Search Results (2,150)

The increasing integration of renewable energy sources (RES) and rising energy demand have created challenges in maintaining stability in interconnected power systems, particularly in terms of frequency, voltage, and tie-line power. While traditional load frequency control (LFC) and automatic voltage regulation (AVR) strategies have been widely studied, they often fail to address the complexities introduced by RES and nonlinear system dynamics such as boiler dynamics, governor deadband, and generation rate constraints. This study introduces the Arithmetic Optimization Algorithm (AOA)-optimized PI(1+DD) controller, chosen for its ability to effectively optimize control parameters in highly nonlinear and dynamic environments. AOA, a novel metaheuristic technique, was selected due to its robustness, efficiency in exploring large search spaces, and ability to converge to optimal solutions even in the presence of complex system dynamics. The proposed controller outperforms classical methods such as PI, PID, I–P, I–PD, and PI–PD in terms of key performance metrics, achieving a settling time of 7.5 s (compared to 10.5 s for PI), overshoot of 2.8% (compared to 5.2% for PI), rise time of 0.7 s (compared to 1.2 s for PI), and steady-state error of 0.05% (compared to 0.3% for PI). Additionally, sensitivity analysis confirms the robustness of the AOA-optimized controller under ±25% variations in turbine and speed control parameters, as well as in the presence of nonlinearities, demonstrating its potential as a reliable solution for improving grid performance in complex, nonlinear multi-area interconnected power systems. Full article

Background/Objectives: Growing evidence supports the use of a single trough concentration, rather than both a peak and trough, to estimate the 24 h area under the curve (AUC₂₄) of vancomycin using Bayesian software (InsightRx^® Ver.1.71). However, patients with body mass index (BMI) ≥ 40 kg/m² are underrepresented in validation studies. Studies in patients with obesity have produced mixed results, potentially because of different population models used. Methods: This single-center, retrospective study evaluated adult inpatients with BMI ≥ 40 kg/m². Steady-state AUC₂₄ estimates generated by Bayesian software using both two-concentration and one-concentration inputs were compared. Agreement was defined as a percent difference within ±20%. Subgroup analyses were conducted for patients with defined peak and trough concentrations and for comparisons between two Bayesian population models (Carreno vs. Hughes). Linear regression assessed covariates associated with percent difference. Results: Among 82 encounters, 97.5% of one-concentration estimates based on the smaller concentration were within ±20% of the two-concentration AUC_24,SS (mean difference: 2.9%, 95% CI: 0.14 to 3.8%). Similar agreement was observed using the larger concentration (97.5%, mean difference: −3.1%, 95% CI: −4.7 to −0.1.5%). Subgroup analysis for encounters with true peak/trough levels (n = 22) also showed 100% agreement within ±20%. The percent difference did not correlate with BMI or other covariates. Comparison of Hughes vs. Carreno models showed larger variability (only 59.1% within ±20%). Conclusions: In patients with BMI ≥ 40 kg/m², Bayesian AUC_24,SS estimation using a single vancomycin concentration is feasible. Greater caution is warranted in the setting of acute kidney injury, poor model fit, or targeting AUC at the extremes of the therapeutic range. The population model used to generate the Bayesian AUC estimate has a much greater influence than the number of concentrations analyzed. Furthermore, measuring two concentrations does not ensure concordance between models. Full article

Inspired by the free-flight capabilities of the gannet in both aerial and underwater environments, a foldable-wing air–water cross-medium vehicle was designed. To enhance its propulsive performance and transition stability across these two media, aero-hydrodynamic performance analyses were conducted under three representative operating states: aerial flight, underwater navigation, and water entry. Numerical simulations were performed in ANSYS Fluent (Version 2022R2) to quantify lift, drag, lift-to-drag ratio (L/D), and tri-axial moment responses in both air and water. The transient multiphase flow characteristics during water entry were captured using the Volume of Fluid (VOF) method. The results indicate that: (1) in the aerial state, the lift coefficient increases almost linearly with the angle of attack, and the L/D ratio peaks within the range of 4–6°; (2) in the folded (underwater) configuration, the fuselage still generates effective lift, with a maximum L/D ratio of approximately 2.67 at a 10° angle of attack; (3) transient water entry exhibits a characteristic two-stage force history (“initial impact” followed by “steady release”), with the peak vertical load increasing significantly with water entry angle and velocity. The maximum vertical force reaches 353.42 N under the 60°, 5 m/s condition, while the recommended compromise scheme of 60°, 3 m/s effectively reduces peak load and improves attitude stability. This study establishes a closed-loop analysis framework from biomimetic design to aero-hydrodynamic modeling and water entry analysis, providing the physical basis and parameter support for subsequent cross-medium attitude control, path planning, and intelligent control system development. Full article

This paper investigates the transient behavior of a drain water heat recovery (DWHR) device, which recovers heat from warm grey water in buildings. Experimental and numerical investigations were conducted to study the thermal performance of the device under transient conditions. Thermal performance measurements were carried out, and a mathematical model was developed that considered the thermal inertia of the hot and cold-water streams as well as the device’s material. The experimental data were used to validate the model, and good agreement was observed between the two. Under transient operating conditions, the device’s performance was measured in terms of its effectiveness, and an actual effectiveness model was used to capture the transient effect on the system’s overall performance. The experimental results show that during short hot water usage, the actual device effectiveness is significantly reduced as it does not reach a steady state condition. An economic analysis indicates that considering the device’s transient performance leads to a 27.2% reduction in annual energy savings in a typical domestic installation with regular daily usage of hot water appliances. The presented model and analysis offer valuable insights for the development of improved DWHR devices with the potential to contribute to sustainable engineering and building practices. Full article

This work presents an analytical 2D model for estimating eddy current losses in the permanent magnets (PMs) of a coaxial magnetic gear (CMG), with a focus on loss minimization through magnet segmentation. The model is applied under various operating conditions, including different rotational speeds, load levels, and segmentation configurations, to derive empirical expressions for eddy current losses in both the inner and outer rotors. A 1D lumped-parameter thermal model is then used to predict the steady-state temperature of the PMs, incorporating empirical correlations for the thermal convection coefficient. Both models are validated against finite element analysis (FEA) simulations. The analytical eddy current loss model exhibits excellent agreement, with a maximum error of 2%, while the thermal model shows good consistency, with a maximum temperature deviation of 5%. The results confirm that eddy current losses increase with rotational speed but can be significantly reduced through magnet segmentation. However, achieving an acceptable thermal performance at high speeds may require a large number of segments, particularly in the outer rotor, which could influence the manufacturing cost and complexity. The proposed models offer a fast and accurate tool for the design and thermal analysis of CMGs, enabling early-stage optimization with minimal computational effort. Full article

To address the challenges posed by harmonic distortion and DC offset in the power grid, this paper proposes a novel Phase-Locked Loop (PLL) architecture tailored for single-phase grid-connected systems. The design integrates an Enhanced Adaptive Second-Order Generalized Integrator (EA-SOGI) with a Quasi-Proportional Resonant (QPR) controller. The proposed EA-SOGI extends the conventional SOGI by incorporating an all-pass filter and an additional integrator, which enhance the symmetry of the orthogonal signals and effectively suppress the estimation errors caused by DC offset. In addition, the conventional PI controller is replaced by a QPR controller, whose parameters are tuned using a hybrid Levy-AsyLnCPSO optimization algorithm to improve frequency locking performance and enhance system robustness under steady-state conditions. Simulation and experimental results demonstrate that the proposed PLL achieves a Total Harmonic Distortion (THD) as low as 2.8653% based on Fast Fourier Transform (FFT) analysis, indicating superior adaptability compared to conventional PLL structures and validating its effectiveness in DC offset suppression and harmonic mitigation. Full article

To reveal the influence mechanism of shear creep behavior of the weak interlayer (carbonaceous mud shale) from a microscopic perspective, acoustic emission (AE) technology was introduced to conduct shear creep tests to capture micro-fracture acoustic signals and analyze the microscopic damage evolution laws. The results indicate that, as normal stress increased, shear creep strain decayed exponentially, while the steady state creep rate increased gradually. Additionally, the peak value and cumulative value of the AE ringing count rate also increased gradually. The AE b-value had a staged pattern of “fluctuation adjustment → stable increase → abrupt decline”. The sudden drop in the b-value could serve as a precursor feature of creep failure. The higher the normal stress, the earlier the sudden drop in b-value and the larger the Δb value. The damage variable was defined based on the AE ringing count rate, and a new creep damage model was constructed by combining fractional-order theory. The model can uniformly describe the creep damage law of carbonaceous mud shale under different normal stresses. The reliability of the model was verified through experimental data. The research results provide a theoretical basis for long-term stability analysis of mine slopes containing weak interlayers. Full article

Accurate and timely estimation of the root-mean-square (RMS) voltage is essential for grid-connected inverter systems, where it underpins reference generation, synchronization, and protection functions. Conventional RMS estimation methods, based on squaring, averaging, and taking the square root of values over full-cycle windows, achieve high accuracy but incur significant latency and computational overhead, thereby limiting their suitability for real-time control. Frequency-domain approaches, such as the FFT or wavelet analysis offer harmonic decomposition but are too complex for cost-sensitive embedded controllers. To address these challenges, this paper proposes an averaging-based RMS estimation method that exploits the proportionality between the mean absolute value of a sinusoidal waveform and its RMS. The method computes a moving average of the absolute voltage over a half-cycle window synchronized to the phase-locked loop (PLL) frequency, followed by a fixed scaling factor. This recursive implementation reduces the computational burden to a few arithmetic operations per sample while maintaining synchronization with off-nominal frequencies. Time-domain simulations under nominal (60 Hz) and deviated frequencies (57 Hz and 63 Hz) demonstrate that the proposed estimator achieves steady-state accuracy comparable to that of conventional and adaptive methods but with convergence within a half-cycle, thereby reducing latency by nearly 50%. These results confirm the method’s suitability for fast, reliable, and resource-efficient real-time inverter control in modern distribution grids. To provide a comprehensive evaluation, the paper first reviews conventional RMS estimation methods and their inherent limitations, followed by a detailed presentation of the proposed averaging-based approach. Simulation results under both nominal and off-nominal frequency conditions are then presented, along with a comparative analysis highlighting the advantages of the proposed method. Full article

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

This paper addresses a critical challenge in Modular Multilevel DC-link based T-type Converter (MMDTC) battery energy storage systems: the inherent power symmetry between upper and lower arms that prevents natural state-of-charge (SOC) balancing. A novel inter-arm SOC balancing control strategy is proposed that precisely modulates arm valley voltage characteristics—both amplitude and duration—to create controlled power differentials between arms. The theoretical analysis establishes a quantitative relationship between arm valley voltage width and inter-arm power difference, demonstrating that SOC balancing time is inversely proportional to arm valley voltage width. A key advantage of the proposed approach is its adaptive regulation capability: during steady-state operation, closed-loop feedback maintains arm valley voltage width within a narrow range through real-time SOC differential assessment, ensuring dynamic SOC equilibrium while minimizing impact on output power quality. The strategy works effectively in both charging and discharging modes, with comprehensive control logic for various operational scenarios. Finally, simulation and experimental results confirmed the effectiveness of the proposed control strategy. Full article

This study investigates steady-state heat conduction in materials with stepwise discontinuities in thermal conductivity, a phenomenon frequently encountered in layered composites, thermal barrier coatings, and electronic packaging. The problem is formulated for a 2D two-domain region, where each subdomain has a distinct constant conductivity. Both the direct problem—determining the temperature field from known conductivities—and the inverse problem—identifying conductivities and the internal heat source from limited temperature measurements—are addressed. To this end, three deterministic finite-difference-type models are developed: two for the standard formulation and one for a meshless formulation based on Moving Least Squares (MLS), all derived within a local framework that efficiently enforces interface conditions. In addition, two Monte Carlo models are proposed—one for the standard and one for the meshless setting—providing pointwise estimates of the solution without requiring computation over the entire domain. Finally, an algorithm for solving inverse problems is introduced, enabling the reconstruction of material parameters and internal sources. The performance of the proposed approaches is assessed through 2D benchmark problems of varying geometric complexity, including both structured grids and irregular node clouds. The numerical experiments cover convergence studies, sensitivity of inverse reconstructions to measurement noise and input parameters, and evaluations of robustness across different conductivity contrasts. The results confirm that the hybrid difference-meshless Monte Carlo framework delivers accurate temperature predictions and reliable inverse identification, highlighting its potential for engineering applications in thermal design optimization, material characterization, and failure analysis. Full article

To address the poor trajectory tracking of mining trucks in narrow, high-curvature paths, this study explores the impact of four-wheel steering (4WS) and direct yaw moment control (DYC) on vehicle stability. A validated two-degree-of-freedom 4WS vehicle model was developed. A fuzzy logic controller with dual inputs (yaw rate and yaw angular acceleration) and a single output (compensatory yaw moment) was designed, alongside an optimal torque distribution controller based on tire friction circle theory to allocate the resultant yaw moment. A co-simulation platform integrating TruckSim and MATLAB/Simulink was established, and experiments were conducted under steady-state and double-lane-change conditions. Comparative analysis with traditional front-wheel steering and alternative control methods reveals that the 4WS mining truck with fuzzy-controlled optimal torque distribution achieves a reduced turning radius, enhancing maneuverability and stability. Hardware-in-the-loop (HIL) testing further validates the controller’s effectiveness in real-time applications. Full article

We perform large-eddy simulations (LESs) with realistic radiation, including topographic shading, and an advanced land surface model to investigate drainage flow dynamics in an idealized compound-slope mountain geometry. This allows an analysis not only of fully developed profiles in steady state—the subject of existing analytical solutions—but also of transient two- and three-dimensional dynamics. The evening onset of downslope flow is related to the duration of shadow front propagation along the eastern slopes, for which an analytic form is derived. We demonstrate that the flow response to this radiation pattern is mediated by the thermal inertia of the land through sensitivity to soil moisture. Onset timing differences on opposite sides of the peak are explained by convective structures that persist after sunset over the western slopes when topographic shading is considered. Although these preceding convective systems, as well as the presence of neighboring terrain, inhibit the initial development of drainage flows, the LES develops an approximately steady-state, fully developed flow over the finite slopes and finite nocturnal period. This allows a comparison to analytical models restricted to such cases. New analytical solutions based on surface heat flux boundary conditions, which can be estimated by the coupled land surface model, suggest the need for improved representation of the eddy diffusivity for analytical models of drainage flows. Full article

Hammerstein adaptive filters (HAFs) are widely used for nonlinear system identification due to their structural simplicity and modeling effectiveness. However, their performance can degrade significantly in the presence of impulsive disturbance or other more complex non-Gaussian noise, which are common in real-world scenarios. To address this limitation, this paper proposes a robust HAF algorithm based on the kernel mean p-power error (KMPE) criterion. By extending the p-power loss into the kernel space, KMPE preserves its symmetry while providing enhanced robustness against non-Gaussian noise in adaptive filter design. In addition, random Fourier features are employed to flexibly and efficiently model the nonlinear component of the system. A theoretical analysis of steady-state excess mean square error is presented, and our simulation results validate the superior robustness and accuracy of the proposed method over the classical HAF and its robust variants. Full article

This paper develops a tractable method to solve a general equilibrium model with bank runs and exogenous leverage ratio restrictions, enabling welfare analysis of macroprudential policy across the business cycle. By computing bankers’ value functions via backward induction from steady state, the framework quantifies how leverage caps affect capital allocation, asset prices, and run probabilities during recovery from crises. Calibrated simulations show that welfare-enhancing policy is time-varying—lenient when households’ marginal utility of consumption is high, and restrictive in low-marginal-utility states. The results highlight a trade-off: tighter leverage restrictions improve stability but risk persistent efficiency losses if imposed too harshly after crises. Full article