Polymer materials are widely used in power electronics and power transmission and distribution systems because of their excellent dielectric properties. However, under the long-term coupling effect of mechanical stress, thermal effect, electrical stress and other factors inside the insulation material, it is easy to cause the growth of electrical trees, which will cause internal damage and deterioration of the material, and eventually lead to the harm caused by penetrating discharge. The numerical simulation method can provide reference for improving the insulation reliability of the system. However, some key parameters of the existing model are difficult to obtain directly from the experiment, and can only be realized through the model verification to achieve the microscopic electrical tree simulation of specific materials, and can not achieve the engineering tasks from material parameter testing to complex structure electrical tree prediction. Therefore, this paper aims to propose a method of electrical tree limb simulation with simple model and parameters that can be obtained by experiment, so as to improve the engineering applicability of electrical tree limb simulation.

The inverse power law is a phenomenological model directly based on the lifetime data of solid dielectric, which describes the physical process of the accumulation of electrical damage in solid dielectric to the generation of penetrating tree channels, and has the theoretical basis for describing the growth of electric treees. Therefore, this paper analyzes the physical relationship between the parameters of the inverse power model and the growth law of electrical trees, establishes the basic equation of local electrical damage based on the inverse power model, and establishes the electric tree simulation method based on the inverse power model combined with the electric field calculation and the material dispersion equation. Further, a sample of pin-plate electrode is used to demonstrate how to simulate the electrical tree by testing the basic parameters of the material. The experimental verification of the simulated results of electric treees is carried out, and the simulated growth law of electric treees is compared with the experimental growth law of electric treees. Finally, the difference between the proposed method and the phase-field simulation and WZ model is compared.

The final results show that the method proposed in this paper can effectively simulate the electrical trees by using the experimental material parameters. The simulated electrical trees in this paper agree with the experimental results in terms of morphology and growth law. In this method, the shape of electrical trees is correlated with voltage tolerance index and cumulative damage standard deviation, and the growth rate of electrical trees is correlated with voltage tolerance index and cumulative damage mean. Compared with the phase-field simulation and WZ model, the proposed method can simulate the gradual growth of electrical trees, and the model parameters can be obtained experimentally.

Gas insulated switchgear (GIS) faults occur frequently after live operation, and according to statistics, the faults after live operation account for 60% of the total. The strong shock vibration generated by GIS live operation not only produces metal contaminants, but also activates latent particles, which seriously affects the insulation safety of GIS. The physical effects generated by GIS live operation are complicated, mainly including shock vibration, overvoltage, residual voltage and other physical effects. The mechanism by which the particles inside the GIS are affected by switching operation is unknown and is extremely dangerous. The mechanism of switching operation on particles in GIS is unknown and extremely dangerous.

In order to solve the problem that particles are prone to discharge after live operation, and to clarify the motion mechanism of spherical metal particles in GIS after impact vibration, this paper carries out the following work: firstly, we study the propagation process of vibration inside the cavity, and then we carry out the forced correction on the basis of the flexible boundary three-dimensional cylindrical vibration model, which improves the equivalence with the actual working conditions. The propagation form and the "ripple" propagation behavior of shock vibration in GIS are revealed, and it is clarified that the vibration wave is divided into P-wave and S-wave during the propagation process, the P-wave propagates faster than the S-wave, and the S-wave plays a major role in the jumping of the particles. And then the energy transfer process of the cavity and particles after being vibrated is analyzed, and the excitation effect of the shock vibration on the system is analyzed in the form of energy as a link. The energy transformation of the system after the vibration work is clarified, the cavity-particle kinetic energy transfer conservation model is constructed, and the activation conditions of the particles are defined with the help of the jumping field strength, and the activation criterion of the particles subjected to vibration is proposed. On the basis of the aforementioned research, the whole process model of particle motion under typical vibration excitation of live operation is established by considering the particle charge motion characteristics and collision random characteristics, and verified by experiments. On the basis of the validation, the correction considering the microscopic force is carried out, and the error is analyzed from the microscopic point of view.

The model fully considers the influence of different particle sizes and materials on the particle dynamics, and is more equivalent to the actual working conditions than the method defined by the recovery coefficient. Finally, it is clarified that the vibration excitation can activate the motion of the particles, and the influencing factors of the motion behavior of the particles are explored. Combined with the propagation and attenuation characteristics of vibration, the criterion of critical activation distance of particles under charged working conditions is proposed, and the changes of critical activation distance of particles under different factors are calculated. The study shows that the critical activation distance increases logarithmically with the increase of voltage amplitude and vibration intensity. This study provides support for solving the problem that particles are very likely to cause discharge faults after GIS live operation.

The low temperature and high humidity environment in winter can easily cause wind turbine blades to freeze, seriously affecting the actual power output and safe operation of wind turbines. To avoid problems such as increased fatigue load and vibration of unit components caused by icing, wind farms need to implement shutdown strategies in a timely manner based on the icing situation of the blades. Therefore, accurate identification of blade icing status has become one of the key points in maintaining the safe operation of winter wind turbines. However, current ice diagnosis methods rely on a large amount of time series data for modeling and prediction. In practical work, due to equipment and working conditions, it is difficult to collect sufficient ice sample monitoring data, which leads to the widespread problem of data imbalance and has a continuous impact on the improvement of ice diagnosis accuracy. To solve this problem, this paper proposes a fusion diagnostic model based on conditional generative adversarial network (CTGAN) and light gradient boosting machine (LightGBM), aiming to achieve high-performance wind turbine blade ice diagnosis using a small number of training samples.

Firstly, based on the sliding window algorithm, new mixed features are further constructed on the basis of the original features. Secondly, the CTGAN model is used to learn the data distribution of real samples, and Nash equilibrium is achieved through adversarial training with generators and discriminators, generating new samples that are similar to real samples. Then, the synthesized samples are input into LightGBM to extract effective features and diagnose icing, and the LightGBM model is modified by introducing a focus loss function to improve its ability to distinguish confusing samples. Finally, the attribution theory based on shapley additive explanetions (SHAP) was used to analyze the factors affecting icing.

The simulation results on actual wind farm data show that the diagnostic accuracy of all algorithms has a certain improvement effect after using mixed features, and the average diagnostic accuracy of each model can reach 0.979. Due to the introduction of sample expansion algorithms, the accuracy of each model has improved to varying degrees compared to when data is lacking. When the sample imbalance rate is 30%, the accuracy of the traditional Logistic regression classification model is improved by 11.02%. At the same time, the accuracy of LightGBM (Focal Loss) is 0.982, which is close to the accuracy when the sample is sufficient. As the sample imbalance rate decreases and the actual number of ice-covered samples further decreases, the advantages of the sample expansion algorithm gradually become apparent. When the sample imbalance rate is 10%, compared to the unexpanded samples, the accuracy of Logistic regression model is improved by 13.55%. When the sample imbalance rate is 5% and the actual number of ice-covered samples is only 15, compared to the unexpanded samples, the accuracy of Logistic regression, KNN, XGBoost, and LightGBM models has improved by 35.85%, 4.52%, 9.32%, and 9.18%, respectively. This indicates that CTGAN has good sample generation ability and can effectively learn the distribution of real samples even when the sample data is small.

From the simulation analysis, the following conclusions can be drawn: (1) The mixed features constructed based on the sliding window algorithm in this paper can significantly improve the classification ability of each model. At the same time, the LightGBM model combined with mixed feature information has obvious advantages compared to other models. (2) The sample generation model CTGAN can effectively learn the distribution of real samples, and compared to other data augmentation methods, it can generate new samples that are more similar to real samples. (3) By using the Focal loss function to modify the LightGBM model, the model's ability to distinguish easily confused samples has been increased. In addition, based on the SHAP attribution theory, the importance of each icing factor was analyzed, and the quantitative impact of key features on the diagnostic results was quantified, improving the credibility of the model's diagnostic results.

ZnO-based functional ceramics are widely used in the fields of varistor, thermistor, and gas-sensing. However, the temperature required for the preparation of ZnO-based functional ceramic is still high (>1 000℃). As a result, the additives that lead to modulating the properties of ceramic materials are limited to inorganic fillers. Conventional sintering leads to excessive growth of ZnO grains, which makes it difficult to achieve the miniaturization requirements of ZnO-based functional ceramic devices. Cold sintering process (CSP) enables the densification of ceramic materials at temperatures of below 300°C, thus providing the possibility for grain boundary engineering using ceramic materials as the matrix with organic polymer fillers or organic/inorganic composite fillers.

In this paper, zinc oxide (ZnO)-polytetrafluoroethylene (PTFE)-based ceramic composites were prepared by CSP. Based on the above cold sintering conditions, high-density (>97%) ZnO-PTFE composite ceramics were prepared with ZnO as the matrix and polytetrafluoroethylene (PTFE) as the filler. The electrical properties of the ZnO-PTFE specimens showed better non-ohmic characteristics at the polymer content of 15%, the breakdown field and nonlinear coefficient of the composites can reach 933.68 V/mm and 5.74. The breakdown field is 6.92 times higher than the classical five-element formulation of ZnO varistor (135 V/mm), but the nonlinear coefficient is low. Microstructure observation and impedance performance testing showed that the PTFE phase limits the grain growth and increases the ceramic grain boundary impedance. PTFE at grain boundaries can induce the formation of varistor properties of ZnO-based composite ceramics, and improve the flexibility of ZnO ceramics.

Further, the effects of metal oxides and PTFE on the microstructures and electrical properties of ZnO-PTFE based composites were investigated. The results indicate that a high relative density of over 97% was achieved for ZnO-PTFE-based composites doped with PTFE or co-doped with PTFE and metal oxides (CoO, Mn₂O₃). It is found that the electrical properties of ZnO-PTFE-based composites were significantly enhanced with the co-doping of PTFE, CoO, and Mn₂O₃. Specifically, the breakdown field and nonlinear coefficient of the composites were improved to 3 555.56 V/mm and 13.55, respectively. The J-E results show that the electrical conduction of the ceramic composites were dominated by the thermionic field emission at grain boundary. Moreover, the elastic modulus of the ceramic composites decreases greatly with the addition of PTFE and then increases after doping metal oxides (CoO, Mn₂O₃).

This study demonstrates that CSP provides a new route to fabricate ceramic-polymer-based composites and modulate their properties.

The rapidly developing field of artificial intelligence (AI) has made significant advancements in areas such as image processing, language, decision-making, and diagnostics, providing new methods for solving complex problems. The increasing intelligence of electrical equipment, combined with the coupling of strong and weak electrical fields, has led to the emergence of multi-scale, multi-physical field coupling and nonlinear problems in electromagnetic fields. High-precision numerical modeling and optimization are increasingly challenging.

Therefore, this paper combines recent research outcomes from the author’s team to introduce deep learning methods for solving typical interdisciplinary problems, such as data-driven modeling, physics-driven PDE solving, and knowledge-embedding modeling. In particular, the paper discusses the current state of intelligent modeling for complex electromagnetic field problems driven by both data and knowledge. It also offers perspectives on the scientific challenges and important future directions in the research and engineering implementation of electromagnetic field intelligent modeling.

In the area of data-driven modeling, the paper explores its application in the performance analysis and optimization of electrical equipment. The discussion is divided into three parts: performance parameter calculation, electromagnetic thermal field prediction, and knowledge discovery modeling. By combining numerical simulation and experimental data, deep learning algorithms can mine potential knowledge from the data, enabling rapid computation of one-dimensional performance and two- and three-dimensional fields. This approach allows for real-time simulation of local performance, global performance, and micro characteristics.

Regarding physics-driven partial differential equation (PDE) solving, the paper discusses two main research directions: knowledge-embedding regularization methods and designing machine learning model structures based on physical meaning. Constructing loss functions or network structures that align with physical laws makesit possible to solve PDEs without relying on sample data. This method is beneficial when physical conditions are incomplete and sample data is scarce. Using AI to solve physical equations helps overcome traditional bottlenecks, improving computational efficiency and expanding application scope.

In the area of knowledge-embedding modeling, the paper discusses how to implicitly integrate domain knowledge, mainly through multi-fidelity models and neural network operator methods, to improve the precision and efficiency of computational models. By embedding the knowledge inherent in high-precision samples into the model, high-precision forward and inverse problem models can be built. As data accumulates, the model's accuracy and generalization ability will improve. This method fully utilizes the advantages of deep learning and integrates basic physical theories. As data grows, knowledge-embedding methods are expected to be crucial in solving more complex electromagnetic field problems and enhancing overall simulation outcomes.

In conclusion, the fusion of AI and knowledge has become a significant trend in the development of numerical simulation. Integrating data-driven, physics-driven, and knowledge-embedding methods has accelerated the advancement of electromagnetic field modeling and optimization. These methods have improved simulation accuracy and expanded the application range of numerical simulations for complex electromagnetic field problems. However, the exploration of AI in numerical simulation is still in its early stages, facing challenges such as insufficient model generalization, computational efficiency improvement, and physical constraint integration. Future research should focus on addressing these issues to promote the broader application and development of AI in the field of electromagnetic field numerical simulation.

The magnetic lift control rod drive mechanism (CRDM) is a critical electromagnetic actuator for regulating nuclear reaction rates. Its dynamic process is complex to predict due to the cross-coupling among current response, magnetic circuit saturation, and motion state. The latest equivalent magnetic network (EMN) model exhibits inaccuracies and relies on flux distribution during modeling, lacking generality. In multi-field coupling analysis, researchers often employ multi-software collaborative or semi-simulation methods, which incur significant time and hardware costs. This paper proposes a dynamic equivalent magnetic network (DEMN) model and a multi-physics field coupling calculation method considering transient current changes and saturation.

Firstly, the structure of the magnetic lift CRDM is introduced, and its lift solenoid valve is selected to analyze the electromagnetism-mechanics coupling during dynamic processes. Then, the mechanism is partitioned using orthogonal grid lines, and mesh units of multiple media are consolidated into a single medium to unify the reluctance calculation formula. During dynamic changes, only the grid size or position in the motion region is altered, thereby eliminating redundant modeling and reducing computational errors caused by mesh discrepancies. A connection relationship and calculation method for branch reluctance are established to address the misalignment between fixed and moving mesh units, facilitating continuous armature movement. A multi-physics field coupling calculation method is proposed by combining circuit models and kinematic formulas, which consider transient current changes and saturation. Finally, Compared with 3D finite element analysis (FEA) and experiments, the DEMN model and the proposed multi-field coupling calculation method are verified.

3D FEA results show that the magnetic density distribution, inductance, and electromagnetic force are highly consistent with the DEMN results, where the maximum error of inductance is 5.7%, and the maximum error of electromagnetic force is 3.4%. The experiments show that linear and saturated inductance variations are similar. The calculation accuracy of the release and suction currents under various loads exceeds 91%. Additionally, the dynamic results closely align with experimental results, with motion time calculation errors at 28 A and 40 A currents of 0.99% and 2.99%, respectively.

The conclusion is as follows. (1) By using orthogonal grid lines, the unified calculation of reluctance is achieved, accelerating the modeling speed. (2) By changing the grid size or position of local areas, the dynamic changes of the EMN model are achieved, avoiding the problem of repeated modeling in the multi-field coupling calculation process and reducing the calculation errors caused by differences in mesh partitioning. (3) Compared with FEA, the proposed DEMN model requires less computational resources and shorter computation time while ensuring accuracy. Compared with the experimental results, the effectiveness and accuracy of the DEMN model and multi-field coupling calculation method are verified. It can be extended to EMN modeling, performance analysis, and rapid optimization design of the entire CRDM.

The electromagnetic rail launch process exists in high current, ultra-high speed, high temperature-rise, strong friction, and extreme impact conditions. The high heat generated causes the surface of the aluminum armature to melt, resulting in a transition at the pivot-rail interface from solid-solid electrical contact to a solid-liquid-solid melt process. Eventually, molten aluminum solidifies on the rail surface, forming a complex deposition layer. This deposition layer has implications for the performance of the pivot rail system during subsequent launches. The operational environment characterized by ultra-high-speed friction during repeated launches results in a low melting point in the armature. A portion of molten material forms a liquid transferred onto the rail, enhancing the interface and diminishing the electromagnetic rail's longevity. Consequently, it is imperative to investigate the impact of the aluminum deposition layer on the sliding electrical contact at the pivot-rail interface.

This study conducted small-diameter electromagnetic launching tests with varying launching times to examine the carrier friction wear behavior of the friction sub-material of the pivot rail. The results revealed that a significant amount of molten aluminum was transferred to the rail surface after multiple launches, increasing the roughness of the pivot-rail interface due to the residual deposit layer. As a result, the pivot-rail friction sub-contact deteriorated, characterized by organizational features such as gouges and cracks on the rail surface. The wear intensity escalated with an increase in the number of launches. However, after a certain number of launches, the aluminum alloy oxide layer on the rail surface reached a critical thickness, reducing the wear on the rail body. Nonetheless, mechanical and electrical wear simultaneously intensified the environmental conditions at the pivot-rail contact surface.

Finally, a liquid film fusion deposition model at the pivot-rail interface was developed, and the deposited layer’s impacts on the operational dynamics of the liquid film and the electrical contact condition of the pivot-rail interface were studied. The study involved the calculation of the thickness of the deposited layer and the deposition efficiency for varying launch times. During high-speed launches, the aluminum liquid layer experienced significant viscous forces, and pronounced velocity variations of the liquefied layer at the armature tail exit increased viscous dissipation forces. With multiple launches, heightened interfacial friction can counteract the viscous forces within the aluminum liquid layer, destabilizing the interfacial liquid film. Thickening the aluminum deposition layer on the rail surface can exert extrusion effects on the liquid film, introducing destabilizing factors to the flow of the liquefied layer. Consequently, the aluminum liquid layer, which serves as a lubricant between the armature and the rail, may be extruded from the interface. Therefore, the armature’s normal operation is compromised, and the rail's longevity is diminished.

This paper analyzes the electromagnetic energy flow and coupling characteristics in wireless power transmission (WPT) systems. Addressing the complexities in accurately depicting the electromagnetic energy transformation within WPT systems, the research overcomes challenges associated with the intricate mathematical methods and the absence of models capable of characterizing spatial energy flow distribution.

A model for electromagnetic energy flow in WPT under sinusoidal excitation is established based on Poynting’s theorem. A reduced-order mathematical model is developed by analyzing common electromagnetic energy flow characteristics of basic electrical components. This model qualitatively explores the energy coupling in the transmission space. The coexistence of inductive and capacitive coupling in WPT systems is discussed, emphasizing the significance of enhancing near-field electromagnetic coupling for overall system performance. The working mode of WPT in the electromagnetic near-field region is presented from the perspective of the energy flow mechanism. An experimental platform is constructed to verify the inductive and the capacitive coupling methods.

The electromagnetic energy flow characteristics of conductors, transformers, and capacitors are analyzed, facilitating power transmission through the electromagnetic fields in their vicinity rather than by themselves. The electromagnetic energy flow characteristics of WPT systems are discussed as a combination of multiple RLC circuits. It is found that electromagnetic power is partly stored in the electromagnetic fields of individual circuits (self-energy) and partly in the fields between circuits (mutual energy). The analysis is simplified by considering a system with two RLC series circuits, revealing that WPT relies on mutual coupling between the primary and secondary sides for contactless power transfer.

The paper also discusses typical magnetic resonant WPT systems using the image method to analyze the field strength distribution in the coupling space. The symmetry between inductive and capacitive coupling characteristics is revealed, offering guidance for practical engineering design. Experiments are conducted to verify the coexistence of inductive and capacitive couplings and to analyze their frequency characteristics. Results indicate that as the operating frequency increases, the ratio of capacitive coupling to inductive coupling increases, which is significant for understanding WPT technology.

In conclusion, the paper analyzes the electromagnetic energy flow and coupling characteristics in WPT systems, offering valuable insights for the theoretical research and practical application of WPT technology. It highlights the importance of understanding the coexistence of inductive and capacitive couplings and their frequency characteristics to enhance the performance of WPT systems.

As a non-contact power supply method, wireless power transfer (WPT) technology is widely used in medical, automotive, and cellular devices because of its reliability, safety, and high degree of freedom. However, the parameter drift phenomenon of the coupler inevitably occurs in practical applications, which leads to the fluctuation of self-inductance and makes the WPT system suffer from frequency detuning. Thus, its transmission characteristics and stability are affected. Traditional bilateral frequency tuning methods are non-uniform because of the type of topology, and some require communication equipment and complex optimization of control parameters. This paper proposes a unified decoupling control strategy of frequency tuning for high-order compensated WPT systems.

Four T-type higher-order compensation networks of LCC/LCC, LCC/S, CLC/CLC, and CLC/S are analyzed as examples. Based on the impedance model, the bilateral resonance characteristics of the primary-side LCC-compensated WPT system and the primary-side CLC-compensated WPT system are deduced. If the primary side is in a resonant state, the RMS value of the input current will reach the minimum. If the secondary side is in a resonant state, the RMS value of the current will reach the maximum. Finally, the generalized criterion is obtained for bilateral tuning decoupling control of higher-order compensated WPT systems.

This paper proposes a control strategy to realize the bilateral tuning decoupling control without communication or parameter identification. Instead of the inherent compensation capacitance, the switched capacitor converter (SCC) structure is used, and the equivalent capacitance of the SCC is varied by changing the conduction angle of the control signal. Based on the generalized tuning criterion, the conduction angle of the SCC control signal is changed with the help of the double-step perturbation observation method, and the primary and secondary currents are searched until the minimum value of the primary input current and the maximum value of the secondary coil current. Therefore, the WPT system reaches the resonant state while the conduction angle is optimal. The primary and secondary resonance parameters are realized independently and adaptively, and the bilateral tuning and decoupling control is achieved.

Finally, an experimental prototype of a 180 W LCC/LCC WPT system is built. The experimental results are consistent with the theoretical analysis, verifying the effectiveness of the proposed generalized tuning decoupling control strategy. The results show that the method can effectively suppress the frequency detuning problem caused by the parameter drift of the coupler and the self-inductance fluctuation, improving the transmission efficiency and stability of the WPT system. In addition, the proposed method is applicable to the high-order WPT system with a π-type compensation network.

In recent years, the rapid development of renewable energy has posed a significant challenge to the breaking capacity of DC circuit breakers in power systems. Gas-blowing arc extinguishing technology based on gassing materials can greatly enhance the breaking capacity of DC circuit breakers. However, the macroscopic and microscopic pyrolysis mechanisms of gassing materials are unclear.

Firstly, the micro-pyrolysis mechanism of typical gassing material polyamide 66 (PA66) at different pyrolysis temperatures and rates was analyzed based on the reactive force field (ReaxFF). The decomposition process of PA66 and the types and quantities of small molecule gases produced were discussed. It was found that the initial bond breaking of PA66 occurred in the C—C bond adjacent to the amide group. H₂ and H₂O were the main pyrolysis gases of PA66, and their production process was analyzed. The reaction rate of carbon-free small molecule gas at high temperatures accelerates, and the amount increases. The product amount with carbon atoms below four increases rapidly and decreases slightly after reaching a peak. The main reasons are the Diels-Alder reaction, C3/C4 reaction, and cyclization reaction in the unsaturated hydrocarbons in the product, which leads to the decrease of hydrocarbon molecules. The temperature increase aggravates the disintegration of the PA66 molecular chain and the formation of small molecular gas. The heating rate of the system affects the distribution of heat in the reaction system, thus affecting the formation of the product. The slower the heating rate of the system, the more conducive to the uniform distribution of heat in the reaction system. Additionally, the amount of carbon deposition during pyrolysis at 2 600 K was analyzed. Light tar was dominant, followed by heavy tar, with the least amount of coke.

Subsequently, pyrolysis experiments at four different heating rates were carried out. Based on the Flynn-Wall-Ozawa isoconversional model, the average activation energy of PA66 was 194.85 kJ/mol, which was very close to the activation energy of 195.015 kJ/mol obtained by molecular dynamics simulation. Additionally, the pyrolysis gas distribution of PA66 was analyzed by pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) experiments, which verified the accuracy and reliability of the pyrolysis kinetics calculation method. PA66 is suitable for the first-order reaction kinetic model, and the simulation data have high accuracy and reliability for the thermal decomposition reaction path and gas type of PA66 at the microscale.

Finally, simulation calculations and arcing experiments of three gas-producing materials, PA6, PA46, and PA66, were carried out. The gas generation rate and quantity changes during pyrolysis were observed, and the transient pressure changes during the arc-breaking experiment were analyzed. The order of transient pressure generated during the arcing process is PA6>PA46>PA66, consistent with the trend of the number of product gas molecules obtained by simulation calculation. The ReaxFF simulation results are confirmed and supplemented with the arc-breaking experiment, further verifying the reliability and accuracy of the research.

This paper offers a theoretical framework for understanding the macroscopic pyrolysis behavior and the microscopic pyrolysis mechanism of gassing materials. It contributes to a deep comprehension of material behavior under high-temperature and arc conditions, laying a methodological foundation for evaluating the performance of gassing materials in DC circuit breakers.