Search Results (1,016)

This study addresses the problems of poor dynamic stability, high vibration coupling, and inefficient energy use in large-farm manure handling machines. A profiling wheel-based multi-disciplinary approach is proposed in the study. With the rocker arm prototype, double-ball heads, and a hydraulic damping system, a parametric design is built that includes vibration and energy consumption. The simulation results in EDEM2022 and ANSYS2022 prove the structure viability and motion compensation capability, while NSGA-II optimizes the damping parameters (k₁ = 380 kN/m, C = 1200 Ns/m). The results show a 14.7% σ_Fc reduction, 14.3% α_RMS decrease, resonance avoidance (14–18 Hz), Δx (horizontal offset of the frame) < 5 mm, 18% power loss to 12.5%, and 62% stability improvement. The new research includes constructing a dynamic model by combining the Hertz contact theory with the modal decoupling method, while interacting with an automatic algorithm of adaptive damping and a mechanical-hydraulic-control-oriented optimization platform. Future work could integrate lightweight materials and multi-machine collaboration for smarter, greener manure cleaning. Full article

This study, inspired by the impact resistance of the pistol shrimp’s predatory claw, investigates the design and optimization of bionic energy absorption structures. Four types of bionic hierarchical porous metamaterial lattice structures with a negative Poisson’s ratio were developed based on the microstructure of the pistol shrimp’s fixed claw. These structures were validated through finite element models and quasi-static compression tests. Results showed that each structure exhibited distinct advantages and shortcomings in specific evaluation indices. To address these limitations, four new bionic structures were designed by coupling the characteristics of the original structures. The coupled structures demonstrated a superior balance across various performance indicators, with the EOS (Eight pillars Orthogonal with Side connectors on square frame) structure showing the most promising results. To further enhance the EOS structure, a parametric study was conducted on the distance d from the edge line to the curve vertex and the length-to-width ratio y of the negative Poisson’s ratio structure beam. A fifth-order polynomial surrogate model was constructed to predict the Specific Energy Absorption (SEA), Crush Force Efficiency (CFE), and Undulation of Load-Carrying fluctuation (ULC) of the EOS structure. A multi-objective genetic algorithm was employed to optimize these three key performance indicators, achieving improvements of 1.98% in SEA, 2.42% in CFE, and 2.05% in ULC. This study provides a theoretical basis for the development of high-performance biomimetic energy absorption structures and demonstrates the effectiveness of coupling design with optimization algorithms to enhance structural performance. Full article

In response to China’s dual-carbon strategy, this study proposes a comprehensive analytical framework to identify the evolutionary pathways of key policy tasks in developing a new-type power system. A dual-channel data acquisition process was designed to extract, standardize, and segment policy documents and online texts into a unified corpus. A multi-label BERT classification model was then developed, incorporating domain-specific terminology injection, label-wise attention, dynamic threshold scanning, and imbalance-aware weighting. The model was trained and validated on 200 energy news articles, 100 official policy releases, and 10 strategic planning documents. By the 10th epoch, it achieved convergence with a Macro-F1 of 0.831, Micro-F1 of 0.849, and Samples-F1 of 0.855. Ablation studies confirmed the significant performance gain over simplified configurations. Structural label analysis showed “Build system-friendly new energy power stations” was the most frequent label (107 in plans, 80 in news, 24 in policies) and had the highest co-occurrence (81 times) with “Optimize and strengthen the main grid framework.” The label co-occurrence network revealed multi-layered couplings across generation, transmission, and storage. The Priority Evaluation Index (PEI) further identified “Build shared energy storage power stations” as a structurally central task (centrality = 0.71) despite its lower frequency, highlighting its latent strategic importance. Within the domain of national-level public policy and planning documents, the proposed framework shows reliable and reusable performance. Generalization to sub-national and project-level corpora is left for future work, where we will extend the corpus and reassess robustness without altering the core methodology. Full article

In this study, the impact resistance of WUF-B steel frame beam–column joints under fire was investigated using ABAQUS finite element software through a sequential thermal–mechanical coupling approach. By integrating a room-temperature impact model with a single-sided fire field applied to the lower flange of the steel beam, the multi-parameter influence mechanisms—including temperature (150–750 °C), fire area distribution, and impact momentum—were systematically analyzed. Results indicate that elevated temperatures significantly degrade structural impact resistance. At 750 °C, the peak impact force decreases by 73.3% compared to room temperature, while the mid-span bending moment increases by 63.3%. When the fire zone is near the impact point, localized thermal softening further reduces the peak impact force. Under constant impact energy, lower momentum (i.e., higher velocity) accelerates the rebound of the falling mass, revealing the role of momentum transfer efficiency in governing the transient response of high-temperature structures. Additionally, an analytical prediction model based on Timoshenko beam theory and thermo-mechanical stiffness degradation is developed. By introducing a segmented temperature reduction function, the model significantly enhances the accuracy of mid-span displacement predictions for steel structures under fire. Full article

We consider a multi-species mixture of interacting bosons, $N_1$ bosons of mass $m_1$, $N_2$ bosons of mass $m_2$, and $N_3$ bosons of mass $m_3$, in a harmonic trap with frequency $\omega$. The corresponding intra-species interaction strengths are ${\lambda }_{11}$, ${\lambda }_{22}$, and ${\lambda }_{33}$, and the inter-species interaction strengths are ${\lambda }_{12}$, ${\lambda }_{13}$, and ${\lambda }_{23}$. When the shape of all interactions is harmonic, the system corresponds to the generic multi-species harmonic-interaction model, which is exactly solvable. We start by solving the many-particle Hamiltonian and concisely discussing the ground-state wavefunction and energy in explicit forms as functions of all parameters, the masses, numbers of particles, and the intra-species and inter-species interaction strengths. We then explicitly compute the reduced one-particle density matrices for all the species and diagonalize them, thus generalizing the treatment by the authors earlier. The respective eigenvalues determine the degree of fragmentation of each species. As an application, we focus on phenomena that do not arise in the corresponding single-species or two-species systems. For instance, we consider a mixture of two kinds of bosons in a bath made by a third kind, controlling the fragmentation of the former by coupling to the latter. Another example exploits the possibility of different connectivities (i.e., which species interacts with which species) in the mixture, and demonstrates how the fragmentation of species 3 can be manipulated by the interaction between species 1 and species 2, when species 3 and 1 do not interact with each other. We highlight the properties of fragmentation that only appear in the multi-species mixture. Further applications are briefly discussed. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

The coupling of quantum computing with multi-agent reinforcement learning (MARL) provides an exciting direction to tackle intricate decision-making tasks in high-dimensional spaces. This work introduces a new quantum-inspired multi-agent reinforcement learning (QiMARL) model, utilizing quantum parallelism to achieve learning efficiency and scalability improvement. The QiMARL model is tested on an energy distribution task, which optimizes power distribution between generating and demanding nodal power stations. We compare the convergence time, reward performance, and scalability of QiMARL with traditional Multi-Armed Bandit (MAB) and Multi-Agent Reinforcement Learning methods, such as Greedy, Upper Confidence Bound (UCB), Thompson Sampling, MADDPG, QMIX, and PPO methods with a comprehensive ablation study. Our findings show that QiMARL yields better performance in high-dimensional systems, decreasing the number of training epochs needed for convergence while enhancing overall reward maximization. We also compare the algorithm’s computational complexity, indicating that QiMARL is more scalable to high-dimensional quantum environments. This research opens the door to future studies of quantum-enhanced reinforcement learning (RL) with potential applications to energy optimization, traffic management, and other multi-agent coordination problems. Full article

The growing deployment of electric vehicles (EVs) offers a unique opportunity to utilize them as mobile energy resources during large-scale emergencies. However, existing emergency dispatch strategies often neglect the compounded uncertainties of hazard disruptions, infrastructure fragility, and user behavior. To address this gap, we propose the Emergency-Responsive Aggregation Framework (ERAF)—a behaviorally informed, spatially aware, and probabilistic optimization model for resilient EV energy dispatch. ERAF integrates a Bayesian inference engine to estimate plug-in availability based on hazard exposure, behavioral willingness, and charger operability. This is dynamically coupled with a GIS-based spatial filter that captures road inaccessibility and corridor degradation in real time. The resulting probabilistic availability is fed into a multi-objective dispatch optimizer that jointly considers power support, response time, and delivery reliability. We validate ERAF using a high-resolution case study in Southern California, simulating 122,487 EVs and 937 charging stations across three compound hazard scenarios: earthquake, wildfire, and cyberattack. The results show that conventional deterministic models overestimate dispatchable energy by up to 35%, while ERAF improves deployment reliability by over 28% and reduces average delays by 42%. Behavioral priors reveal significant willingness variation across regions, with up to 47% overestimation in isolated zones. These findings underscore the importance of integrating behavioral uncertainty and spatial fragility into emergency energy planning. ERAF demonstrates that EVs can serve not only as grid assets but also as intelligent mobile agents for adaptive, decentralized resilience. Full article

With the increase in energy density of power batteries, the risk of thermal runaway significantly increases under extreme working conditions. Therefore, this article proposes a biomimetic liquid cooling plate design based on the fractal structure of fir needle leaf veins, combined with Murray’s mass transfer law, which has significantly improved the heat dissipation performance under extreme working conditions. A multi-field coupling model of electrochemistry fluid heat transfer was established using ANSYS 2022 Fluent, and the synergistic mechanism of environmental temperature, coolant parameters, and heating power was systematically analyzed. Research has found that compared to traditional serpentine channels, leaf vein biomimetic structures can reduce the maximum temperature of batteries by 11.78 °C at a flow rate of 4 m/s and 5000 W/m³. Further analysis reveals that there is a critical flow rate threshold of 2.5 m/s for cooling efficiency (beyond which the effectiveness of temperature reduction decreases by 86%), as well as a thermal saturation temperature of 28 °C (with a sudden increase in temperature rise slope by 284%). Under low-load conditions of 2600 W/m ³, the system exhibits a thermal hysteresis plateau of 40.29 °C. To predict the battery temperature in advance and actively intervene in cooling the battery pack, based on the experimental data and thermodynamic laws of the biomimetic liquid cooling system mentioned above, this study further constructed a support vector machine (SVM) prediction model to achieve real-time and accurate prediction of the highest temperature of the battery pack (validation set average relative error 1.57%), providing new ideas for intelligent optimization of biomimetic liquid cooling systems. Full article

Shaft alignment is adversely affected by the increasingly severe coupled hull–raft deformation in deep-diving, highly integrated submersibles, thereby compromising operational safety and potentially amplifying vibration noise. To address to this issue, this paper investigates an intelligent alignment control method for the floating raft air spring mounting system (ASMS) applied to marine propulsion unit (MPU) under coupled hull–raft deformation conditions. A multi-objective alignment control algorithm was developed based on the NSGA-II optimization method within an N-step receding horizon optimal control framework, enabling simultaneous achievement of shaft alignment attitude adjustment, hull deformation compensation, raft deformation suppression, and pneumatic energy consumption. Experimental validation was conducted on two distinct ASMS prototypes to evaluate the control algorithm. Tests performed on the ASMS for MPU (MPU-ASMS) prototype demonstrated effective compensation of hull-induced deformations, maintaining shaft alignment offsets within ±0.3 mm and angularities within ±0.5 mm/m. Concurrently, experiments on the floating raft ASMS for the stern compartment (SC-FR-ASMS) achieved precise control of axial offsets within ±0.3 mm, angularities within ±0.5 mm/m, and vertical displacements of critical monitoring points within ±1 mm. The adaptive control strategy additionally proved effective in suppressing raft deformation while simultaneously optimizing pneumatic energy consumption. This research provides robust theoretical and technical foundations for intelligent vibration isolation systems in deep-sea equipment to accommodate extreme-depth-induced hull deformation and large-scale raft deformation. Full article

Aiming at the demands for low-carbon transformation in multi-energy-coupled industrial parks, a model is proposed that incorporates a carbon trading system incorporating incentives and penalties. This model includes joint combined heat and power (CHP) units, carbon capture technologies, and power-to-gas (P2G) conversion equipment. Firstly, we develop a modeling framework for the joint operation of cogeneration units to establish a comprehensive energy system within the industrial park that integrates electricity, heat, gas, and cold energy sources. Subsequently, we introduce a reward and punishment carbon trading mechanism into an industrial park to regulate carbon emissions effectively. With an optimization objective focused on minimizing the overall operating costs of the system while considering relevant constraints, we formulate an optimization model. The Gurobi solver is employed through the Yalmip toolkit to address this optimization problem. Finally, four operational scenarios are established to compare and validate the feasibility of our proposed optimization strategy. The results from our computational example demonstrate that integrating combined heat and power along with carbon capture and P2G technologies—coupled with a tiered reward and punishment carbon trading mechanism—can significantly enhance the energy consumption structure of the system. Under this model, the overall expenses are decreased by 12.36%, CO₂ emissions decrease by 33.37%, and renewable energy utilization increases by 36.7%. This approach has effectively improved both wind power consumption capacity and low-carbon economic benefits within the system while ensuring sustainable economic development in alignment with “dual carbon” goals. Full article

Regulatory changes related to the policy of reducing CO₂ emissions from natural gas are leading to an increase in the share of hydrogen in gas transmission and utilization systems. In this context, the impact of the change in composition on thermal radiation zones should be assessed for flaring during startups, scheduled shutdowns, maintenance, and emergency operations. Most existing models are calibrated for hydrocarbon flare gases. This study assesses how the CH₄–H₂ blends affect thermal radiation zones using a developed solver based on the Brzustowski–Sommer methodology with composition-dependent fraction of heat radiated (F) and range-dependent atmospheric transmissivity. Five blends, 0–50% (v/v) H₂, were analyzed for a 90 m stack at wind speeds of 3 and 5 m·s⁻¹. Comparisons were performed at constant molar (standard volumetric) throughput to isolate composition effects. Adding H2 contracted the radiation zones and reduced peak ground loads. Superposition analysis for a multi-flare layout indicated that replacing one 100% (v/v) CH₄ flare with a 10% (v/v) H₂ blend reduced peak ground radiation. Emission-factor analysis (energy basis) showed reductions of 3.24/3.45% at 10% (v/v) H₂ and 7.01/7.44% at 20% (v/v) H₂ (LHV/HHV); at 50% (v/v) H₂, the decrease reached 22.18/24.32%. Hydrogen blending provides coupled safety and emissions co-benefits, and the developed framework supports screening of flare designs and operating strategies as blends become more prevalent. Full article

To analyze and enhance the transient stability of a hydro-wind-PV VSC-HVDC transmission system, this paper establishes a transient stability analytical model and proposes strategies for stability improvement. Based on the dynamic interaction mechanisms of multiple types of power sources, an analytical model integrating GFM converters, GFL converters, and SGs is first developed. The EAC is employed to investigate how the factors such as current-limiting thresholds and fault locations influence transient stability. Subsequently, a parameter tuning method based on optimal phase angle calculation and delayed control of current-limiting modes is proposed. Theoretical analysis and PSCAD simulations demonstrate that various factors affect transient stability by influencing the PLL of converters and the electromagnetic power of synchronous machines. The energy transfer path during transient processes is related to fault locations, parameter settings of current-limiting modes in converters, and the operational states of equipment. The proposed strategy significantly improves the transient synchronization stability of multi-source coupled systems. The research findings reveal the transient stability mechanisms of hydro-wind-PV VSC-HVDC transmission systems, and the proposed stability enhancement method combines theoretical innovation with engineering practicality, providing valuable insights for the planning and design of such scenarios. Full article

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article

Magnetic components serve as critical energy conversion elements in power conversion systems, with their performance directly determining overall system efficiency and long-term operational reliability. The development of accurate core loss frameworks and multi-objective optimization strategies has emerged as a pivotal technical bottleneck in power electronics research. This study develops an integrated framework combining physics-informed modeling and multi-objective optimization. Key findings include the following: (1) a square-root temperature correction model (exponent = 0.5) derived via nonlinear least squares outperforms six alternatives for Steinmetz equation enhancement; (2) a hybrid Bi-LSTM-Bayes-ISE model achieves industry-leading predictive accuracy (R² = 96.22%) through Bayesian hyperparameter optimization; and (3) coupled with NSGA-II, the framework optimizes core loss minimization and magnetic energy transmission, yielding Pareto-optimal solutions. Eight decision-making strategies are compared to refine trade-offs, while a crow search algorithm (CSA) improves NSGA-II’s initial population diversity. UFM, as the optimal decision strategy, achieves minimal core loss (659,555 W/m³) and maximal energy transmission (41,201.9 T·Hz) under 90 °C, 489.7 kHz, and 0.0841 T conditions. Experimental results validate the approach’s superiority in balancing performance and multi-objective efficiency under thermal variations. Full article