Search Results (37,784)

Active vehicle systems integrate electromechanical actuators and advanced control strategies to improve driving comfort and safety. Their development requires coordinated mechanical, electrical, and software design, supported by early evaluation of system performance and driver acceptance. The automotive industry accelerates the development process by adopting multi-stage simulation workflows, from Model-in-the-Loop to hardware-in-the-loop and track testing, progressively reducing the virtualization level. Final testing stages require actuators with programmable control units, often unavailable in commercial products. This paper proposes a research-oriented steering actuator based on the modification of an existing system by introducing an additional torque sensor after the steering wheel. Results indicate that the additional compliance significantly alters the passive steering response, while the impact on active EPS operation is negligible, confirming the suitability of the modified actuator for experimental research applications. Full article

This work presents an alternating sequential model predictive control (ASMPC) scheme applied to multimodular matrix converters. The proposed strategy alternately evaluates two control objectives: load current tracking and input reactive power minimization. The algorithm was implemented in MATLAB/Simulink on an architecture composed of two direct matrix converters in a multimodular configuration. The influence of parameter $N_2$ on system performance was analyzed under step changes in reference current of 30 A and 60 A. To this end, performance metrics such as THD and MSE were used, along with a descriptive statistical analysis including the mean, standard deviation, mean absolute deviation (MAD), and coefficient of variation (CV). Simulation results show stable performance for variations in $N_2$, with an input current THD of $8.10\%$ and load THD reduced to $1.00\%$, demonstrating improved harmonic performance compared with classical weighted MPC approaches. Full article

Mispronunciation detection (MD) is a key component in computer-assisted pronunciation training (CAPT) and speaking tests. Most MD systems adopt a production view, measuring phone-level deviation from a canonical pronunciation (Native Norm) or the expected pronunciation of a target population (Target Norm). Yet, pronunciation assessment is fundamentally perceptual: listeners map speech to linguistic categories under uncertainty and with individual psychological priors, so judgments are inherently subjective and lack a single gold standard. Labels are therefore often aggregated (e.g., voting), but aggregation rules are themselves subjective, require many annotators, and entangle individual perception with social consensus, complicating model training. In this paper, we propose a “Perception Norm”, which models MD as the decision process of individual annotators and trains models to simulate single listeners rather than an annotator pool. To support this study, we introduce UY/CH-CHILD-MA, a corpus of Uyghur-accented child Mandarin words and phrases with four independent phone-level annotations. Our experiments reveal substantial inter-annotator variation and show that a Transformer with pre-training and fine-tuning can learn annotator-specific patterns with high accuracy. Finally, we present a committee ensemble that combines annotator models using application-matched aggregation rules to produce task-specific assessments. The data and source code will be made publicly available upon publication. Full article

With the rapid advancement of information technology, the energy consumption of data centers has become a critical issue. Accurate cooling load prediction is essential for optimizing cooling system operations and improving energy efficiency. However, conventional models often struggle to capture the complex nonlinearities and multi-variable coupling effects inherent in data centers. To address the limitations of existing models in terms of training efficiency and generalization performance, this study proposes a cooling load prediction model that integrates the light gradient boosting machine (LightGBM) algorithm with Bayesian optimization. The model was validated using data generated from an EnergyPlus simulation of a representative medium-scale data center. Comparative analysis demonstrates that the proposed model surpasses naive benchmarks (T-1, T-24, and T-168) and other machine learning models (SVR, XGBoost, and LSTM), achieving superior performance with a Root Mean Squared Error (RMSE) of 4.3234 kW, R² of 0.9999, and Mean Absolute Percentage Error (MAPE) of 0.07%. A noise robustness analysis further reveals that the model maintains excellent performance under realistic uncertainties, achieving an R² above 0.99 and an RPD exceeding 12 even at high noise levels (SNR = 20 dB). The total runtime and Relative Prediction Deviation (RPD) were 33.45 s and 86.2685, respectively, indicating an excellent balance between computational efficiency and robust predictive reliability. The key contribution of this research is the effective integration of LightGBM and Bayesian optimization to provide a highly accurate and efficient tool for data center cooling load prediction. This approach offers a scientific foundation for the intelligent control of cooling systems and energy efficiency optimization in data centers, with direct practical implications for building energy management. Full article

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit–Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies—PD control, PD with gravity compensation, and the computed torque method—are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4–1.45 m/s, validating the effectiveness of the proposed model and control strategies. Full article

As an important aerodynamic configuration of the new-generation UAV, the dorsal S-shaped inlet’s performance is affected by the complex coupling of inflow conditions and the boundary layer ingestion effect. To investigate the influence mechanisms of these factors on inlet performance, CFD based on the scale-adaptive simulation (SAS) turbulence model is used to systematically analyze the flow field and performance of a UAV dorsal S-shaped inlet within a typical flight envelope. It is found that with increasing Mach number (0.6–0.9), the exit total pressure recovery decreases significantly, while the circumferential distortion coefficient almost doubles. As the angle of attack varies from −10° to 10°, a slight decrease in total pressure recovery is observed, but distortion improves due to a relatively stable separation region. Changes in sideslip angle have minimal impact on overall performance but notably alter the symmetry of the vortex system, resulting in a decrease in distortion coefficient. Additionally, at a specific Mach number, back pressure correlates positively with inlet performance. The increase in back pressure can effectively inhibit the flow separation and enhance the total pressure recovery, while the distortion coefficient decreases. The research results provide an important theoretical basis for the design optimization of the new-generation UAV. Full article

Hydrogels and nanocomposite−enhanced hydrogels, owing to their high−water content, excellent biocompatibility, and mechanical flexibility, have demonstrated broad application prospects in tissue engineering, drug delivery, and flexible electronics. With the continuous advancement of computational power, molecular dynamics (MD) simulations have increasingly become an important tool for characterizing nanocomposite materials and hydrogel systems. This approach enables the capture of structural evolution at the atomic/molecular scale and provides mechanistic insights into deformation behaviors and interaction mechanisms under external stimuli such as mechanical force, temperature, and electric fields. This review is organized around the central framework of “structural construction–interfacial regulation−responsive behavior–dynamic evolution”, and systematically summarizes the recent progress in the application of molecular dynamics and multiscale simulation methods to hydrogels and nanocomposite hydrogels. The systems discussed mainly include synthetic polymer-based hydrogels, natural polymer−based hydrogels, peptide/protein−based hydrogels, and nanocomposite hydrogels. Particular emphasis is placed on modeling strategies and force−field selection principles for describing atomic interactions in various nanocomposite hydrogel systems. In addition, the important applications of multiscale simulation strategies in elucidating the interfacial behavior of hydrogels and the mechanisms underlying their dynamic responses under nonequilibrium conditions are also discussed. Finally, future development trends are outlined, including multiscale coupled simulations, closed−loop correction between experiments and simulations, and data−driven modeling strategies for the precise design and performance prediction of complex hydrogel systems. Full article

In fire emergency ventilation systems, EC (Electronically Commutated) internal-rotor axial fans are critical devices, but their high-speed operation generates aerodynamic noise often exceeding 90 dB (A). While struts are core structural components regulating flow field stability, their specific geometric impact on trailing-edge vortex shedding and noise generation mechanisms remains unclear. This study investigates three strut configurations: a hexagonal annular type, a hexagonal double-ring type, and a three-pronged type. A coupled numerical model was established using Large Eddy Simulation (LES) and the Ffowcs Williams and Hawkings (FW-H) acoustic analogy. The Q-criterion was employed to analyze vortical structures, with numerical predictions validated against experimental measurements in a semi-anechoic chamber. The results quantitatively demonstrate that optimizing the strut geometry significantly mitigates unsteady flow separation. The three-pronged strut (Model C) effectively dispersed high-velocity airflow, reducing the peak turbulent kinetic energy (TKE) at the inlet by 30% compared to the original design (Model a). Furthermore, Model C achieved a 6.7 dB reduction in the sound pressure level at the blade-passing frequency (BPF), alongside a 14.1% reduction in pressure pulsation amplitude near the blade tip. Structural optimization of struts enables synergistic control over turbulence distribution and pressure fluctuations. By disrupting the phase coherence of shed vortices, the optimized design fundamentally suppresses aerodynamic noise, advancing axial fan design toward precise quantitative aeroacoustic optimization. Full article

A single-tank molten-salt heat-exchanger storage system is promising for small-scale industrial heat supply, yet transient natural convection and heat transfer in closed tanks remain insufficiently understood. This study develops a physical model and performs numerical simulations of a top-heated single-tank sensible thermal storage unit using a realistic post-discharge, non-uniform initial temperature field. During charging, an upward plume forms near the heating rod, with heat concentrated around the rod and weak flow in remote regions. Two large-scale circulation cells separated by an inclined thermocline are observed, and the interface shifts downward over time. To address short storage duration, a segmented-heating strategy is proposed by varying the heating-section height. Results show that heater height strongly governs flow and storage performance: compared with full-length heating, 2/3-, 1/2-, and 1/3-length configurations extend storage duration by 93%, 100%, and 103.9%, respectively. Lowering the heating zone toward the tank bottom effectively prolongs storage and improves thermal efficiency. Full article

Wind-assisted ship propulsion (WASP) systems are gaining importance due to their contribution to reducing greenhouse gases and saving fuel. Existing studies mostly focus on the aerodynamics of sailing systems, the integration of sails and ship dynamics, and the prediction of fuel savings. The present study extends the use case of sailing systems by proposing a new control logic that improves maneuvering performance. Determining the spin ratio of rotor sails not only with thrust but also with side forces and moments is also included as an objective function. Using numerous random weights for each term and environmental conditions, the turning performance of the target ship was evaluated. Then, an artificial neural network (ANN) model was trained to decide on the optimal weights, depending on the environmental conditions. Finally, the performance of the new control approach was evaluated based on turning and zigzag test simulations. It was found that the advance, transfer, and tactical diameters dropped by up to 5%, 7% and 7%, respectively, compared to those of a conventional ship. When it comes to the zigzag performance, it was revealed that the overshoot angles dropped even though there was no simulation data about zigzag tests in the trained ANN model. Thus, it was shown that sails improve the maneuverability of ships in addition to providing additional thrust if a proper control approach is adopted. Full article

The growing integration of generative artificial intelligence in logistics demands efficient simulation modeling. This study evaluates generative large language models, Perplexity and ChatGPT, for discrete-event simulation in ExtendSim. It focuses on modeling a real, complex manufacturing system, yielding 9721 tons of output. The following three scenarios were assessed: autonomous model creation, output estimation from process descriptions and parameters, and copilot-guided manual building. LLMs cannot autonomously construct ExtendSim models due to the lack of APIs. Output estimation only matched benchmarks after iterative prompt refinement, achieving errors of 0.1% for Perplexity and 1.2% to 22.8% for ChatGPT. Estimation without substantial human intervention proved infeasible. Only the copilot approach appeared viable despite initial errors. It enabled a validated model with 9718 tons output after resolving 25 errors for Perplexity and 22 for ChatGPT through iterative refinement. Approximately 28% (Perplexity) or 32% (ChatGPT) of the errors were hallucinations. The copilot approach reduced development time from several days to 8–10 h. Human expertise remained essential for verifying model outputs and addressing hallucinations and logical flaws. Consequently, this approach may be less feasible for inexperienced users. The copilot paradigm offers practical acceleration for experienced users; however, its limitations underscore the need for API integration and retrieval-augmented generation enhancements. Full article

Stadiums are large buildings that attract attention due to their high energy consumption and environmental impact. Considering the effects of climate change, the integration of sustainable energy solutions and energy efficiency is of great importance in the design and planning of these buildings. This study focuses on pitch lighting, which accounts for a significant and fluctuating share of energy consumption in stadiums, and aims to reduce its carbon footprint through the integration of renewable energy. This study aims to analyze the feasibility of achieving a net-zero annual energy balance for different levels of field lighting of a football stadium in accordance with FIFA lighting standards with solar energy systems in different climate zones and under future climate change scenarios. In addition, it is aimed at revealing the effect of climate change scenarios and climate zone differences on the azimuth angle, tilt angle, and area of the solar panel. In the study, a stadium model was created using parametric design—Grasshopper—and optimization software; lighting systems were designed according to FIFA standards, and lighting performance on the field was optimized with simulations through ClimateStudio and Galapagos. Based on Liverpool FC’s home match data, the annual illumination time is calculated, and the azimuth angle, tilt angle, and area of the solar panel systems are optimized for different climate scenarios. The most useful result of this study is that it demonstrates that the solar panel area required to meet stadium lighting needs varies depending on climate scenarios and geographical conditions and that the same energy production can be achieved with less panel area in low-emission scenarios. For instance, simulation results for Liverpool under the RCP 2.6 scenario show a decrease in the required panel area from 86.09 m² in 2050 to 84.27 m² by 2100. Similarly, in Moscow for the year 2050, the medium-emission scenario (RCP 4.5) requires a larger panel area (92.22 m²) compared to the low-emission RCP 2.6 scenario (88.12 m²) to achieve the same energy output. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

In response to the pressing issues of unclear adhesion mechanisms during the soil-removal process in peanut harvesting, poor soil fragmentation quality, and difficulties in separating the pods from the soil. Based on TRIZ theory, this study has innovatively designed a separation device that relies on external forces, such as kneading and squeezing. A mechanical model of soil fragmentation and separation was developed. The key factors affecting the device’s operational performance were identified. Through theoretical analysis and discrete element simulation, this study elucidates the working principle by which the device crushes and separates soil particles using kneading and squeezing forces. Through analysis of one-factor and orthogonal experiments, the optimal operating parameter combination for the device was determined to be: a drum installation clearance of 104.7 mm, a rotational speed difference of 75.2 rpm, and a pattern roughness of Grade III (reticulated). The system’s performance metrics are a soil removal rate of 96.59% and a pod damage rate of 2.48%. Field tests have confirmed that the deviation from simulation results is minimal. The device’s performance meets the requirements of actual production. Full article

This study develops an integrated mathematical model to investigate the interaction between tuberculosis (TB) transmission and childhood stunting, which is aligned with the United Nations Sustainable Development Goals (SDG 3). The population is structured into two age groups (0–5 years and ≥5 years), with stunting explicitly incorporated into the pediatric population to capture its potential influence on TB dynamics. The model is formulated as a system of ordinary differential equations and analyzed using equilibrium and stability analysis, with the basic reproduction number, R₀. The disease-free equilibrium is locally asymptotically stable when R₀ < 1, while an endemic equilibrium exists when R₀ > 1. Sensitivity analysis indicates that the transmission rate (β), progression rate from latent to active infection (σ), and recovery rate (γ) are the most influential parameters affecting R₀. These parameters are therefore selected as control variables in an optimal control framework to design effective intervention strategies. Numerical simulations show that the combined control strategy significantly reduces TB transmission, resulting in a reduction of more than 80% in active TB cases within a relatively short intervention period. The results suggest that integrated interventions targeting transmission, disease progression, and recovery are substantially more effective than single-measure strategies. This study provides a quantitative framework to support integrated public health policies addressing TB and childhood stunting simultaneously. Full article