Field emission coupled with molecular dynamics simulation (FEcMD) software package is a computational tool for studying atomic structure evolution, structural deformation, phase transitions, recrystallization as well as electron emission characteristics of micro- and nano-protrusions and nanowires consisting of elemental metals or multi-component alloys by means of multi-physics and multi-scale methodology. Current implementations of molecular dynamics simulation coupled with multi-scale electrodynamics (ED) and heat conduction (HC) in FEcMD program are advanced mainly in the two aspects as follows. In electrodynamics, the FEcMD program incorporates the space charge interactions (space charge potential and exchange-correlation effects) in the self-consistent solved Poisson-Schrödinger equation with Wentzel-Kramers-Brillouin-Jeffreys (WKBJ) approximation to evaluate the field emission current density and the related resistive heating process more reliably for nanowires or nano-protrusions especially for nano-gaps between two metal electrodes. Meanwhile, the two-temperature heat conduction model is implemented in electrodynamics coupled with molecular dynamics simulations (ED-MD), providing more dedicated descriptions for the hierarchical electron-phonon two-channel heat conduction mechanism and the temperature evolutions of electron and phonon subsystems under the radiofrequency (RF) or pulse electric fields. Benchmark tests are performed for some key implementations in FEcMD software to validate the numerical results, and also to demonstrate the use of program to study the atomic structure evolution of metal nano-structures under electric field and heating processes.
This paper presents a numerical approach to the stochastic obstacle problem using the stochastic Galerkin (SG) method. Due to the low regularity of the solution, linear finite elements are employed in both the physical and random variable spaces. Properties of random fields and variational inequalities of the first kind are employed to establish the well-posedness of the problem. Finite element spaces are introduced to construct suitable approximation subspaces, and a comprehensive SG formulation is proposed to solve the stochastic obstacle problem. Well-posedness of the discrete formulation is shown and an optimal error estimate for the numerical solution in the H1-norm is derived. Numerical experiments validate the effectiveness of the SG method, showing that the expectation error converges at a rate of O(h) in the H1-norm, consistent with theoretical predictions.
In human-centric manufacturing, collaborative robots (cobots) are increasingly expected to continually acquire knowledge as products and workflows evolve, and learning from human demonstration videos has emerged as a flexible means to this end. Realizing this paradigm requires perception that can be deployed with minimal hardware overhead and that generates executable task knowledge for robotic manipulation. Existing video-based methods rarely satisfy both requirements. Geometric pipelines rely on CAD models or multi-camera rigs, hindering scalable deployment across diverse workpieces, while pre-trained vision-language models require demonstration corpora rarely available in high-mix, low-volume manufacturing. In addition, existing methods recover only low-level geometry or high-level semantic labels in isolation, leaving a gap between geometric perception and executable task knowledge. To address these limitations, we propose Video2Knowledge, a video-based learning framework that takes a single monocular RGB video as input and distills it into temporally consistent task knowledge, covering action sequences, object state transitions, and spatial relations, without requiring additional sensors, manual CAD models, or task-specific retraining. The framework couples a physics-aware 6D object pose corrector with hand-object interaction analysis, and consolidates the results into a structured knowledge representation that maps directly to robot action sequences. Experimental evaluation on the HA-ViD and Assembly101 datasets demonstrates that the physics-aware correction yields improved pose coherence, while the extracted task knowledge faithfully recovers demonstrated action semantics and relative geometric configurations. A real-world case study further validates the transfer of task knowledge from a monocular demonstration video to physical cobot execution, confirming the practical deployability in manufacturing scenarios.
Dry-type transformers are widely used in power distribution systems, and accurate prediction of winding temperature distribution is essential for ensuring insulation reliability and thermal design optimization. However, conventional finite element methods (FEM) are often computationally expensive, while existing equivalent thermal network (ETN) approaches usually rely on simplified thermal resistance assumptions and provide limited spatial temperature information. In this paper, a temperature-dependent nonlinear equivalent thermal network (ETN) model is proposed for the thermal analysis of the winding section of a 100 kVA aluminum-wound ceramic-insulated dry-type transformer. According to the actual winding cross-sectional structure, conductive, convective, and radiative heat transfer paths are represented by equivalent thermal resistances, and a ninth-order nonlinear thermal network is established through nodal analysis considering temperature-dependent thermal parameters. The nonlinear equation system is solved in MATLAB using the Newton–Raphson iterative method. To improve modeling credibility, a two-dimensional finite element thermal model is constructed and cross-validated with the ETN results. In addition, no-load and short-circuit temperature-rise experiments are conducted to evaluate the proposed method under laboratory temperature-rise test conditions. Experimental temperatures are obtained through resistance-based winding temperature calculation and infrared thermal measurements. The results demonstrate that the proposed ETN model can accurately reproduce the winding temperature distribution while significantly reducing computational complexity compared with FEM. The maximum temperature deviation between ETN and FEM at representative nodes remains within 2 °C. The proposed method provides an efficient and physically interpretable tool for thermal analysis and preliminary thermal design of dry-type transformers.
Against the backdrop of deepening cross-regional electricity trading, accurate modeling of transmission losses and ensuring their fair allocation is crucial for improving market clearing efficiency and the accuracy of economic signals. Addressing the issues of prevalent linear loss approximations and the potential unfairness in loss allocation caused by opposing power flows on tie-lines in existing transaction-path-based centralized bidding and clearing models, this paper proposes a novel clearing model. This model incorporates quadratic losses on tie-lines and a well-defined loss allocation mechanism. The tie-line loss is modeled as a quadratic function of the power flow to more accurately reflect physical characteristics. Integer variables are introduced to indicate the power flow direction, and the tie-line loss is allocated fairly in proportion to the contribution of each transaction path. Since the original formulation constitutes a mixed-integer nonlinear programming (MINLP) problem, the piecewise McCormick convex relaxation technique is employed to linearize the quadratic and bilinear constraints, thereby transforming the problem into a tractable mixed-integer linear programming (MILP) problem. Case studies based on data from the inter-provincial trading platform of the Northwest Branch of State Grid Corporation of China validate the effectiveness of the proposed model.
