Search Results (9,580)

The increasing integration of renewable energy sources (RESs) into modern power grids has reduced the rotational inertia traditionally provided by synchronous generators. This reduction poses significant challenges for transient stability, frequency regulation, and the dynamic resilience of power systems. The problem is more pronounced in renewable-dominated hybrid microgrids, where inverter-based resources are progressively replacing conventional generation units. In this context, sudden disturbances can produce large frequency deviations, high RoCoF values, oscillatory behavior, and even loss of stability. This paper proposes an energy-aware adaptive virtual synchronous generator (VSG) control strategy coordinated with a model predictive control (MPC)-based energy-management layer for a battery energy-storage system (BESS), in order to enhance the transient stability of a hybrid microgrid with high renewable penetration. Unlike conventional fixed-parameter VSGs, the proposed MPC–VSG–BESS framework dynamically updates active-power references and adjusts the virtual inertia J and damping D parameters in real time. In this way, the framework seeks to reduce RoCoF, improve the frequency nadir, and preserve the BESS operating constraints, including its energy availability. The methodology is evaluated using detailed EMT simulations in MATLAB–Simulink^® R2023b under severe contingencies, including three-phase faults and sudden loss of conventional generation, with renewable-penetration levels ranging from 70% to 100%. Under the most severe 100% RES condition, the proposed MPC–VSG–BESS framework limits the frequency nadir to 59.26 Hz compared with 56.78 Hz for the conventional control case, corresponding to a 77.0% reduction in the magnitude of the frequency nadir deviation. The proposed controller also extends the critical clearing time from 0 ms to 185 ms under the considered severe fault condition, while maintaining a maximum observed RoCoF of approximately 0.32 Hz/s. During the transient response, the virtual inertia and damping reach maximum observed values of 48.5 kg·m² and 38.2 N·m·s/rad, respectively. Under a low initial SoC of 22%, the energy-aware constraint limits the admissible virtual inertia to 12.4 kg·m². The MPC implementation requires an average computation time of approximately 2.8 ms and a maximum of 6.5 ms for a 10 ms control interval, demonstrating computational feasibility within the adopted simulation framework. Full article

Harsh-environment pressure sensing requires piezoelectric materials that can simultaneously withstand elevated temperature, mechanical loading, and structural degradation. GaN is a promising lead-free piezoelectric semiconductor owing to its wide bandgap, high thermal stability, and non-centrosymmetric wurtzite structure. However, its piezoelectric output can be significantly affected by free-carrier compensation in unintentionally n-type GaN. Here, we report a flexible all-inorganic piezoelectric pressure sensor based on a directly grown h-BN/GaN heterostructure thin film. The h-BN layer was deposited on GaN/Si by plasma-enhanced chemical vapor deposition, followed by backside Si removal, electrode deposition, and transfer onto a flexible Cu foil substrate. Structural characterizations confirmed the formation of a compact h-BN/GaN interface with clear lattice fringes, preferential out-of-plane orientation, and characteristic Raman signatures of both h-BN and GaN. Compared with the flexible GaN/Cu reference, the h-BN/GaN device exhibits modified interfacial electrical transport behavior, enhanced voltage and current-density outputs, and prolonged transient voltage retention. Finite-element simulations reveal modified electrostatic potential distribution after h-BN integration, while electrical and interfacial characterizations suggest electronic structure modulation and reduced carrier compensation effects at the heterointerface. Raman optothermal analysis indicates an improved relative/local thermal response of the h-BN/GaN device under identical optical excitation conditions, supporting its enhanced thermal robustness. Under 200 psi at 400 °C, the h-BN/GaN sensor maintains an output voltage of approximately 27.65 mV, about 2.32 times that of the GaN reference. This work demonstrates an interfacial engineering strategy based on two-dimensional h-BN integration for constructing flexible, thermally robust, and high-output piezoelectric sensors for harsh-environment monitoring. Full article

Laser Powder Bed Fusion (LPBF) has revolutionized high-end manufacturing, particularly in aerospace and biomedical fields. However, internal defects such as pores, cracks, and inclusions compromise the structural integrity and service reliability of LPBF components. Laser ultrasonics, a non-contact, broadband non-destructive testing (NDT) method, offers a promising solution for detecting and characterizing these defects. This study systematically investigated laser ultrasonic testing technology for LPBF-fabricated Ti6Al4V using a combined approach of physics-driven simulation modeling and experimental validation. To accurately model material anisotropy, a finite element model was developed that integrated Voronoi algorithm-generated polycrystalline microstructures with orientation-dependent elastic tensors, providing a comprehensive representation of the material’s microstructural heterogeneity. Simulation results revealed that while sub-100-μm defects yield weak ultrasonic scattering signals, the Synthetic Aperture Focusing Technique (SAFT) markedly improves the detection and imaging performance for such small-scale defects. Experimental validation using a laser ultrasonic system identified a 90 μm internal defect in the LPBF Ti6Al4V specimen, though a 75 μm defect was undetectable. This highlights the need for enhanced sensitivity. A signal processing method combining time-truncation principal component analysis (PCA) with targeted noise reduction and SAFT was proposed to reduce high-frequency noise and improve high-resolution imaging, enhancing defect detection accuracy. This study provides theoretical foundations and technical support for high-precision defect detection in metal additive manufacturing components, with significant implications for quality control in high-end equipment manufacturing. Full article

Passive tuned mass dampers (TMDs) can reduce chatter, but designing and fabricating an accurately tuned absorber remains challenging due to manufacturing constraints. This study proposes a Design of Experiments and Finite Element Analysis (DOE-FEA) based constrained design optimization framework for a passive two-degree-of-freedom (TDOF) TMD to suppress regenerative chatter in boring operations by considering practical and manufacturing constraints on absorber position, mass ratio, moment of inertia and fixed inter-spring distance. The proposed, additively manufactured TMD housing, made from polylactic acid (PLA), includes a mass block supported by two spring-damper elements that enable coupled translational and rotational interactions with the boring bar. A finite-element forced vibration analysis of the boring bar TMD system is developed to obtain the real and imaginary parts of the frequency response function (FRF), which are then used to construct the stability lobes. The minimum limiting depth of cut over the spindle speed range is used as the optimization criterion, and response surface methodology (RSM) is used to obtain optimum absorber parameters within realistic design constraints. The dynamic behaviour of the TDOF TMD is experimentally and numerically evaluated and compared with that of a single-degree-of-freedom (SDOF) TMD, attributing the relative improvement in performance primarily to the combined effects of independent absorber architecture, mass, stiffness and damping distribution and dynamic tuning. The results showed that the optimal TDOF TMD achieved a DOC of 11.054 mm, while the SDOF TMD achieved 4.335 mm. The experimental investigation of additively manufactured TDOF and SDOF TMDs demonstrated qualitatively similar dynamic phenomena to those of the corresponding numerically optimized absorbers. Time-domain acceleration response, spectrogram and power spectrum were used to compare these dynamic phenomena demonstrated by the SDOF and TDOF TMDs. A reduction in corresponding first and second amplitude peaks from −2.8 dB (670 Hz) and −22.7 dB (1360 Hz) in the case of the SDOF TMD to −17.6 dB (600 Hz) and −23.8 dB (1150 Hz) for the TDOF TMD verified the vibration attenuation and frequency redistribution phenomenon as exhibited by the FE-model. Full article

A line-start permanent-magnet motor (LSPM) combines the direct-on-line starting capability of a squirrel-cage induction motor (IM) with permanent-magnet-assisted synchronous operation. Previous studies on LSPM winding connections have mainly focused on load-dependent efficiency and the power factor, while their effects on harmonics, torque ripple, and synchronization across different rotor configurations remain unclear. This study compares Y- and delta-connected windings in two 5.5 kW, four-pole LSPM models using transient finite-element analysis. Current and voltage harmonics, losses, efficiency, torque ripple, and synchronization response were evaluated. The Y-connected cases exhibited lower current harmonic distortion and stator copper loss, whereas the delta-connected cases reduced torque ripple and maximum speed overshoot but required slightly longer settling times. For LSPM-B, the Y connection achieved the highest efficiency of 92.92% with a stator copper loss of 137.81 W, while the delta connection reduced the torque ripple ratio from 43.8% to 39.5%. These results demonstrate that winding-connection effects depend on the rotor magnetic circuit and cage-assisted starting characteristics, requiring a trade-off among efficiency, harmonic loss, torque ripple, and synchronization response. Full article

Emerging technology cultivation is formulated as a finite-time stochastic reachability problem under a budget-simplex constraint. Novelty potential, implementation feasibility, and industrial embeddedness form a three-state cascade Itô system, and success requires their joint entrance into a target set before a fixed horizon. The analysis establishes positive invariance, a unique globally attractive deterministic equilibrium, and an explicit asymptotic budget boundary whose minimum occurs at the maximizer of the knowledge-distance kernel. For specified constant-allocation rules, projected Euler–Maruyama simulation provides rule-specific node-monitoring probabilities and budget thresholds. A repeated finite-candidate sample-average approximation with independent candidate-generation, selection, and validation samples assesses the additional gain from adaptive candidate selection. The numerical results show that the deterministic grid-search rule reaches prescribed probability levels at lower intervention rates than balanced allocation, while the repeated SAA procedure yields further gains in the tested transition region. Finite-time success therefore depends on both the total budget rate and its allocation across cascade-dependent channels. Full article

Bioreceptive building envelopes offer a potential pathway for integrating biological colonization into architectural surfaces. This article proposes a geometric design framework for bioreceptive façade components in which the surface itself is shaped to increase the exposed area available to the environment, and asks which admissible regular Euclidean tessellation maximizes that area. Triangular, square, and hexagonal tessellations are compared under a common circumscribed radius R and a common absolute indentation depth $h_j$, counting only the inclined faces generated by the indentation. A closed-form expression is derived for the exposed-surface density ${\sigma }_n=\sqrt{h_j^2+r_n^2}/r_n$, where $r_n$ is the apothem of the regular cell, and the triangular tessellation is shown to maximize it uniquely for every $h_j>0$; when $h_j=0$ the three configurations coincide. Because each inclined face has constant slope magnitude $h_j/r_n$, the area Jacobian is constant, so clipping the indented surface with any measurable footprint $\Omega$ of finite positive area gives $S_n(\Omega )={\sigma }_n|\Omega |$ exactly, with no boundary-error term. For the representative case $h_j/R=45/34$, the hexagonal density is $0.6454$ times the triangular one, equivalent to a $54.93\%$ triangular advantage specific to that ratio. An independently constructed CAD model reproduces the closed-form panel areas at both reference footprints to within ${10}^{-3}\%$ and preserves the same ordering. Exposed area is a geometric descriptor of the available surface–environment interface and a candidate design parameter, not evidence of colonization, water behavior, or durability; no material, fabrication, or biological validation is claimed. Full article

This paper studies local and global existence, uniqueness, and Lipschitz continuous dependence of mild solutions for a semilinear Hadamard fractional integro-differential equation in a Banach space ${\displaystyle X}$, with nonlocal initial conditions, a state-dependent Volterra kernel embedded in the nonlinearity, and impulsive effects at finitely many fixed times, where ${\displaystyle -A}$ generates a compact ${\displaystyle C_0}$-semigroup on ${\displaystyle X}$. The mild-solution formula is derived from first principles via ${\displaystyle \tau =lnt}$ and the Riemann–Liouville variation of parameters formula. Local existence follows from Schauder’s fixed-point theorem, global existence from a Hadamard–Gronwall inequality with an explicit blow-up alternative, uniqueness from a logarithmic power-weight contraction with an explicit condition on the Lipschitz constants, and continuous dependence with an explicit stability constant ${\displaystyle C_{\mathrm{dep}}}$; two concrete examples verify all hypotheses. Beyond this compact-semigroup theory, we prove two further results: an existence theorem under a Kuratowski measure-of-noncompactness condition via a Hadamard-adapted Mönch fixed-point argument, removing the compactness assumption on ${\displaystyle {\left\{T\left(t\right)\right\}}_{t\ge 1}}$ altogether, and an Ulam Hyers Rassias stability theorem whose constant reuses the same contraction weight ${\displaystyle {\lambda }^{*}}$ from the uniqueness theorem. Both extend recent noncompact-semigroup and Ulam-stability results in the literature to the present nonlocal, impulsive, state-dependent-kernel setting. Full article

Contact polymer–grease pairs of materials are widely used in highly loaded friction units. However, modeling the joint mechanical deformation of polymer grease significantly increases computational costs and complicates the convergence of the numerical solution. This is due to the pronounced nonlinear behavior of the materials and the significant difference in their physical and mechanical characteristics. Modeling the behavior of complex spatial configurations of structures, taking into account temperature and time factors, and investigating the behavior of structures in dynamics challenges researchers and engineers with the task of reducing computational costs for numerical experiments without loss of accuracy. Therefore, it is of great importance to design methods and approaches for describing effective characteristics, taking into account the nonlinear behavior of a polymer–grease pair of materials. Representing the design volumes of polymer grease as an equivalent medium can reduce computational costs. This paper considers the design volume, including a polymer base made of ultrahigh-molecular-weight polyethylene and a spherical recess filled with CIATIM-221 grease. The modified elastic–viscoplastic Anand model has been used to describe the behavior of materials and an equivalent medium. The model parameters have been determined based on a set of numerical experiments at various temperatures, deformation rates, and sizes of the design volume. The effective elastic and viscoplastic characteristics of the equivalent medium have been determined. An increase in temperature leads to a decrease in stiffness and resistance to viscoplastic flow. Increasing the size of the design volume helps to stabilize effective characteristics and reduce the impact of local structural heterogeneities. The proposed analytical dependencies make it possible to take into account the influence of temperature and the geometry of the design volume when determining the parameters of an equivalent medium. The developed approach can be used in finite element modeling of large structures with friction units to reduce computational costs without explicitly modeling the polymer–grease interface. Full article

Predicting the nonlinear seismic response of structures that have entered the plastic range under strong ground motions is severely constrained by data scarcity and computational cost. In this article, to address this dual challenge, we propose a physics-guided ensemble model based on Support Vector Regression. A finite element model of a single-story steel structure was created, and 500 nonlinear time-series analyses were generated using Incremental Dynamic Analysis for 50 different natural ground motions, at 10 levels of PGA intensity. Using an innovative feature engineering strategy, the 16 original ground motion parameters were decomposed into intensity, waveform and interaction features, thereby expanding the input space to 47 physically meaningful dimensions. Subsequently, the 35 features with the greatest information richness were extracted using a selection process based on mutual information. A systematic comparison with benchmark models demonstrated that Support Vector Regression (SVR) with Radial Basis Function (RBF) kernels offered significantly superior performance to Deep Learning with a reduced number of samples for this task, thus confirming the superiority of the structural risk minimization principle under conditions of limited data. Furthermore, the proposed two-level stacked ensemble achieved the lowest Mean Absolute Error among all evaluated models, demonstrating improved robustness in reducing prediction deviations and suppressing extreme errors in nonlinear seismic response estimation. These results demonstrate that the combination of physics-guided feature engineering and kernel-based learning provides an efficient surrogate approach for rapid seismic response prediction of steel structures under previously characterized ground-motion conditions. Full article

The mission planning problem of using ground-fixed communication base stations to guide Unmanned Aerial Vehicle (UAV) swarms back under communication denial is addressed. The core challenge is to optimally match limited resources with massive UAV demands under constraints such as time windows, base station exclusivity, and relay continuity, which we formulate as an NP-hard combinatorial optimization problem. We first build a static model maximizing comprehensive benefits, incorporating base station heterogeneity and a super-linear congestion penalty for load balancing. We then extend it to a rolling horizon dynamic framework. Through task state partitioning and frozen resource inheritance, this extension decomposes the long-term optimization into sequential finite-horizon subproblems, enabling online decisions as UAV information is gradually revealed. To solve these models, we propose CMSA-MSWOA, which integrates elite opposition-based learning and Lévy flights to navigate the fragmented feasible solution space. Simulation results show 100% guidance coverage across scales from 100 to 500 UAVs in static scenarios, with the benefit advantage over the best benchmark growing from 10.0% to 65.5% as scale increases. In dynamic scenarios, the rolling framework satisfies all constraints and achieves full coverage. While our framework performs robustly in simulations, the current evaluation assumes idealized communication conditions; validation under more complex interference and external testing remains future work. Overall, our model and algorithm offer a useful simulation-based closed-loop framework for resource scheduling in denial environments, providing a foundation for further validation under more realistic field conditions. Full article

This article proposes a linear-time correlation-controlled shuffling method that attenuates Pearson correlation by permuting one variable through local swaps on a random regular graph. Each round processes a fixed edge order and accepts only vertex-disjoint swaps, producing greedy matching rather than a standard interchange-process update. The analysis uses the induced first-moment operator. A uniform lower bound on edge acceptance yields a Laplacian-domination relation, while a lower bound on the probability that vertices remain unmatched controls the negative spectrum. Conditional on the loop-deleted realized graph being connected and having a spectral gap bounded away from zero, the absolute value of the expected correlation contracts exponentially. For every fixed same-sign attenuation target, an activation probability exists that attains the target in expectation. Finite-sample calibration uses a fixed-budget multi-resolution direct search without assuming monotonicity. Because the procedure applies only permutations, the marginal distribution is preserved exactly. Under fixed degree and fixed calibration budgets, both calibration and shuffling scale linearly with sample size. Experiments show small target errors, exact marginal preservation, and approximately linear runtime scaling. A matched-degree spectral ablation further shows that, with degree and edge count held fixed, the higher-gap random regular graph exhibits substantially faster decay of the mean correlation than the low-gap regular circulant graph. Full article

This study investigates how coordinated wireless and fast charging services reshape electric vehicle departure time and path–charging choices when a trip-level charging requirement must be completed before arrival. A multi-class dynamic user equilibrium model is formulated for road networks containing wireless charging lanes and fast charging stations. An energy-aware dynamic network loading model propagates traffic and battery states, transfers upstream wireless energy into the residual station workload, and determines endogenous waiting. The equilibrium is expressed as a finite-dimensional variational inequality and solved by an energy-aware inertial fixed-point framework with safeguarded route swapping and independent verification. Experiments on the Nguyen–Dupuis and Sioux Falls networks show that low-state-of-charge users depart 6.91 min earlier on average, while exposure-informed wireless-charging placement can substantially reduce downstream station waiting and exhibits saturation once all behaviorally exposed links are active. Under compound demand and low-state-of-charge pressure, roadway queues activate more sharply than station waiting. In a common Sioux Falls algorithm benchmark, the inertial method reaches stable acceptance in 776.2 s compared with 1562.9 s for its non-inertial counterpart. The method of successive averages crosses the practical gap threshold earlier but does not satisfy the common flow-stability criterion within 3000 updates and 9018.1 s. Across 30 final Sioux Falls scenarios, all solutions satisfy the practical verified gap and physical feasibility gates, with 11 difficult cases requiring explicit route-swap continuation. The results clarify the complementary operational roles of corridor and station charging while delimiting the numerical and behavioral assumptions of the framework. Full article

In this work, we develop a projection neural network based on a second-order dynamical model (SO-PDM) for solving inverse variational inequality problems (IVIPs) in Hilbert spaces. The proposed framework incorporates inertial and damping components, resulting in improved convergence behavior while ensuring feasibility through a projection operator. Under the Lipschitz continuity assumption on the operator, the proposed SO-PDM admits a unique global trajectory. Under the additional strong monotonicity assumption and suitable parameter conditions, convergence to the unique solution of the IVIP is established. A discrete-time formulation is derived via a finite-difference scheme, leading to a projection-based inertial algorithm with relaxation. Under suitable parameter conditions, the algorithm is shown to converge linearly to the unique solution of the IVIP, and under an additional parameter condition, the global asymptotic stability of the continuous-time SO-PDM is established via Lyapunov analysis. Furthermore, a numerical comparison in a higher-dimensional setting shows that the proposed algorithm converges faster and attains higher accuracy than the existing first-order projection method. Numerical experiments further confirm the effectiveness and stability of the proposed SO-PDM, including its application to sparse signal recovery in compressed sensing. Full article

Settlement-induced bending may cause excessive tensile and compressive strains in buried station pipelines, while full finite element analysis is too time-consuming for rapid integrity screening. This study proposes a strain prediction framework that couples nonlinear pipe–soil finite element simulation with stacking ensemble learning. A pipe–soil model for X65 buried pipelines is established to generate 196 samples with pipe diameter, wall thickness, settlement length, and settlement amount as inputs, and maximum tensile and compressive strains as outputs. Random Forest, LightGBM, and support vector regression are trained as base learners and then fused through stacking. Results show that RF performs best for tensile strain prediction (R² = 0.8815), whereas SVR performs best for compressive strain prediction (R² = 0.8997). The stacking models further improve accuracy, with RF + LGBM + SVR achieving R² = 0.9033 for tensile strain and LGBM + SVR achieving R² = 0.9048 for compressive strain. The proposed model provides an efficient tool for settlement pipeline integrity assessment. Full article