Search Results (205)

Previous studies on maglev vehicle–bridge coupled systems have mostly described the bridge using classical boundary conditions, while the effects of general constrained boundaries and uncertain parameters have not been fully considered. In this study, an energy-based dynamic model of a maglev vehicle–bridge coupled system is established. The boundary constraints of the bridge are introduced through equivalent springs, so that complex boundary conditions can be represented in a unified form. The proposed model is verified by comparison with published results. On this basis, Monte Carlo simulations are carried out to investigate the effects of random suspension parameters and random control parameters on the dynamic responses of the system. Two simplified electromagnetic-force models are also considered, and Sobol sensitivity analysis is used to evaluate the contributions of different parameters to the vibration responses and vibration energy. The results indicate that the suspension and control parameters affect different response quantities in different ways. The two electromagnetic-force models also lead to different sensitivity results, especially when the vibration energy is used as the evaluation index. The proposed method provides a useful tool for analyzing the stochastic vibration mechanism and optimizing the parameters of maglev vehicle–bridge coupled systems under general constrained boundaries. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

Complex optimization problems are widely encountered in engineering design, intelligent manufacturing, communication systems, and wireless sensor network deployment. However, the original Enterprise Development Optimization Algorithm (EDOA) still suffers from insufficient population diversity, weak search guidance, and limited adaptability in balancing exploration and exploitation when solving high-dimensional and multimodal optimization problems. To address these issues, this paper proposes a Multi-Strategy Improved Enterprise Development Optimization Algorithm (MIEDOA). First, a Strategic Diversification Initialization (SDI) strategy is developed by integrating Sobol sequence sampling, random initialization, and Gaussian perturbation to improve the diversity and distribution quality of the initial population. Second, an Organizational Synergy Learning (OSL) mechanism is introduced to enhance search guidance through the collaborative utilization of elite information, population mean information, and peer interaction. Third, an Adaptive Governance with Feedback Regulation (AGFR) strategy is designed to dynamically regulate the exploration–exploitation behavior according to the current population fitness state. The proposed MIEDOA is evaluated on the CEC2017 and CEC2020 benchmark suites and compared with representative EDOA variants, CEC winner algorithms, and other advanced optimization methods. The experimental results indicate that MIEDOA generally achieves competitive performance in terms of solution quality, convergence behavior, and robustness across different benchmark scenarios. In addition, strategy effectiveness analysis, parameter sensitivity analysis, and statistical tests further provide evidence supporting the effectiveness of the proposed strategies. Finally, MIEDOA is applied to a three-dimensional wireless sensor network deployment problem. The results suggest that the proposed algorithm can obtain competitive deployment solutions and satisfactory coverage performance under different node scales, demonstrating its potential applicability to practical engineering optimization problems. Full article

Seismic reliability assessment of steel frame structures using nonlinear finite element analysis is often hindered by implicit limit state functions and high computational cost. To address these challenges, this study proposes a machine learning-based framework for global seismic reliability analysis. A nine-story steel frame model is established and validated through modal and pushover analysis. Global sensitivity analysis using the Sobol’ method is performed to identify key parameters governing the maximum inter-story drift ratio. Three machine learning models—PSO-SVR, PSO-XGBoost, and PSO-BPNN—are trained with the selected features and integrated into Monte Carlo simulation (MCS) for reliability calculation. The results show that the PSO-BPNN model achieves the highest accuracy with the maximum error of 1.0259% relative to direct MCS, outperforming the conventional MLE-based approach, which yields errors up to 11.9383% due to the non-standard distribution of the structural response. The impact of training sample size on model performance is also examined, with 1000 samples identified as a practical threshold for acceptable prediction accuracy. Existing code design methods require modifications based on the total probability approach for global reliability analysis. This study offers an efficient and precise methodology for seismic reliability design of steel frame structures, particularly when structural responses deviate from standard parametric distributions. Full article

The AC rotary head, serving as a dual-axis direct-drive rotary actuation unit in five-axis CNC machine tools, integrates torque motors for A- and C-axis actuation, and its structural static and dynamic characteristics directly govern the actuation accuracy, dynamic response, and stability of the electromechanical system. Its complex spatial pose variations further complicate performance prediction. To overcome the difficulty of existing local optimization methods in balancing stiffness-mass matching for such complex actuation assemblies, this paper proposes a static and dynamic performance optimization method based on stiffness-mass matching. First, a pose-dependent semi-analytical dynamic model is established using dynamic condensation and component mode synthesis (CMS) to reveal performance distribution laws across the workspace and identify weak poses. Then, Sobol’ sensitivity analysis identifies key joints and structural components, and the NSGA-II algorithm optimizes their stiffness-mass matching. Finally, a surrogate model performs dimensional parameter optimization targeting the optimized matrices. Results show that the first-order natural frequency increases by 10.5%, translational static stiffness in the X and Y directions improves by over 20%, and other directions by 4.2–18.6%. The proposed method effectively enhances global static and dynamic performance, providing theoretical guidance for the structural design of direct-drive rotary actuators in electromechanical actuation systems. Full article

As critical underground engineering structures, wellbores may suffer complex structural deterioration and hidden safety hazards may be encountered during drilling. Multi-source sensor monitoring data provides an effective data basis for structural health perception and early warnings for wellbore structures at risk. The inherent diversity of formation conditions and the dynamic disturbances during drilling jointly lead to the differentiated presentation of drilling loss types, among which fractured, permeable, and vuggy losses are the most typical. This paper focuses on fractured wellbore leakage, regards wellbore leakage as an important structural failure form of underground drilling engineering structures. In-depth analysis and research on the structural deterioration mechanism of wellbore leakage were conducted, and we propose a wellbore leakage prediction method based on the improved sparrow search algorithm (ISSA) optimized gradient boosting decision tree (XGBoost). First, the Sobol sequence is adopted to replace the random initialization strategy, combined with the opposition-based learning mechanism; then, an adaptive Levy flight search mechanism is introduced to dynamically adjust the population ratio of discoverers and vigilantes; finally, intelligent optimization technologies are integrated to reconstruct the position update strategies of discoverers, followers, and vigilantes, enhancing the optimization adaptability of the algorithm. Relying on multi-field sensor monitoring datasets collected from actual drilling engineering, this paper compares the proposed model with wellbore leakage prediction models built by classical machine learning algorithms, and verifies its generalization ability on different datasets. Experimental data indicate that the improved algorithm exhibits significant advantages in optimization accuracy, enabling the proposed model to achieve an AUC improvement of 4.46%, along with accuracy (95.1%), precision (94.9%), recall (94.7%), and F1-score (94.2%). On this basis, the ISSA was applied to the hyperparameter optimization of XGBoost, constructing the ISSA-XGBoost prediction model. The method has high accuracy and good generalization ability in fractured wellbore leakage prediction, and it can realize intelligent health monitoring of underground wellbore structures, including early warnings. This study provides a reliable sensing data analysis scheme and technical support for structural health monitoring and hazard prevention in drilling engineering. Full article

This study investigates the reliable numerical analysis of chaotic dynamics in the Glukhovsky–Dolzhansky system, which models convective fluid motion in a rotating ellipsoidal cavity. Hidden and self-excited attractors are localized using the numerical continuation method (NCM), Pyragas time-delayed feedback control, and Leonov’s analytical dimension formula following global stability loss. A critical assessment of Lyapunov exponents and Lyapunov dimensions in a finite-time setting shows that positive values over long but finite intervals may incorrectly indicate sustained chaos due to transient effects and shadowing breakdown. Furthermore, we demonstrate that the fractional order $\gamma$ plays a bidirectional control role: it induces chaotic behavior at $\rho =5$ for $\gamma <0.94$ and suppresses chaos at $\rho =15$ for $\gamma <0.93$. The multifractal spectrum and correlation dimension are used to quantify attractor complexity, where transient chaos exhibits a broader spectrum ($\Delta \alpha \approx 0.67$) compared to sustained chaos ($\Delta \alpha \approx 0.48$). Monte Carlo simulations, Sobol sensitivity analysis, Kaplan–Meier survival analysis, and bootstrap-based hypothesis testing confirm the robustness of the results. Overall, the findings provide a unified framework for analyzing hidden attractors, transient chaos, and fractional-order effects in nonlinear fluid dynamical systems. Full article

Improving indoor thermal comfort in high-altitude rural housing remains a persistent challenge for low-income communities in the Peruvian Andes. This study evaluates the thermal performance of a standardized Sumaq Wasi modular dwelling in Langui (Cusco, Peru, 3969 m.a.s.l.) and proposes passive envelope modifications that enhance comfort while preserving economic feasibility. A multi-objective optimization approach combining EnergyPlus simulations with the NSGA-II algorithm was applied to minimize total thermal discomfort ($TD{I}_{total}$), bedroom underheating ($TDI{U}_{bedrooms}$), and 10-year life cycle costs ($LCC$). The calibrated model incorporated field measurements of indoor air temperatures. Global sensitivity analysis using Morris and Sobol methods identified ceiling thermal transmittance as the dominant contributor for $TD{I}_{total}$, and exterior wall solar absorptance as the driver of $TDI{U}_{bedrooms}$. Optimization reduced $TD{I}_{total}$ and $TDI{U}_{bedrooms}$ to 22% and 8% of the base case, requiring additional investments of USD 2347 and USD 1959, respectively, above the base case cost (USD 8100). Cost-neutral strategies, raising exterior wall solar absorptance to 0.9 and increasing the skylight-to-roof ratio (13.1%), reduced bedroom underheating to 30% of the base case and outperformed a scenario with two 400 W electric heaters. These results demonstrate that context-appropriate passive design can substantially improve comfort under severe climatic and financial constraints. Full article

In predicting tubing corrosion rates under multi-factor coupling, traditional methods often struggle to effectively analyze the nonlinear interactions among variables such as temperature, pressure, CO₂ partial pressure, and H₂S partial pressure, and they also lack interpretability in the prediction process. To address this, this study first establishes a corrosion dataset covering three typical steels (2205DSS, CT80, N80) through high-temperature and high-pressure weight-loss experiments. A machine learning framework is then proposed, integrating feature coupling analysis with a SHAP-Sobol-based interpretability framework. By incorporating the Context-Aware Sparse Attention (CASA) mechanism into the XGBoost ensemble, a CASA-XGBoost prediction model is constructed to systematically analyze interactions among multiple features and convert them into effective predictive information. Bayesian optimization enables adaptive hyperparameter tuning, while five-fold cross-validation tailored to different materials enhances model generalization and stability. Furthermore, the SHAP-Sobol weighting method systematically evaluates feature contributions and interaction effects across global sensitivity analysis and local sample interpretation, enabling feature coupling reconstruction. Experimental results demonstrate that the proposed framework outperforms benchmark models (Random Forest and Gaussian Process Regression) on three steel corrosion datasets, achieving test set R² values up to 0.98 with a low MAE and RMSE. The SHAP-Sobol-based interpretability framework also reveals material-specific sensitivities: 2205DSS is highly influenced by CO₂-H₂S interaction, CT80 by temperature–pressure coupling, and N80 shows reduced performance at high corrosion rates due to localized mechanisms. This study provides a reference for corrosion prevention and control by delivering high-accuracy and interpretable corrosion rate prediction for tubing under multi-factor coupling conditions, offering practical value for industrial modeling and decision-making. Full article

Accurately estimating the state of health (SOH) of lithium-ion batteries remains challenging for battery management systems. Traditional data-driven methods, such as long short-term memory (LSTM), lack physical interpretability and often fail to generalize across varying operating conditions. To address this, a physics-informed SP-LSTM framework is proposed that integrates the single particle model (SPM) with a bidirectional LSTM network. A hybrid optimization strategy combining particle swarm optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounds (L-BFGS-B) is first used to identify key SPM parameters, which are then combined with macro external features (charging time, discharge energy, IC peak) to form a seven-dimensional fusion vector. A dual-stream Bi-LSTM architecture separately models fast-varying macro trends and slow-varying physical parameters, achieving robust SOH mapping. Validated on the NASA PCoE dataset, the proposed SP-LSTM achieves a root mean square error (RMSE) of 0.0136 and a mean absolute error (MAE) of 0.0089 on an independent test set (B0018), outperforming the baseline LSTM by 38.2% in RMSE. Noise robustness tests (0–3% voltage noise) and Sobol global sensitivity analysis further confirm its stability and interpretability. By embedding electrochemical priors into the data-driven pipeline, this work provides a practical physics-data collaborative framework for accurate and trustworthy battery SOH estimation. Full article

Uncertainty quantification (UQ) is essential for the robust and competitive design of climate-friendly transportation systems, such as aircraft and space launch systems. However, supporting software applications for UQ are fragmented across numerous open-source libraries, often require in-depth knowledge of the mathematics underlying UQ, and commercial solutions often involve licensing costs. This can make it difficult for design experts to take uncertainties into account. To address this issue, we propose a modular, web-based framework that will guide practitioners through the most common UQ processes, such as statistical sampling, propagation through design workflows, and statistical analysis of the results. Adopting a modern client-server architecture, a backend service, called uqFramework, wraps relevant software libraries for each of the aforementioned steps. The current version focuses on probabilistic approaches, enabling the generation of Design-of-Experiment (DOE) inputs via Quasi-Monte Carlo, Latin Hypercube, and Low Discrepancy Sequence sampling methods. Furthermore, it enables the parallel execution of design and analysis workflows via DLR’s Remote Component Environment (RCE) or Python scripts. Finally, uqFramework performs global sensitivity analyses using Sobol, FAST, or Morris techniques. An interactive front-end application called uqStudio connects to uqFramework through a Representational State Transfer (REST) interface. It guides users through the UQ process via an intuitive, step-by-step interface. Interactive visualizations enable detailed exploration of each step. The framework’s capabilities are illustrated through two examples, the Ishigami function and a multidisciplinary UAV design study, verifying its precision, adaptability, and user-friendliness. We demonstrate that uqStudio enables researchers to conduct integrated UQ studies covering uncertainty specification, propagation, and sensitivity analysis without the difficulty of installing and properly using fragmented libraries. Future work includes extending visualization capabilities and integrating surrogate-modeling capabilities to enable faster workflow execution. Full article

Ammonia (NH₃) from pig houses contributes to air-quality degradation and odor, yet farm-level emissions are highly sensitive to housing design, slurry chemistry and management. This study developed and validated a minute-resolution dynamic model for indoor NH₃ concentration and house-level emission in a mechanically ventilated finishing pig house. Volatilization from the slurry surface was computed from total ammonia nitrogen (TAN), pH and temperature using established mass-transfer formulations, and coupled between two zones (pit headspace and room airspace) via advection and diffusion across the slatted-floor open area. Over one production cycle, key drivers and indoor NH₃ were monitored; discrete TAN observations were upsampled to minute resolution by linear interpolation. Model coefficients were optimized by a genetic algorithm with chronological 70/30 splits for calibration and validation in the grower and finisher phases, respectively. The calibrated model reproduced minute-scale dynamics (validation RMSE 1.53–1.76 ppm, R² 0.87–0.88; MAPE 9.95–10.87%). Sobol’s global sensitivity analysis identified ventilation rate as the dominant driver of indoor concentration, and TAN and slurry pH as the principal drivers of emissions. The model provides decision support for minute-scale monitoring and management, and can be integrated with factor-control methods and ICT-based supervisory systems. Full article

Low-altitude terrain-following flight is essential for obtaining high-quality data in unmanned aerial vehicle (UAV) aeromagnetic surveys, but achieving efficient and safe path planning within complex terrains remains challenging. To address this issue, a Q-learning-assisted multi-strategy Starfish Optimization Algorithm (QMSFOA) is proposed for offline path planning. The proposed algorithm integrates four improvement strategies: (1) employing a Sobol sequence combined with Refraction Opposition-based Learning for population initialization to enhance population diversity; (2) adopting a hybrid adaptive differential mutation mechanism to improve search efficiency; (3) utilizing Q-learning to intelligently schedule optimization modes, thereby accelerating convergence speed; (4) introducing an adaptive t-distribution elite perturbation strategy to refine convergence accuracy. Experimental results on the CEC-2022 benchmark suite indicate that QMSFOA achieves the best convergence accuracy on nine functions and exhibits a superior performance across most metrics compared with the competing algorithms. Simulation experiments of aeromagnetic surveys in complex 3D terrains demonstrate that paths planned by QMSFOA satisfy kinematic and obstacle avoidance constraints while reducing path costs by approximately 25% compared with the standard Starfish Optimization Algorithm (SFOA). Additionally, the standard deviation is reduced by one to two orders of magnitude compared with the competing algorithms. These results demonstrate that the proposed method provides an efficient, reliable, and intelligent solution for high-precision UAV geophysical exploration in complex environments. Full article

Under hull–propulsor coupling conditions, the geometric shape of an unmanned underwater vehicle (UUV) can significantly affect the inflow conditions of the aft rim-driven thruster (RDT) and, consequently, its propulsive performance. However, the number of UUV shape design parameters is relatively large, and their influences on the propulsive efficiency of the RDT differ markedly. If an equal-weight search strategy is still adopted for optimization, the computational cost will increase and the optimization efficiency will be reduced. To address this issue, this paper proposes an efficient global-sensitivity-information-driven sequential surrogate-based optimization method for the shape optimization design of the UUV, with the aim of improving the propulsive efficiency of the RDT corresponding to the self-propulsion equilibrium state under the cruise condition. Based on the hull–propulsor coupled numerical model of the UUV and RDT, the proposed method obtains the propulsive efficiency of the RDT at the self-propulsion point under the cruise condition by solving the self-propulsion equilibrium condition. On this basis, Sobol global sensitivity analysis is performed using the Kriging surrogate model to quantitatively evaluate the influence of the UUV shape design parameters on the propulsive efficiency of the RDT. Then, the global sensitivity information is mapped into optimization weights. Based on this, the minimum of surrogate prediction (MSP) and expected improvement (EI) sampling criteria are introduced. In this way, a surrogate model sequential optimization method driven by global sensitivity information is developed. The optimization results show that, after optimizing the UUV external shape, the propulsive efficiency of the RDT under the cruise condition is increased by 22.83%, thereby verifying the effectiveness of the proposed method. Full article

Unsignalized intersections pose a representative challenge for autonomous-driving decision-making because online planning must satisfy tightly coupled requirements for safety, task completion, traffic efficiency, and control smoothness under a limited computation budget. Existing continuous-action MCTS planners often suffer from sparse candidate-action coverage and from the absence of an internal safety filter before node expansion. To address these issues, this paper proposes CP-LDS-MCTS, a decision-making framework that coordinates Sobol low-discrepancy sampling, truncated Taylor control barrier function (TTCBF)-based safety pruning, and policy-value composite scoring within the expansion stage of Monte Carlo tree search. Sobol sampling improves candidate representativeness under a fixed sampling budget; TTCBF provides a local one-step screening rule that removes actions inconsistent with safety constraints before search resources are consumed; and composite scoring prioritizes safe actions that are simultaneously policy-consistent and value-promising. To clarify the methodological contribution, CP-LDS-MCTS is formulated as a unified expansion-stage design rather than a loose combination of independent modules. The revised manuscript further adds a local approximation-error discussion for the TTCBF truncation, a computational-complexity analysis, a real-time latency evaluation, statistical significance tests, and two stronger baselines, namely PPO and MPC-CBF. Experiments in CARLA Town03 under low-, medium-, and high-density traffic show that the proposed method achieves the best overall balance among safety, success rate, travel time, and control smoothness while maintaining a mean planning latency below 25 ms per step on the test platform. The resulting safety assurance is local rather than global, as TTCBF pruning performs a one-step approximation-based feasibility check within the expansion stage and is validated in simulation. These results suggest that candidate coverage, internal safety screening, and value-aware expansion should be designed jointly for real-time continuous-action planning at unsignalized intersections. Full article