Search Results (477)

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Conventional black-box models for electric vehicle (EV) battery State-of-Health (SOH) prediction achieve high accuracy but lack interpretability, limiting their practical deployment in Battery Management Systems (BMSs). To circumvent these limitations, this study proposes a novel Grey-Box Residual-Driven Framework (GBRDF) that synergizes Deep Symbolic Regression (DSR) with a residual-learning BiLSTM network with two contributions: (1) the DSR component derives explicit, interpretable mathematical expressions governing global degradation trajectories based on electrochemical features, and (2) the BiLSTM network models the residual errors to capture high-frequency nonlinearities and complex sequential dependencies not addressed by the symbolic baseline. By fusing the physics-informed transparency of DSR with the data-driven refinement of BiLSTM, the GBRDF significantly enhances forecasting precision. Experimental validation across four independent EV datasets shows that the GBRDF achieves the highest coefficient of determination (R²) of 0.982, and the lowest mean absolute error (MAE) of 0.1398 and root mean square error (RMSE) of 0.3176, significantly outperforming existing methods. Furthermore, the DSR-derived SOH equation shows that battery degradation is primarily driven by high voltage exposure and charging time, with mathematical transformations reflecting how degradation accelerates initially then slows, matching real-world aging patterns where voltage stress dominates over temperature and usage variations. Full article

Scanning electrochemical microscopy (SECM) provides spatially resolved electrochemical information but is constrained by long acquisition times arising from dense spatial sampling requirements. This work investigates whether physics-informed signal representations can improve neural reconstruction of SECM approach curve signals from sparse measurements. We propose an exponential function-based Neural Tangent Kernel (NTK) framework in which SECM signals are encoded using deterministic exponential feature mappings aligned with diffusion-controlled electrochemical dynamics. A layer-wise NTK checkpointing mechanism is employed to filter covariantly insignificant components during training, reducing redundancy while preserving dominant signal modes. The method is evaluated on synthetically generated SECM signals designed to replicate characteristic approach curve behavior. Quantitative performance is assessed using root mean square error (RMSE), mean absolute error (MAE), relative error (%), and the coefficient of determination (R²). Compared to a random Gaussian (Fourier feature) baseline (RMSE = 0.0952, MAE = 0.0547, Rel.Err = 17.68%), the proposed exponential mappings achieve consistently lower reconstruction error, with the best configuration yielding RMSE = 0.0858, MAE = 0.0375, and relative error = 11.10% under identical training conditions. Results demonstrate that incorporating physically motivated exponential feature representations into NTK-aware learning improves reconstruction fidelity and stability for low-dimensional electrochemical signals, highlighting the potential of physics-informed kernel methods for accelerated SECM data acquisition. Full article

The skyhook inertance (SHI) control strategy facilitates the real-time adaptation of inertance parameters to dynamic loading conditions, consequently enhancing vehicle ride comfort. It features a simple algorithm and strong robustness. However, traditional skyhook inertance systems only adjust the magnitude of the control force by changing the inertance, without regulating the control force phase, which limits the control effect of the SHI control strategy. To solve this problem, this study introduces a fractional-order skyhook inertance (Fo-SHI) control approach. This method substitutes the second-order differential terms appearing in the conventional equation of motion of the fractional-order skyhook inertance system with fractional-order derivatives of the displacement. Consequently, the proposed strategy enables continuous and independent tuning of both the amplitude and phase of the generated control force. To achieve a realistic representation of the Fo-SHI forces, a fractional-order model integrating an adjustable damper and an inerter was developed. This model was subsequently validated through prototype testing, and its parameters were identified via a fitting process. The results of Hardware-in-the-Loop experiments demonstrate that the semi-active suspension employing the Fo-SHI control strategy achieves significant performance improvements over the conventional SHI-controlled suspension: the root mean square of body acceleration is reduced by up to 18.12% under full-load conditions, while suspension working space and dynamic tire load also show favorable responses. These findings clearly underscore the advantages and rationale for incorporating fractional-order control into vehicle suspension systems. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

A high-speed magnetic pump rated at 7800 r/min was studied. A numerical model was established, and a hydraulic, vibration, and noise testing system was set up to conduct flow simulations, noise, and vibration experiments at different speeds. The results show that increasing speed leads to a higher pressure difference between the pump chamber and the cooling circuit. Meanwhile, the turbulent kinetic energy at the impeller outlet increases. Despite an increase in energy loss, the loss ratio decreases, and overall efficiency improves. The internal flow noise collected by the outlet hydrophone mainly comes from Rotor–Stator Interference (RSI), and it can sensitively capture changes in rotational speed. The dominant frequency of the outlet noise agrees well with the blade frequency calculated from the set speed, with a maximum deviation of 0.26%. As the speed increases, the overall sound pressure level (OASPL) at the inlet and outlet and the Root Mean Square (RMS) acceleration values at the outlet and pump body generally increase, while the acceleration at the motor base shows a decreasing trend. The conclusions are helpful for the design and optimization of rotary machinery such as high-speed magnetic pumps. Full article

Potholes are typical negative road obstacles that can significantly compromise vehicle safety and ride comfort when traversed at inappropriate speeds. To address this issue, this paper proposes a pothole-detection-based, comfort-oriented pothole traversal algorithm that integrates multi-sensor fusion perception, comfort-constrained speed planning, and fuzzy control. A camera and a single-point ranging LiDAR are first fused to extract key geometric features of potholes, including contour, area, and depth. Based on these features, a vehicle–pothole dynamic model is developed in ADAMS to quantify the influence of pothole area and depth on vehicle vertical vibration. The vertical frequency-weighted root-mean-square (RMS) acceleration is adopted as the ride comfort indicator, based on which the maximum allowable traversal speed under different pothole geometries is determined. Furthermore, a longitudinal pothole traversal control strategy based on fuzzy theory is designed to regulate vehicle acceleration, enabling the vehicle to reach the comfort-constrained limiting speed within a finite preview distance while ensuring braking safety. The proposed method is validated through multi-scenario co-simulations using MATLAB/Simulink and CarSim, as well as real-vehicle experiments. Results demonstrate that the proposed strategy can effectively adjust vehicle speed before pothole traversal, satisfying comfort constraints and improving ride comfort without sacrificing driving safety. Full article

The need for reliable software for data acquisition, processing and communication with laboratory instruments, as well as for extending laboratory findings to real-scale structures, is imperative. In this context, ReplicaXLite is presented: an open-source software framework designed to facilitate and organize structural experimental testing on seismic tables. The software enables the creation of digital twin models and real-time sensor data recording. Furthermore, it allows for the processing, storage and visualization of results within a graphical interface. It features two primary modes of operation: (a) via terminal with specific Application Programming Interfaces (APIs) and (b) via a Graphical User Interface (GUI), adapting to the user’s expertise level. The software lies on top of open-source libraries like OpenSeesPy and opstool. It supports many material types, such as concrete, steel, fibers and composites, among others. Models produced by ReplicaXLite demonstrate strong agreement with experimental data across varying structural configurations. For both acceleration and displacement, the framework yielded satisfactory accuracy at the top slab with mean envelope correlations ranging from 0.91 to 0.97 and mean Pearson correlations generally between 0.83 and 0.95 for varying seismic intensities (0.1 g to 1.4 g). The numerical framework successfully captured global stiffness degradation, with Normalized Root Mean Square Errors (NRMSE) well-constrained between 2.3% and 7.9% across both acceleration and displacement response metrics. The architecture allows for the one-click execution of custom user codes, providing full access to the source code and the ability to perform live toolkit modifications via the “app.” terminal variable. Finally, it provides mid-simulation modification of the mass and elements of the model. Full article

Cylinder–plate combined structures (CPCS) are widely used in aerospace, marine engineering, and offshore platform systems. During service, they are frequently subjected to stochastic excitations induced by turbulent boundary layers, acoustic loads, hydrodynamic disturbances, and broadband operational vibrations. Excessive random vibration responses may significantly reduce structural reliability, accelerate fatigue damage, and compromise operational safety. To address these engineering challenges, a unified stochastic dynamic analysis and vibration suppression framework is established for functionally graded graphene platelet-reinforced composites (FG-GPLRC) CPCS equipped with distributed dynamic vibration absorbers (DVAs). Adopting the First-order Shear Deformation Theory (FSDT), a comprehensive energy functional for the CPCS is established, in which the penalty method is implemented to impose boundary conditions and ensure interface continuity. Subsequently, the Pseudo-excitation Method (PEM) is utilized to convert the stochastic vibration analysis into an equivalent deterministic harmonic problem, and the governing equations are spatially discretized by combining the spectral geometric method (SGM) with the Ritz variational procedure, enabling efficient evaluation of power spectral density (PSD) and root-mean-square (RMS) responses. The reliability of the proposed model is verified through a series of numerical validation comparisons. On this basis, comprehensive parametric investigations are conducted to assess how material properties, structural geometries, and critical DVA parameters influence system behavior. The results demonstrate that the incorporation of distributed DVAs can achieve superior vibration suppression performance. This study provides an efficient and reliable theoretical framework for stochastic vibration analysis and damping design of advanced composite plate–shell coupled structures operating in complex random environments, offering important theoretical support for dynamic optimization design in aerospace and marine engineering applications. Full article

Traditional passive hydraulic dampers face the challenges of extended design cycles, inefficient parameter matching, and fixed performance, limiting their adaptability. This paper proposes an integrated solution that combines inverse parametric design with active, continuously adjustable damping. First, a high-fidelity nonlinear model is developed based on valve plate elasticity and multi-valve coupling dynamics, achieving a simulation error of ≤4%. An improved genetic algorithm is then designed to inversely optimize five key parameters. This optimization reduces the deviation between the prototype’s damping force–velocity characteristics and the target curve to ≤3% and shortens the design cycle by approximately 40%. Building on this foundation, a pilot-operated electro-hydraulic proportional relief valve is integrated to enable continuous damping adjustment. Co-simulation using AMESim2404 and MatlabSimulinkR2022 reveals the influence of solenoid valve parameters on damping characteristics and calibrates the current–damping force mapping. A co-simulation of a skyhook-controlled quarter-vehicle model demonstrates that the semi-active suspension system reduces the root mean square (RMS) of vertical body acceleration by 21.7%, indicating a significant theoretical improvement in ride comfort. This study establishes a complete technical pathway of “modeling → inverse optimization → integration → verification,” providing an efficient and viable core component solution for intelligent suspension systems. Full article

Aiming at the challenge of simultaneously controlling ride comfort and wheel grounding performance for mining dump trucks, this paper proposes a multi-dimensional synergistic optimization control (MDSOC) strategy based on model predictive control (MPC) for active hydro-pneumatic suspension. First, an accurate hydro-pneumatic suspension and hinged mining truck full-vehicle-dynamics model is established, and the model accuracy is validated through actual vehicle testing. Subsequently, an MDSOC-MPC for active hydro-pneumatic suspension is constructed to minimize the mean square root of the three-axis acceleration of the body, pitch angle, roll angle, and wheel dynamic tire load. Comparative analysis is performed with traditional single-MPC longitudinal, lateral, and vertical control, and the simulation results showed: under emergency braking conditions, the root mean square (RMS) value of the pitch angle is reduced by 18.2%; under single and double-shift conditions, the RMS values of the roll angle are reduced by 40.4% and 30%, respectively; under D-class random road, the RMS values of the longitudinal, lateral, and vertical body acceleration are significantly reduced by 22%, 21.5%, and 21.2%, respectively, while the RMS values of pitch angle and roll angle are reduced by 22.5%, and 20.2%, respectively, systematically improving riding comfort, vehicle wheel contact, and driving safety. This study provides a theoretical basis and feasible engineering methods for the active control of hydro-pneumatic suspension systems in heavy engineering vehicles. Full article

To address the poor cutting stability and deterioration of hole quality caused by the inherent trade-off between chip evacuation performance and drill-body stiffness in U-drilling, a multi-objective optimization framework was established. The design variables were the core thicknesses ${\mathrm{L}}_1$ and ${\mathrm{L}}_2$ of the inner and outer chip flutes, the inner and outer offset angles ${\mathrm{\theta }}_1$ and ${\mathrm{\theta }}_2$, and the inner and outer helix angles ${\mathrm{\beta }}_1$ and ${\mathrm{\beta }}_2$. The objectives were to maximize the chip evacuation force and minimize the drill-body strain (which serves as an equivalent indicator of maximizing drill-body stiffness). The chip evacuation force was rapidly evaluated using a mechanistic chip evacuation force model derived from mechanism-based analysis. The drill-body strain was efficiently predicted using a GA–BP neural-network surrogate model. An NSGA-II algorithm combined with the entropy-weighted TOPSIS method was employed to solve the optimization problem, yielding the optimal parameter combination for the U-drill chip-flute geometry. The results show that drilling experiments on 42CrMo under the optimal structural parameter combination reduced the cutting forces in the $x$, $y$, and $z$ directions by approximately 11.2%, 13.1%, and 11.8%, respectively. The root-mean-square acceleration in the $x$ and $y$-directions decreased by about 17.3% and 22.9%, respectively. These improvements effectively enhanced the hole-wall surface roughness and hole diameter accuracy, and further improved chip evacuation smoothness and cutting stability of the U-drill. Full article

The mechanization of the agricultural sector exposes operators to vibrations generated by tractors, terrain inclination, and attached implements. Prolonged exposure to such vibrations can lead to health problems, including visual disturbances, fatigue, spinal injuries, and low back pain. In this context, the present study aimed to assess the severity of mechanical vibrations in an agricultural tractor with four-wheel drive, both as a standalone unit and as part of a mechanized assembly comprising the same tractor coupled to a fertilizer seeder during sowing operations. Vibrations were monitored at four data collection points: the front and rear axles, the cab floor, and the operator’s seat. Root mean square (RMS) acceleration values were compared with the limits established by ISO 2631-1, and the comfort levels at the operator’s seat were classified as “uncomfortable” and “very uncomfortable.” Vibration transmissibility between the rear axle and the cab floor (T2) was found to exceed 1, indicating amplification of vibrations. Overall, the operator’s seat attenuated the vibration severity transmitted to the operator. Forward speed significantly influenced vibration severity, with higher speeds associated with increased RMS accelerations. Slope also affected vibration levels, with slope D2 (the sloped area) presenting higher mean RMS acceleration values. Notably, the tractor operating with the seeder fertilizer exhibited attenuated vibration levels compared to the tractor alone. Full article

The growing need for sustainable and lightweight building materials has accelerated research on alternatives to conventional concretes, out of which Lightweight Expanded Clay Aggregate (LECA) concrete has emerged as a promising solution. However, the high porosity and nonlinear mechanical behavior of LECA concrete complicate the accurate prediction of compressive strength through conventional empirical models. The main focus of the paper is on identifying a comprehensive machine learning-based framework for modeling and predicting the 28-day compressive strength of LECA-based lightweight concrete. The dataset was created and preprocessed by using statistical normalization and correlation analysis. In this study, five supervised machine learning models—Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were developed and fine-tuned using a grid-search strategy combined with ten-fold cross-validation. The quality of the prediction made by each model was evaluated by means of standard performance indicators, such as the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). After the evaluation, the models were subsequently compared and ranked according to the Gray Relational Analysis (GRA) method. The comparative assessment shows that CatBoost demonstrated the most reliable performance, achieving an R² of 0.907, RMSE of 3.41 MPa, MAE of 2.47 MPa, and MAPE of 10.05%, outperforming the remaining algorithms. To interpret the significance of features, SHAP (Shapley Additive exPlanations) analysis was applied, which identified water and LECA content as the dominant factors influencing compressive strength, followed by the cement and fine aggregate proportions. The findings reveal that the ensemble-based gradient boosting model is capable of capturing intricate nonlinear interactions, as observed in the heterogeneous matrix of LECA concrete. Full article