Search Results (353)

Monopiles are the predominant foundation type for offshore wind turbines (OWTs). Their diameters have increased substantially to accommodate larger structures, while current design approaches primarily rely on the API “p-y” model to simulate soil–pile interaction (SPI), which significantly underestimates the ultimate lateral pile capacity of large-diameter monopiles. Further, the API model accounts only for lateral soil resistance, neglecting mechanisms that substantially influence the lateral response of piles with low length-to-diameter (L/D) ratios, including pile toe shear, toe moment, and axial interfacial shaft friction. To address these problems, this study proposes a complete set of mechanism-aligned, spring-based SPI models capable of accurately simulating lateral pile response in sand across the full L/D spectrum typical of OWTs. The models include: a one-spring “p-y” model for flexible piles, capturing distributed lateral soil resistance; a two-spring “p-y + M_R-θ_R” model for semi-rigid piles, which additionally accounts for pile toe shear and bending moment resistance against rigid-body rotations; and a three-spring “p-y + M_R-θ_R + M_p-θ_p” model for rigid piles, which further includes rotational springs to account for distributed moment resistance due to rotation-induced shaft friction effects in sand. The derived spring parameter formulas have been calibrated using readily available engineering parameters, such as soil modulus, friction angle, and pile geometry. The three mechanism-aligned SPI models were validated against full-scale offshore monopile tests, centrifuge tests, and small-scale laboratory experiments, achieving less than 10% error in predicted pile capacities and less than 15% error in soil–pile coupled stiffness evolution. Full article

Understanding the spatial generation mechanisms of ride-hailing demand is crucial for sustainable urban mobility. However, existing literature largely assumes monocentric urban layouts and globally stationary spatial scales, often overlooking the severe topographical constraints inherent in “strip cities”. To bridge this gap, the present study proposes a novel dual-level analytical framework coupling the Spatially Embedded Laplacian Graph Partition (SE-LGP) algorithm with a Log-Gaussian Multiscale Geographically Weighted Regression (MGWR) model. Taking Jinan, China, as a quintessential strip city, we incorporate spatial penalties to decode its mobility dynamics. Macroscopically, we reveal that substantial topographic friction fragments the workday mobility network into a chain of 23 highly localized micro-circulations. This anisotropic friction results in a notable 41.70% intra-community retention rate, demonstrating that flexible mobility operates within confined functional basins rather than a unified citywide market. Microscopically, the MGWR uncovers significant multiscale spatial heterogeneity: the jobs–housing mismatch is strongly associated with demand at a global macro scale (bandwidth = 1335), whereas public transit integration operates predominantly at a localized micro scale (bandwidth = 44). Crucially, the interaction between topographical friction and infrastructure capacity unveils a highly localized pressure-valve effect (bandwidth = 46), indicating that physical road networks mitigate natural barriers strictly at a micro scale. Comparative analysis quantifies a “spatial fusion effect” during weekends; the relaxation of rigid tidal commuting reveals a structural invariance in built-environment scales (bandwidth = 1335), while the impact intensity of natural topographical friction undergoes a marked spatial inversion. This behavioral elasticity merges fragmented micro-circulations into larger regional communities ($k=20$). The findings indicate that flexible transit is strongly associated with scale-dependent and temporally elastic mechanisms. It provides insights for planners to transition from uniform city-wide fleet dispatching toward region-customized, temporally dynamic mobility management in topographically constrained metropolises. Full article

Driven by orchard labor shortages and rising demand for intelligent harvesting, automated apple picking requires a balance between conformal enveloping and slip-resistant stability. To reduce damage and slippage caused by fragile skins, variable morphologies, and motion disturbances, this study proposes a multi-joint pneumatic flexible apple-picking hand with adjustable circumferential configuration. Based on structural configuration determining grasping stability, six apple-morphology-based finger-base supports were designed. Parametric analysis of soft gripper cavities identified an isosceles trapezoidal profile as the best configuration. Using the Yeoh constitutive model, an equivalent joint model for conformal gripping was developed, and genetic algorithm (GA) optimization selected the four-joint design as the preferred configuration. Static finite element simulations determined an operating pressure of 20.32 kPa. Grasping stability was quantified by relative slip displacement in rigid–flexible coupled dynamic simulations. Among the tested support configurations within 60–110°, the 90° bracket produced the most stable slip response under vertical and horizontal disturbances. Thin-film pressure tests showed an asymmetric but stable three-finger load-sharing pattern. Field trials in a high-density dwarf spindle orchard achieved an 83.98% harvesting success rate. After 72 h of cold storage, no obvious surface browning, epidermal abrasion, or compression marks were observed during visual inspection. This assessment was limited to visible external damage and did not include quantitative evaluation of internal bruising, firmness degradation, flesh browning, or long-term storage quality. These results demonstrate stable grasping performance and low visible external damage under the tested conditions. Full article

This paper proposes an integrated attitude control framework for flexible spacecraft subject to external disturbances, rigid–flexible dynamic coupling, and actuator faults. The control framework combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with an adaptive fault-tolerant (AFT) compensator. First, a rigid–flexible coupling dynamic model is formulated using Modified Rodrigues Parameters. Second, an observer-based TD3 attitude controller is designed, where a hierarchical reward function incorporating the observer-estimated flexible modal displacement $\widehat{\mathit{\eta }}$ is constructed to train the agent for simultaneous attitude convergence and vibration suppression. Third, a composite fault-tolerant control structure is developed by integrating the trained TD3 policy with an adaptive sliding mode compensator that handles both partial loss-of-effectiveness faults and time-varying additive faults. The proposed framework is evaluated under a progressive five-scenario uncertainty evaluation framework encompassing measurement noise, parameter mismatch, external disturbances, and actuator faults. Simulation results demonstrate that (i) the $\widehat{\mathit{\eta }}$-augmented reward enables substantial improvements in vibration suppression over the baseline reward, achieving a better balance between pointing accuracy and vibration attenuation; (ii) under the most demanding fault scenario, the AFT compensator proves essential for precise convergence, and the composite TD3+AFT architecture achieves the best overall performance among the four compared control schemes. Full article

With the advancement of energy market reform, integrated energy systems (IESs) have achieved rapid development. Considering insufficient research on an electricity–heat coupled master–slave game and the local optimum defect of traditional algorithms, this paper proposes a Stackelberg game optimization strategy for IES considering integrated demand response (IDR), with microgrid operator (MGO) as the leader and load aggregator (LA) as the follower. Firstly, an IDR model containing rigid, shiftable electric loads and reducible thermal loads is established, and a bi-level game model is built: the upper MGO optimizes electricity and heat pricing to maximize profit, while the lower LA adjusts flexible loads for maximum consumer surplus. Secondly, an improved snake optimizer (ISO) is constructed via Hammersley sequence initialization, Lévy flight and random perturbation and combined with quadratic programming (QP) to form the ISO-QP hybrid solving method. Benchmark function and CEC2017 tests verify the superior convergence and stability of ISO against multiple classical intelligent algorithms. Case simulation obtains the Stackelberg equilibrium result, and repeated experiments and parameter sensitivity analysis verify model robustness. Results show that the proposed method smooths load fluctuations via price guidance and synchronously improves MGO revenue and LA consumer surplus on the premise of guaranteed user satisfaction. Full article

This study presents a computational and experimental aerodynamic characterisation of a full-scale 5.5 m diameter, six-sail horizontal-axis windmill of the traditional Cretan Lasithi type, equipped with flexible woven polyester sails that act as a passive load-control mechanism. Seventeen operating points spanning wind speeds of 2.3–18.3 m/s were simulated in OpenFOAM using a transient sliding-mesh Arbitrary Mesh Interface formulation with the k–ω SST turbulence closure on a 2.3 million cell grid, selected on the basis of a four-level grid convergence study. CFD simulations identify three distinct aerodynamic regimes: a drag-dominated high-TSR regime (λ > 2.1), a mixed lift–drag working range with peak loading near λ ≈ 1.4–1.5, and a deep-stall regime in which boundary-layer separation propagates from root to tip as λ falls below 1.0. Field measurements conducted at the Energy Systems Synthesis Lab of the Hellenic Mediterranean University in compliance with IEC 61400-12-1:2005(E) confirm that rotor speed stabilises passively at 55–58 RPM above 13 m/s without any active control mechanism; CFD predictions agree with measured power output within 8–12% across the 2–13 m/s attached-flow envelope. The combined evidence indicates that passive overspeed self-regulation is driven by aeroelastic sail deformation, reducing effective disc solidity at high wind speeds, a mechanism that rigid-geometry CFD correctly identifies in trend but cannot quantify in magnitude. The primary limitation of the present work is the rigid-sail assumption of the CFD model, which requires a two-way coupled fluid–structure interaction extension as a future step. Full article

With the rapid expansion of artificial intelligence infrastructure and cloud computing services, data centres are evolving from rigid electricity loads into flexible resources capable of contributing to renewable energy integration, grid regulation and cross-regional computing power allocation. Addressing the shortcomings in existing research regarding the differences between various types of computing tasks, the mechanisms of green migration under network constraints, and the characterisation of curtailment risks for renewable energy, this paper proposes a risk-aware collaborative optimisation and scheduling method for a power–computing–network–storage coupled system across remote data centres. Firstly, a hierarchical model of multi-type computing tasks is constructed, classifying data centre loads into fixed real-time tasks, online inference tasks, long-duration AI training tasks, and opportunistic elastic tasks, to characterise the differences between these tasks in terms of latency, time-shift, migration, and completion volume constraints. Secondly, a graph-attention-inspired green affinity prior is proposed, mapping grid topological distance, renewable energy availability, data centre PUE, and energy storage regulation capacity into interpretable migration signals, thereby guiding flexible computing power to migrate towards nodes with abundant green electricity and favourable grid support conditions. Subsequently, we introduce the CVaR metric to quantify the tail risk of renewable energy curtailment, establishing a multi-scenario stochastic linear optimisation model that incorporates DC power flow, unit output, renewable energy utilisation, campus energy storage, task SLAs, and cross-node migration constraints. A 24 h simulation based on the IEEE 10-machine, 39-node system demonstrates that the proposed method can reduce the expected curtailment volume from 176.939 MWh to 0 MWh, lower the CVaR curtailment risk from 694.085 MWh to 0 MWh, and increase the proportion of green computing power by 9.283 percentage points compared to the fixed-load baseline, whilst improving the five-tier collaborative score by 4.885 points. Full article

High-temperature temporary sealing operations require liquid plug materials that can be placed as low-viscosity precursors, converted into mechanically stable networks under reservoir temperature, and subsequently removed after service. Existing epoxy-based sealing systems generally provide high post-curing strength, but the coordination among pumpability, thermally triggered curing, and post-service degradability remains insufficiently addressed. In this work, an epoxidized soybean oil (ESO)-modified epoxy–anhydride liquid plug was designed to regulate these sequential stages within a single material system. The precursor formulation, rheological transition, curing kinetics, mechanical response, network structure, and degradation behavior were evaluated using viscosity monitoring, curing-time tests, DSC, compression testing, DMA, gel fraction and swelling measurements, FTIR, and high-temperature degradation experiments. The optimized precursor exhibited an initial viscosity of 65.4 ± 2.1 mPa·s, remaining below the pumpability threshold of 100 mPa·s before curing. Its curing time was adjustable within 1–10 h at 120–140 °C through temperature and initiator regulation. ESO incorporation produced a non-monotonic mechanical response, with the optimized network reaching a compressive strength of 112.5 ± 3.5 MPa and an elastic modulus of 142.50 ± 5.26 MPa. FTIR and thermal–mechanical analyses supported the formation of an ester-rich epoxy–anhydride network containing both rigid epoxy-derived segments and ESO-derived flexible chains. In the post-service stage, degradation was strongly temperature dependent, with the characteristic unsealing time decreasing from 84 h at 120 °C to 24 h at 130 °C and 18 h at 140 °C. The combined results define a coupled curing–degradation window in which pumpable placement, thermal network formation, load-bearing sealing, and controlled unsealing are temporally separated but structurally connected. Full article

Optimizing the structural stability of foundations is challenging in modern geotechnical engineering. This study investigated the mechanism of geocell-reinforced foundations through discrete element modeling based on transparent soil model tests. A three-dimensional particle flow code (PFC^3D) model was developed to investigate the micromechanical soil–geocell interactions in both unreinforced and geocell-reinforced foundations under strip loading. Particle displacement, contact force distribution, and structural deformation within the foundation system were analyzed to quantify the performance of geocell reinforcement. The results show that geocell inclusion enhances structural performance by 2.1 times compared to an unreinforced foundation, increasing the bearing capacity from 60.6 to 126.8 kPa at a defined bearing capacity criterion. The geocell walls act as rigid physical boundaries that microscopically intercept the lateral migration and horizontal extrusion of soil particles. The kinematic trajectories of soil particles beneath the loading plate are forced into a downward realignment, decreasing the displacement vector rotation angle from 42° in the unreinforced soil to 27° in the reinforced soil and effectively mitigating the heave of adjacent surfaces. Furthermore, the quasi-rigid three-dimensional network completely interrupts the continuous steep contact force chains inherent in unreinforced foundations. Concentrated vertical stresses are converted into horizontal components through interfacial friction and mechanical interlocking, resulting in the lateral redistribution of the applied load by a distance of approximately 0.06 m. The geocell–soil composite considered as a flexible raft foundation extends load dispersion and reduces average subsoil pressure. A coupled tension and compression stress state in the horizontal plane is developed within the geocell structure. Forces are channeled along rigid paths by elevated bending moments and stress concentrations at the cell junctions. These findings provide micromechanical insights into the performance of geocell-reinforced-foundation systems. Full article

Rapid advances in on-orbit servicing technologies have driven exponential growth in micro-spacecraft on-orbit ejection missions. Post-separation attitude disturbances are the dominant factor determining mission success, requiring accurate and rapid disturbance prediction. This study develops an efficient multi-rigid-body dynamic simulation framework for on-orbit ejection based on the simulation software ADAMS. Contact parameters between the micro-spacecraft and guide rail are calibrated against high-fidelity rigid–flexible coupled simulation results from the simulation software LS-DYNA, establishing a streamlined simulation pipeline. Using this validated framework, the effects of thrust misalignment angle, thrust eccentricity, and mass eccentricity on ejection-phase attitude disturbances are systematically quantified. Results demonstrate that the calibrated ADAMS multi-rigid-body model effectively substitutes computationally intensive rigid–flexible coupled models without sacrificing predictive accuracy. Specifically, constraining the axial thrust misalignment angle to ≤0.2°, axial thrust eccentricity to ≤0.4 mm, and axial mass eccentricity to ≤0.2 mm can significantly enhance separation attitude stability. This work provides a practical and efficient engineering methodology for the rapid assessment of attitude disturbances in micro-spacecraft on-orbit ejection systems. However, this study is limited to analyzing the ejection phase of separation, neglecting attitude disturbance effects in the subsequent orbital flight and target impact phases. Future work will address these omissions by extending the model to the entire mission profile and quantifying associated uncertainties. Full article

Modular Mobile Robotic Systems (MMRSs) require simulation tools capable of supporting distributed control architectures, dynamic reconfiguration, and scalable experimentation. This work evaluates three complementary simulation strategies for a homogeneous MMRS composed of autonomous Two-Wheel Inverted Pendulum (TWIP) modules: (i) Webots, selected for rapid prototyping through its integrated GUI; (ii) Pinocchio, paired with the Jiminy simulator to enable modern rigid-body dynamics and control-oriented modeling; and (iii) PyBullet, chosen for programmatic flexibility and reinforcement learning (RL) compatibility. A minimal and controlled benchmark scenario was implemented across all platforms to isolate core simulation characteristics: two differentially driven robots were coupled using the most appropriate mechanism available in each environment and simulated for 1000 steps in headless mode while monitoring CPU usage, memory consumption, and execution time. In addition, a feature-based analysis focused on MMRS-relevant requirements, including dynamic reconfiguration, multi-agent scalability, and suitability for RL workflows. Full article

To address fault diagnosis under limited sample conditions, this paper proposes a small-sample diagnosis framework integrating multibody dynamic modeling and a GAN–CNN fusion strategy. First, a rigid–flexible coupled multibody dynamic model of the reducer is established to simulate vibration responses under typical fault modes, including broken gear tooth, gear wear, and bearing outer ring fault, thereby generating representative simulation samples. Second, to reduce the distribution discrepancy between simulated and measured data, the simulated samples are introduced into a generative adversarial learning framework for feature enhancement, with limited measured samples used as references. Cosine similarity is employed to evaluate the consistency between the enhanced simulated data and the measured data in the feature space. Finally, the enhanced simulated samples are fused with measured samples to construct a hybrid dataset for convolutional neural network training and fault classification. Experimental results show that the proposed framework improves the similarity between simulated and measured data, with cosine similarity increasing from below 0.65 to above 0.80. Under small-sample conditions, the mean diagnosis accuracy reaches 83.81%, which is 17.33 percentage points higher than that obtained using the original small-sample dataset. The proposed framework provides an effective modeling and algorithmic approach for reducer fault diagnosis under data-scarce conditions. Full article

This paper aims to develop an on-board shock absorber detection method for general aviation aircraft. The effects of common gas and oleo leakage are analyzed in this paper. Based on the principle of landing gear dynamics, it is found that gas leakage and oleo leakage would mainly affect air spring force of shock absorbers in various ways. A rigid–flexible coupled landing gear multi-body system (MBS) model is developed by considering strut flexibility, aiming to offer more accurate simulated responses. A database is developed that considers common leakage faults and typical landing conditions using the developed landing gear model. A deep learning model is proposed in this paper. The proposed model is trained and tested using the database simulated from the rigid–flexible coupling landing gear model. The proposed method demonstrates robust detection performance, achieving over 95% precision for most fault types. This work provides a practical, sensor-efficient solution for real-time health monitoring of landing gear shock absorbers, contributing to improved maintenance strategies and operational safety for general aviation aircraft. As this is a preliminary feasibility study, full validation requires future drop tests or instrumented flight tests. Full article

Soft robots have attracted extensive attention owing to their high flexibility. Inflatable membrane tubes offer lightweight and safe environmental interaction, and external shaping actuators have further expanded their applicability. However, modeling such rigid-flexible gas coupled systems remains challenging due to the internal pressure, external loads, and complex deformations including bending, indentation, and wrinkling. To address curvature variation caused by tube deformation hysteresis, this study presents a static model based on virtual work and a segmented approach for inflatable robots. In the actuator unit, the irregular curvature variation and centerline deviation are quantified. In the cantilever unit, the effective bending moment, as well as the wrinkling and failure criteria are derived. The post-buckling deflection equation characterizes the abrupt curvature variation at the tube root caused by the local wrinkling and collapse. A multi-sensor experimental platform is conducted. The experimental results show that the proposed models achieve superior performance in static parameter identification and kinematic prediction. The bending torque error is below 7%, and the tip position error is less than 5% within the bending angle range of 0° to 100°, which confirm that the proposed models accurately predict the coupled deformation and provide a theoretical basis for the precise control of rigid–flexible gas coupled systems. Full article

Gravity energy storage systems (GESS) have emerged as a promising long-duration energy storage technology capable of supporting large-scale renewable integration and enhancing grid resilience. However, the modeling framework for the hoisting electromechanical subsystem in wire-rope-based GESS remains underdeveloped, thereby limiting the accurate characterization of its transient grid-connected behavior, dynamic operating response, and cross-domain coupling effects. Existing studies commonly simplify wire ropes and related transmission components as rigid bodies or low-dimensional mechanical elements, failing to adequately account for their flexibility and the resulting high-dimensional nonlinear dynamics. Although related studies in mine hoisting and elevator systems have addressed mechanical vibration phenomena, they primarily focus on mechanical-side effects, such as shock loading and guide-structure response, whereas the mechanism by which flexible mechanical vibrations propagate through electromechanical coupling and influence electrical dynamic performance remains inadequately understood. To address this gap, this study establishes a distributed-parameter model for the wire-rope hoisting mechanism based on Hamilton’s principle and solves the corresponding vibration governing equations using the Galerkin method to capture nonlinear multi-modal dynamics. An electromechanical coupling model is then developed to elucidate how rope-vibration-induced tension fluctuations propagate through the drive chain, resulting in torque ripple, electrical interharmonics, and low-frequency grid-side oscillations. A Bessel-function-based analytical representation is further introduced to explain the formation of interharmonic clusters and beat-frequency phenomena under converter modulation. An experimental prototype is constructed to validate the proposed modeling framework. The measured vibration spectra, beat-frequency characteristics, and torque ripple align closely with analytical predictions, confirming the model’s capability to capture key propagation paths from rope vibration to electromechanical oscillation and grid-side dynamic response. The results provide a solid theoretical foundation for vibration mitigation, dynamic analysis, and control design of hoisting electromechanical subsystems in gravity energy storage applications. Full article