rates amplified heat transfer demands, with exhaust-valve temperatures exceeding 780 K and posing pre-ignition risks. Increasing coolant temperature by 40 K reduced methane slip by 10% and advanced combustion by nearly 2 CAD, improving efficiency at low load, while coordinated lambda/fuel-blend control lowered peak combustion temperature by ~200 K at high load, mitigating thermal-induced pre-ignition without compromising performance or emissions. Full article

Load forecasting plays a vital role in key areas such as energy forecasting and resource management. Traditional forecasting methods are often limited in dealing with shifts in statistical distribution and dynamic changes in periodic parameters of load data, making it difficult to capture complex temporal dependencies and periodic change patterns. To address this challenge, we transform load data into the frequency domain and use a fine-tuned large language model for forecasting. Specifically, we propose the Frequency-Based Spatial–Temporal Mixture Learning Model (FSTML), which uses (1) a Frequency-domain Global Learning Module (FGLM), (2) Temporal-Dimension Learning Module (TDLM), and (3) Spatial-Dimension Learning Module (SDLM) to process load data and extract comprehensive temporal patterns. FGLM transfers load data to the frequency domain and provides the model with a frequency-domain global feature representation of load data. The TDLM and SDLM fine tune the pre-trained large language model in the time dimension and space dimension, respectively, extracting the temporal dependency and spatial dependency of load data, respectively, thereby extracting the spatial–temporal pattern of load data. FSTML achieves the best performance in the forecasting task on two public load datasets, and the forecasting accuracy is significantly improved. The high-precision load forecasting model proposed in this study can significantly improve the operational efficiency of power systems and the integration capacity of renewable energy sources, thereby supporting the sustainable development of the power industry in three dimensions: energy optimization, emission reduction, and economic operation. Full article

Asymmetric friction, that is, different friction forces resisting sliding in opposing directions, works as a rectifier, transferring the applied oscillations into unidirectional motion. Locomotion of devices based on asymmetric friction is investigated by considering a model system consisting of an asymmetric friction block connected to a symmetric friction block by a spring. The symmetric friction block models the resistance to the movement by the environment. It is found that under harmonic oscillation, the system displays two distinct types of motion: Recurrent Movement (stick-slip-type movement) and Sub-Frictional Movement. The Recurrent Movement occurs when the inertia force is sufficient to overcome the frictional force. In this case, the system with asymmetric friction exhibits unidirectional locomotion, while the system with only symmetric friction oscillates about a fixed point. The Sub-Frictional Movement occurs when the inertia is insufficient to overcome the frictional force. Then the symmetric friction block moves against the asymmetric friction block and sufficiently loads the spring to enable some movement of the system. Thus, motion is generated even when the external forces are below the static friction threshold. These types of motion have been found to exhibit different types of spectral fallout: while the Recurrent Movement produces a typically observed frictional fallout 1/ω, where ω is the frequency, the Sub-Frictional Movement produces a stronger 1/ω² fallout, only observed in the development of an oblique fracture in rocks under compression. This discovery can shed light on mechanisms of rock failure in compression. Understanding of the unidirectional movement induced by asymmetric friction can be instrumental in designing novel locomotion devices that can move in narrow channels or fractures in the Earth’s crust or in extraterrestrial bodies utilising the (renewable) energy of external vibrations. Full article

Planetary journal bearings are enablers for wind turbine gearbox torque density and reliability increase due to their compactness and potentially unlimited lifetime. They are designed to withstand the load conditions during wind turbine operation. Despite their general robustness, abnormal events such as particle contamination, strong overload or operation without sufficient oil supply may be harmful to the bearings. In these cases, damage can occur quickly and with little warning time. Such spontaneous failure leads to turbine downtime and cost-intensive repair work on the wind turbine drive train. Thus, reliable load and condition monitoring systems, which allow the detection of critical operating states before damage occurs, would be beneficial. For journal bearings in wind turbine gearboxes, no commercially available monitoring system exists to date. The existing studies on journal bearing condition monitoring are limited to experiments on component test rigs or small gearboxes, and their transferability to full-size systems has yet to be proven. This work presents the results of a system test with an 850 kW wind turbine gearbox equipped with planetary journal bearings and a novel condition monitoring system based on the measurement of surface acoustic waves. First, the journal bearing design, including the sensor setup, is explained. Second, the test campaign layout is presented. The gearbox is tested under load conditions specific to wind turbines, and the condition monitoring signals are examined in detail. An algorithm based on a machine learning model is presented for evaluating the monitoring signals and predicting the friction state of the bearings. Finally, the practical feasibility and quality of the monitoring approach for planetary journal bearings presented in this work is discussed. Full article

Robot grinding requires a constant interaction force between the tool and the workpiece, even under inclination changes. This paper proposes a compact single-axis pneumatic constant-force floating compensator (CFFC) to achieve constant force output. The proportional pressure valve and pressure sensor are used to regulate the cylinder’s pressure. Pneumatic components and sensors are integrated into the narrow space between the cylinder and the slide rail. Embedded controller, power, and communication modules are developed and integrated into a control box and interact with the operator by a touch screen. The mathematical models of the compensator are established and the stability and response dynamics are analyzed through transfer functions. A dual-loop force controller based on active disturbance rejection control (ADRC) is designed to address bias load, inclination change, friction, and the sealing cover spring effect. The outer loop is compensated by displacement, tilt, and pressure sensors, and the unmodeled dynamics are estimated by an extended state observer (ESO) and a recursive least square (RLS). Finally, the CFFC is installed on a testing platform to simulate grinding conditions. The experimental results show that even under large floating stroke, inclination changes, and biased load, the CFFC can still quickly and stably output the desired grinding force. Full article

This study employs a multilevel modeling approach to describe the deformation of specimens made from austenitic Wire Arc Additive Manufactured (WAAM) steel 308LSi. Two WAAM processing modes were investigated: (1) the Cold Metal Transfer (CMT) method and (2) Cold Metal Transfer combined with interlayer deformation strengthening (hammer peening/forging). Test specimens were cut from the deposited walls at 0° and 90° relative to the deposition direction. The grain and dendritic structures of the specimens were analyzed using optical stereomicroscopy. A statistical multilevel model has been developed, accounting for the features of the grain-dendritic and defect structures under various technological deposition modes. Parameter identification and model verification were conducted based on experimental data from uniaxial tensile tests of 308LSi steel specimens. The maximum deviation of the numerical results from the experimental data during the identification stage under uniaxial tensile loading did not exceed 3%, and during the verification stage it did not exceed 10%; the overall mean deviation did not exceed 1% for the identification stage and 2% for the verification stage. The model effectively captured the anisotropic mechanical behavior of WAAM-processed samples. The maximum calculated yield strength 360 MPa was obtained for specimens cut at an angle of 45°, while the minimum value 331 MPa was observed for vertically oriented specimens. Specimens subjected to interlayer forging (hammer peening) exhibited isotropic material properties. Explicit multilevel modeling, incorporating the presence of MnO oxide inclusions located within the austenite matrix, was performed. The results showed good correlation with experimental data and confirmed the localization of fatigue cracks at the phase boundary-matrix-oxide interface. Full article

The manipulator is a key component for harvesting citrus and other fruit crops. A study of the dynamic characteristics and energy consumption modelling of its joints is the foundation for optimising the manipulator’s load parameters and achieving efficient operation. To address the issues of the 6-DOF citrus-picking manipulator’s high degrees of freedom and complex structure, which lead to complex dynamic characteristics and an unclear energy transfer and consumption mechanism, the electromechanical coupling dynamics and energy consumption of the joint system are systematically studied using bond graphs. Firstly, the bond graph model is constructed by combining it with the joint system’s physical structure. On this basis, the corresponding dynamic characteristic state equation and energy consumption model are established. Secondly, the dynamic response and energy consumption characteristics of the joint system are analysed, revealing the system’s energy consumption and dynamic characteristics under different working conditions. Finally, the effectiveness and precision of the proposed model in describing the dynamic behaviour of the joint system and energy consumption are verified through experiments. The model provides a theoretical basis and a new research perspective for optimizing joint parameters, load solutions, and energy efficiency of the harvesting manipulator. Full article

Accurate quantification of ocean tide loading (OTL) is essential for sustainable coastal geodetic monitoring, infrastructure stability assessment, and the interpretation of GNSS vertical displacement time series. This study analyzes long-term vertical displacements observed at the Palmido GNSS station, located in Korea’s largest tidal-range environment, to resolve dominant semi-diurnal and diurnal tidal constituents. Coherent-gain–corrected Fast Fourier Transform (FFT) and continuous wavelet analysis were applied to decompose the GNSS time series, with particular emphasis on the principal lunar (M2) and principal elliptical lunar (N2) constituents. The extracted tidal amplitudes and phases were benchmarked against the NAO99 ocean tide loading model after applying load Love number (LLN) and site-scale corrections. Quantitative evaluation demonstrates that the corrected NAO99 predictions reduce the root mean square difference (RMSD) of the M2 constituent from approximately 14.5 mm to 13.3 mm (≈8% improvement) and that of the N2 constituent from about 2.1 mm to 1.2 mm (≈40% improvement), compared to uncorrected model outputs. Linear regression analyses further show that amplitude scaling improves toward unity for M2 after correction, while maintaining strong phase coherence. Continuous wavelet scalograms reveal persistent semi-diurnal energy with a clear fortnightly modulation, whereas diurnal components appear intermittently and are more sensitive to local environmental conditions. These results demonstrate that combining coherent-gain–corrected FFT, time–frequency wavelet diagnostics, and physics-based NAO99 benchmarking significantly enhances the reliability and interpretability of GNSS-derived tidal loading estimates. The proposed workflow provides a transferable and reproducible framework for high-precision coastal deformation monitoring and long-term sustainability assessments in macrotidal environments. Full article

Triboelectric nanogenerators (TENGs) offer intrinsically high open-circuit voltages in the kilovolt range; however, conventional diode rectifier interfaces clamp the voltage prematurely, restricting access to the high-energy portion of the mechanical cycle and preventing delivery-centric control. This work develops a unified physical basis for contact–separation (CS) TENGs by confirming the consistency of the canonical $V_{oc}$–$C_s$ relation with a dual-capacitor energy model and analytically establishing that both terminal voltage and storable electrostatic energy peak near maximum plate separation. Leveraging this insight, a self-powered gas-discharge-tube (GDT) rectifier bridge is devised to replace two diodes and autonomously trigger conduction exclusively in the high-voltage window without auxiliary bias. An inductive buffer regulates the current slew rate and reduces $I^{2}R$ loss, while the proposed topology realizes two decoupled power rails from a single CS-TENG, enabling simultaneous sensing/processing and actuation. A low-power microcontroller is powered from one rail through an energy-harvesting module and executes an operator-based nonlinear controller to regulate the actuator-side rail via a MOSFET–resistor path. Experimental results demonstrate earlier and higher-efficiency energy transfer compared with a diode-bridge baseline, robust dual-rail decoupling under dynamic loading, and accurate closed-loop voltage tracking with negligible computational and energy overhead. These findings confirm the practicality of the proposed self-powered architecture and highlight the feasibility of integrating operator-theoretic control into TENG-driven rectifier interfaces, advancing delivery-oriented power extraction from high-voltage TENG sources. Full article

Static voltage stability (SVS) is a critical aspect of the safe and efficient operation of electrical power systems (EPS), as it reflects the system’s ability to maintain adequate voltage levels in the face of progressive increases in demand under steady-state conditions. Traditionally, improving SVS has been addressed by compensating reactive power using FACTS devices. However, this research introduces an alternative methodology based on the hybridization of transmission technologies, integrating HVAC and HVDC links in parallel, to increase the stability margin and optimize performance in the event of contingencies. The proposed methodology is based on the resolution of the optimal AC power flow (OPF-AC) and the analysis of P-V curves to evaluate the displacement of the critical collapse point. The validity of the approach was verified through simulations in the Generation-Infinite Busbar and IEEE 9-busbar models, using the DIgSILENT PowerFactory environment. The results obtained show significant improvements in the SVS margin: an increase of 4.6% in the infinite busbar generation system, 9.5% in the critical busbar of the IEEE 9-busbar system, and 7.6% in the critical busbar of the IEEE 30-busbar system. In addition, the hybrid scheme showed a 17.1% reduction in real power losses and a more efficient redistribution of energy flows, which translates into a decrease in line load capacity. It should be noted that, under an N-1 contingency scenario, the hybrid system showed a 13.3% improvement in maximum power transfer before collapse, confirming its effectiveness under critical conditions. These findings position HVAC/HVDC hybridization as a robust and scalable alternative for strengthening voltage stability in modern electrical systems subject to operational variability. Full article