Search Results (18,224)

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented separately, even though they share common unsteady vortex dynamics. Accordingly, we adopt a unified unsteady-aerodynamic perspective to relate propulsion and energy-extraction regimes within a common framework and to clarify their operational duality. Within this unified framework, the feathering parameter provides a theoretical delimiter between momentum transfer and kinetic energy extraction. A critical analysis of experimental foundations demonstrates that while passive structural flexibility enhances propulsive thrust via favorable wake interactions, synchronization mismatches between deformation and peak hydrodynamic loading constrain its benefits in power generation. This review extends the analysis to complex and non-homogeneous environments and identifies that density stratification fundamentally alters the hydrodynamic performance. Specifically, resonant interactions with the natural Brunt–Väisälä frequency of the fluid shift the optimal kinematic regimes. The present study also surveys computational methodologies and highlights a paradigm shift from traditional parametric sweeps to high-fidelity three-dimensional (3D) Large-Eddy Simulations (LESs) and Deep Reinforcement Learning (DRL) to resolve finite-span vortex interconnectivities. Finally, this review outlines the critical pathways for future research. To bridge the gap between computational idealization and physical reality, the findings suggest that future systems prioritize tunable stiffness mechanisms, multi-phase environmental modeling, and artificial intelligence (AI)-driven digital twin frameworks for real-time adaptation. Full article

Ensuring affordable and reliable electricity access to areas with low population density is challenging, as network sparsity and lower connectivity rates can make it nearly impossible for electric utilities to cover the cost of interconnection without raising electricity tariffs. Utility providers that consider extending their networks to remote households must balance multiple and often conflicting objectives, including investment cost, grid resilience, geographical coverage, and environmental impacts. In this paper, a multi-objective decision-making framework is proposed for the electrification of rural households, considering traditional distribution network extension as well as microgrid deployment. In order to condense a wide range of spatial inputs into a tractable problem, a multi-criteria decision-making approach is adopted to identify and rank candidate sites for microgrid deployment that offer superior performance over a variety of technical, environmental, and economic criteria. A novel optimization model is then proposed using multi-objective Chebyshev goal programming, in which project costs, environmental impacts, and energy justice criteria are jointly optimized. The applicability of this framework is demonstrated through a case study of the Shiprock region within the Navajo Nation. The results indicate that the proposed methodology provides a balanced trade-off among conflicting objectives and identifies a priority order of loads to energize first under marginally increasing budgets. Full article

This study proposes an analytical redundancy method that combines empirical models with a Kalman filter to ensure the reliability of measurements from axial deformation sensors in a turbine head-cover bolt-monitoring system. This integration enables the development of predictive models that optimally estimate the dynamic deformation of each bolt during turbine operation at full and partial load. The test results of the models under conditions of outliers, measurement noise, and changes in turbine operating mode, evaluated using accuracy and sensitivity metrics, confirmed their high accuracy (Acc ≈ 0.146 µm) and robustness (S_A < 0.001). The evaluation of the models’ responses to simulated sensor faults (offset, drift, precision degradation, stuck-at) revealed characteristic residual patterns for faults with magnitudes > 5 µm. These findings establish the foundation for developing a fault detection and isolation algorithm for continuous monitoring of these sensors’ operational health. For practical implementation, the models require validation across all operational modes, and maximum admissible deformation thresholds must be defined. Full article

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation. Full article

Traditional flexible polyurethane (PU) foams frequently exhibit limited mechanical support and suboptimal moisture–heat regulation, which can compromise the microenvironmental comfort required for high-quality sleep. In this study, natural luffa fibers (LF) were incorporated as a microstructural modifier to simultaneously enhance the mechanical and moisture–heat regulation performance of PU foams. PU/LF composite foams with varying LF loadings were prepared via in situ polymerization, and their foaming kinetics, cellular morphology evolution, and physicochemical characteristics were systematically investigated. The results indicate that LF functions both as a reinforcing skeleton and as a heterogeneous nucleation site, thereby promoting more uniform bubble formation and controlled open-cell development. At an optimal loading of 4 wt%, the composite foam developed a highly interconnected porous architecture, leading to a 7.9% increase in tensile strength and improvements of 19.4% and 22.6% in moisture absorption and moisture dissipation rates, respectively, effectively alleviating the heat–moisture accumulation typically observed in unmodified PU foams. Ergonomic pillow prototypes fabricated from the optimized composite further exhibited enhanced pressure-relief performance, as evidenced by reduced peak cervical pressure and improved uniformity of contact-area distribution in human–pillow pressure mapping, together with an increased SAG factor, indicating improved load-bearing adaptability under physiological sleep postures. Collectively, these findings elucidate the microstructural regulatory role of biomass-derived luffa fibers within porous polymer matrices and provide a robust material basis for developing high-performance, sustainable, and ergonomically optimized sleep products. Full article

As a cornerstone of China’s energy infrastructure, the coal mining industry relies heavily on the stability of its underground roadways, where the support of soft rock formations presents a critical and persistent technological challenge. This challenge arises primarily from the high content of expansive clay minerals and well-developed micro-fractures within soft rock, which collectively undermine the effectiveness of conventional support methods. To address the soft rock control problem in China’s Longdong Mining Area, an integrated material–structure control approach is developed and validated in this study. Based on the engineering context of the 3205 material gateway in Xin’an Coal Mine, the research employs a combined methodology of micro-mesoscopic characterization (SEM, XRD), theoretical analysis, and field testing. The results identify the intrinsic instability mechanism, which stems from micron-scale fractures (0.89–20.41 μm) and a high clay mineral content (kaolinite and illite totaling 58.1%) that promote water infiltration, swelling, and strength degradation. In response, a novel synergistic technology was developed, featuring a high-performance grouting material modified with redispersible latex powder and a tiered thick anchoring system. This technology achieves microscale fracture sealing and self-stress cementation while constructing a continuous macroscopic load-bearing structure. Field verification confirms its superior performance: roof subsidence and rib convergence in the test section were reduced to approximately 10 mm and 52 mm, respectively, with grouting effectively sealing fractures to depths of 1.71–3.92 m, as validated by multi-parameter monitoring. By integrating microscale material modification with macroscale structural optimization, this study provides a systematic and replicable solution for enhancing the stability of soft rock roadways under demanding geo-environmental conditions. Soft rock roadways, due to their characteristics of being rich in expansive clay minerals and having well-developed microfractures, make traditional support difficult to ensure roadway stability, so there is an urgent need to develop new active control technologies. This paper takes the 3205 Material Drift in Xin’an Coal Mine as the engineering background and adopts an integrated method combining micro-mesoscopic experiments, theoretical analysis, and field tests. The soft rock instability mechanism is revealed through micro-mesoscopic experiments; a high-performance grouting material added with redispersible latex powder is developed, and a “material–structure” synergistic tiered thick anchoring reinforced load-bearing technology is proposed; the technical effectiveness is verified through roadway surface displacement monitoring, anchor cable axial force monitoring, and borehole televiewer. The study found that micron-scale fractures of 0.89–20.41 μm develop inside the soft rock, and the total content of kaolinite and illite reaches 58.1%, which is the intrinsic root cause of macroscopic instability. In the test area of the new support scheme, the roof subsidence is about 10 mm and the rib convergence is about 52 mm, which are significantly reduced compared with traditional support; grouting effectively seals rock mass fractures in the range of 1.71–3.92 m. This synergistic control technology achieves systematic control from micro-mesoscopic improvement to macroscopic stability by actively modifying the surrounding rock and optimizing the support structure, significantly improving the stability of soft rock roadways. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

This study shows an optimal model to estimate the minimum area in contact with the soil for an EIF (elliptical isolated footing), assuming that the partially compressed area, that is, part of the surface below the base in contact with the ground, is compressed, and the other part is not compressed (the pressure of the ground is linear). There are works that show the minimum area for an elliptical isolated footing, but the surface below the base in contact with the ground is fully compressed. The model is developed by integration to determine the equations of the axial load and the two moments (X and Y axes) for the two cases. Two numerical studies are presented: Study 1 considers that the axial load varies, and the moments are equal and remain constant; Study 2 considers that the axial load varies, and the moments are different and remain constant. Two comparisons are also made with the model proposed by other authors (fully compressed area) and the new model (partially compressed area): In the first study, it is assumed that axial load and moment about the X-axis remain constant and moment about the Y-axis is variable; in the second study, it is assumed that the two moments remain constant and the axial load is variable. The results show that significant savings of up to 59.30% can be achieved in the first study and up to 65.67% in the second study in the area of contact with the ground. Another comparison is made between rectangular isolated footings and EIFs; the results indicate that savings of up to 63.18% can be achieved using EIFs. Therefore, this article will be of great help to specialists in foundation engineering. Full article

Structural failure of the lead-carbon battery casing under external loads poses a serious threat to the safety of its energy storage function. To overcome the limitations of traditional protective casings regarding specific energy absorption (SEA) and crush force efficiency (CFE), this study proposes a novel thin-walled protective structure utilizing graded aperture honeycomb sandwich panels fabricated via additive manufacturing (AM). Finite element (FE) models were established using HyperMesh and validated against experimental data. Subsequently, the impact resistance and energy absorption characteristics of four distinct cellular topologies were systematically investigated under varying pore-size gradients, impact directions, and velocities. Experimental and numerical simulation results indicate that, among the investigated configurations, the triangular honeycomb structure exhibits superior impact resistance and energy absorption capability under both axial and lateral loading conditions. Furthermore, the synergistic enhancement mechanism based on topological configuration and gradient design effectively optimizes the progressive crushing mode, thereby reducing the initial peak crushing force transmitted to the battery and resulting in a pronounced advantage in impact performance. This research provides a novel design approach for optimizing next-generation high-performance, lightweight protection systems for energy storage devices. Full article

A major objective of this study is to investigate the incorporation of hydroxyapatite nanoparticles (nHA) in a biopolymeric matrix of alginate (Alg) and gelatin (Gel), with particular emphasis understanding how controlled variation in nHA concentration affects rheological, mechanical, printing, and biological performance. Although Alg–Gel blends and nHA-containing hydrogels have been previously explored, a systematic and quantitative correlation between nHA loading, viscoelastic recovery, yield behavior, filament fidelity, and cell viability under optimized bioprinting conditions has not been established. Here, we address this by preparing and evaluating six composite inks (0, 1, 2, 3, 4, and 5% w/v nHA). The parameters of interest included the printing accuracy, the rheological profile, including over 70% viscosity recovery after 10 s in almost all formulations, the elastic modulus, which was over 10 kPa, and the swelling degree. In addition, pre-osteoblastic cells were embedded in these formulations, subsequently bioprinted, and demonstrated viability over 70% after 7 days. The results advance our understanding on the effect of the chemical composition behind the modification of the properties of the composite materials and their applications for biofabrication. This work contributes quantitative insight into how compositional tuning influences the performance of alginate–gelatin–nHA bioinks for extrusion-based bioprinting applications. Full article

In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn–Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios. Full article

This study investigates the influence of the additive illite on the thermal, tribological, and energy efficiency characteristics of calcium grease (CG) at different concentrations (0.05 wt.%, 0.1 wt.%, 0.2 wt.%, 0.4 wt.%, 0.6 wt.%, and 0.8 wt.%). Thermo-gravimetric analysis under inert and oxidative atmospheres revealed that illite enhances thermal stability by increasing inorganic residue under N₂, but promotes oxidative degradation under O₂, limiting practical thermal use to around 400 °C. Grease with 0.1 wt.% illite (CGI2) performed well in tribological tests by reducing the coefficient of friction and wear scar diameter by 53% and 57%, respectively, compared to the base grease. Fleischer’s energy-based wear model showed that all grease samples operated within the mixed friction regime, and CGI2 exhibited a 93% higher apparent frictional energy density and a substantially lower wear intensity that was 47% lower than the base grease, indicating improved energy dissipation and wear resistance. All samples had the same weld load (1568 N), but CGI2 had a 21% higher load–wear index than the base grease in the extreme-pressure test, indicating better load-carrying capacity. In the energy consumption test, a 6% reduction in current consumption was observed in CGI2 in comparison with the base grease. Overall, illite at an optimal concentration significantly enhances lubrication performance, wear protection, and energy efficiency. Full article

Deep reinforcement learning (DRL) with self-play has emerged as a promising paradigm for solving combinatorial optimization (CO) problems. The recently proposed Gumbel AlphaZero Plan-to-Play (GAZ PTP) framework adopts a competitive training setup between a learning agent and an opponent to tackle classical CO tasks such as the Traveling Salesman Problem (TSP). However, in complex and multi-constrained environments like the Electric Vehicle Routing Problem (EVRP), standard self-play often suffers from opponent mismatch: when the opponent is either too weak or too strong, the resulting learning signal becomes ineffective. To address this challenge, we introduce Two-Stage Self-Play GAZ PTP (TSS GAZ PTP), a novel DRL method designed to maintain adaptive and effective learning pressure throughout the training process. In the first stage, the learning agent, guided by Gumbel Monte Carlo Tree Search (MCTS), competes against a greedy opponent that follows the best historical policy. As training progresses, the framework transitions to a second stage in which both agents employ Gumbel MCTS, thereby establishing a dynamically balanced competitive environment that encourages continuous strategy refinement. The primary objective of this work is to develop a robust self-play mechanism capable of handling the high-dimensional constraints inherent in real-world routing problems. We first validate our approach on the TSP, a benchmark used in the original GAZ PTP study, and then extend it to the multi-constrained EVRP, which incorporates practical limitations including battery capacity, time windows, vehicle load limits, and charging infrastructure availability. The experimental results show that TSS GAZ PTP consistently outperforms existing DRL methods, with particularly notable improvements on large-scale instances. Full article

Aiming at the insufficient capture of temporal dependence and weak coupling of external factors in multivariate load forecasting, this paper proposes a Transformer model integrating timestamp-based positional embedding and convolution-augmented attention. The model enhances temporal modeling capability through timestamp-based positional embedding, optimizes local contextual representation via convolution-augmented attention, and achieves deep fusion of load data with external factors such as temperature, humidity, and electricity price. Experiments based on the 2018 full-year load dataset for a German region show that the proposed model outperforms single-factor and multi-factor LSTMs in both short-term (24 h) and long-term (cross-month) forecasting. The research results verify the model’s accuracy and stability in multivariate load forecasting, providing technical support for smart grid load dispatching. Full article