Abstract:[Objective]With the increasing global attention to climate change and the implementation of the"dual carbon"goals,the resource utilization of CO2 has become a pivotal research focus worldwide.However,the inherent chemical stability of CO2 poses significant challenges for its chemical fixation and conversion under mild conditions,where catalyst design plays a decisive role.While the carboxylation of ethylene oxide(EO)with CO2 to synthesize ethylene carbonate(EC)is recognized as an effective approach for energy conservation and low-carbon development,substituting EO with bio-based ethylene glycol(EG)offers a safer,eco-friendly,and renewable alternative.To address the thermodynamic limitations and low conversion rates in the direct synthesis of EC from EG and CO2,this study aims to construct a synergistic catalytic system combining alkaline ionic liquids with Brønsted acids,thus developing a bifunctional catalyst for efficient CO2 activation and EG conversion under mild conditions.[Methods]Three alkaline ionic liquid catalysts,including[DBUH]PHY,[TBDH]PHY,and[DBUH]TBD,were synthesized using 1,8-diazabicyclo[5.4.0]undec-7-ene(DBU),1,5,7-triazabicyclo[4.4.0]dec-5-ene(TBD),and phenol as precursors.Their chemical structures and thermal stability were verified through Fourier transform infrared spectroscopy(FT-IR)and thermogravimetric analysis(TGA).The synergistic catalytic performance was evaluated in a high-pressure autoclave using various Brønsted acids(H2SO4,H3PO4,CH3COOH)under optimized conditions.[Results]When used individually,either the ionic liquids or Brønsted acids show low catalytic activity(less than 10.54％yield).However,the combined[DBUH]PHY and H2SO4 system achieves a remarkable EC selectivity of 97.80％and a yield of 20.89％,outperforming single-component systems.Density functional theory(DFT)calculations reveal that H2SO4 protonates EG to form carbocation intermediates,while the[DBUH]+cation activates CO2 via a strong binding energy(-61.94 kJ/mol),forming DBU-carboxylate(DBUH-CO2).The PHY-anion facilitates dehydrogenation to generate oxyanions,synergistically driving EC formation and catalyst regeneration.Compared to conventional CeO2-based catalysts(conversion rate is no more than 2％),this synergistic catalytic system demonstrates superior atomic efficiency under mild conditions(120 ℃,3.0 MPa).[Conclusion]This study constructed a synergistic ionic liquid/Brønsted acid catalytic system,offering a novel strategy for the green conversion of CO2 and diols.The developed bifunctional catalyst integrates CO2 activation and EG protonation capabilities,with the proposed mechanism validated by experimental and computational insights.This sustainable synthesis route aligns with green chemistry principles,providing a viable pathway to mitigate greenhouse effects and enhance resource utilization.The findings hold significant implications for advancing the green transformation of the chemical industry,supporting carbon peak and neutrality goals,and fostering the development of circular economy.

Abstract:[Objective]Traditional motor optimization design methods involve establishing analytical models for motor volume,loss,and cost,selecting optimization algorithms to refine them,and deriving optimal design variables.However,since motor models are complex,analytical models fail to precisely describe partial variables.Stator magnetic density is an important variable of slotless permanent magnet direct-current motors,whereas the accuracy of its analytical formula is low.The particle swarm algorithm is widely used in optimization design,but its optimization ability is poor.[Methods]To solve the above problems,an optimization design method of the slotless permanent magnet direct-current motors based on adaptive improved particle swarm algorithm was proposed.An analytical model of the slotless permanent magnet direct-current motors was established,and an objective function was constructed with motor volume,loss,and cost as optimization goals.The Sobol method was employed to identify high-sensitivity variables of the motors,thereby reducing the number of design variables.Subsequently,a magnetic circuit model was developed using finite element simulations,and magnetic density data were extracted after design variable parameters were adjusted.The response surface method was then applied to re-fit the magnetic density data,and a stator magnetic density response surface model was established to replace the original analytical formula.The particle swarm algorithm was improved.The updating modes of inertial weight and learning factor were selected through comparisons between fitness values of individual particles and the average fitness value of global particles during iteration,which enhanced algorithmic precision.Finally,both the original and improved algorithms were utilized to optimize the objective function.The optimal motor design parameters were achieved by comparison.[Results]Comparative analysis of stator magnetic density calculations between the analytical formula and the response surface model reveals that the latter exhibits significantly reduced computational errors.When the adaptive particle swarm algorithm,original particle swarm algorithm,and other classical algorithms were applied to optimize the objective function,the improved particle swarm algorithm achieves the most optimal results.[Conclusion]The experimental results demonstrate that replacing the analytical formula for stator magnetic density with a response surface model effectively mitigates the significant calculation errors associated with the analytical approach.Meanwhile,the particle swarm algorithm incorporating adaptive updates of inertia weight and learning factor exhibits an enhanced optimization capability.Comparative analysis with classical algorithms confirms its superior optimization capability.

Abstract:[Objective]With the continuous expansion of the power grid scale and the increasing complexity of its structure,traditional power grid modeling and visualization methods have gradually revealed many issues.For example,modeling accuracy often fails to meet the refined display requirements of complex grid structures.Application scenarios are limited and unable to effectively address diverse business needs.Moreover,there is a lack of scientific and effective verification mechanisms to ensure the accuracy and reliability of modeling results.To address these challenges,this study proposed a 3D power grid modeling and verification method integrating GIS-GIM and DETR networks,to achieve high-precision 3D grid modeling and establish an effective verification system,providing a solid data foundation and reliable decision support for grid planning,operation and maintenance,and management.[Methods]The first step involved integrating the grid information model(GIM)into the geographic information system(GIS).By leveraging GIS's powerful geospatial analysis and display capabilities,and combining GIM's detailed descriptions of grid equipment and topological structures,a more comprehensive 3D grid modeling approach was achieved,visually presenting the grid's overall layout and equipment distribution from a geospatial perspective.Second,the DETR network was improved by optimizing its structure,adjusting parameter settings,and employing more effective training strategies,enabling it to more accurately detect and classify 3D grid equipment.During training,a large volume of 3D grid equipment data was collected to build a rich and diverse dataset.The data were then annotated and preprocessed to improve the model's generalization ability.Last,the improved DETR network was applied to the 3D grid modeling process to detect and classify equipment in the modeling results individually,ensuring the accuracy of equipment information and the overall accuracy of the modeling results.[Results]To validate the effectiveness of the proposed method,experimental analyses were conducted on 100 sets of equipment data from three newly built substations.The results show that,compared to traditional modeling methods,the proposed 3D grid modeling method that integrates GIS-GIM and DETR networks significantly improves modeling accuracy,enabling more precise restoration of the spatial positions,structural forms of grid equipment,and connection relationships between equipment.Regarding the verification of the modeling results,the verification network demonstrates a good performance with an accuracy rate of 93.14％,indicating that the method can effectively detect potential errors and deviations in the modeling process and ensure the reliability of modeling results.[Conclusion]The proposed 3D power grid modeling and verification method,integrating GIS-GIM and DETR networks,performs excellently in improving grid modeling accuracy and establishing an effective verification mechanism,meeting the high-precision requirements of actual grid modeling.The method contributes significantly to improving the scientific basis for grid planning and provides intuitive 3D visualization for daily operations,maintenance,fault diagnosis,and repair,supporting reliable decision-making in grid management.It holds important theoretical significance and broad application prospects.

Abstract:[Objective]As a new kind of high-temperature materials,refractory high-entropy alloys have a wide application prospect because of their excellent high-temperature performance.However,their poor plasticity at room temperature has become the main factor limiting their development.Among many refractory high-entropy alloy components,TiZrTaNbMo has good biocompatibility and has attracted extensive research interest.Similarly,the alloy also has the disadvantage of poor plasticity at room temperature,which limits the development of the alloy.Ta element is the element with the highest melting point in the component.So far,the mechanisms underlying the influences of Ta element on the microstructure and mechanical properties of the alloy system have remained unclear.[Methods]The influences of the decrease in Ta content on the microstructure and properties of TiZrTaNbMo refractory high-entropy alloy were studied.In this study,the x value in TiZrTaxNbMo which reflected Ta molar ratio was 0.8,0.9,and 1.0,and the molar ratio of other elements remained unchanged.TiZrTarNbMo(x=0.8,0.9,1.0)series refractory high-entropy alloys were prepared by non-consumable high-vacuum arc furnace melting,and the alloy matrix was annealed at 1 000℃/6 h,which was followed by natural cooling with the furnace.The phase structures of the alloys were determined by an X-ray diffractometer(XRD).The microstructures and element distributions of the alloys were characterized by a scanning electron microscope(SEM)and energy dispersive spectrometer(EDS).The Vickers hardness of the alloys was measured by a microhardness tester.[Results]The TiZrTaxNbMo refractory high-entropy alloys are composed of the primary BCC1 phase and the secondary BCC2 phase,showing a typical dendritic structure.With the increase in Ta content,the interdendritic region becomes smaller.Ta,Nb,and Mo elements are enriched in the branches,while Ti and Zr elements are enriched in the interdendritic region.The decrease in Ta content reduces the segregation of Nb and Mo elements in the branches.In terms of mechanical properties,increasing Ta content increases the hardness of the alloys from 433 HV to 501 HV.The experimental results indicate that the change in Ta content does not cause the change in the crystal structures of the alloys,and they still have a BCC biphase structure.The decrease in Ta content leads to the enlargement of interdendritic region of metal dendritic structure.Reducing Ta content is helpful to reduce the segregation of elements,especially for Ti and Zr elements with lower melting points.[Conclusion]In this study,the original design of refractory high-entropy alloys with an equal molar ratio is changed,and the composition is optimized.The micro structure and mechanical properties of the alloys are improved by the adjustment of element content.The research results will help to promote the further application of the TiZrTaNbMo refractory high-entropy alloy system.

Abstract:[Objective]In the monitoring system of large substations,the target recognition of glass insulators is an important step to ensure the safe operation of power equipment.However,due to the complexity of the environment and the limitation of image acquisition conditions,glass insulator images often have problems such as insufficient clarity and similar color interference,which leads to the difficulty of target recognition and directly affects the safety monitoring effect of substations.[Methods]To solve this problem,a target recognition algorithm was proposed for glass insulators in large substations under similar color interference.The original image was converted from RGB space to HSV space to address insufficient image sharpness and similar color interference.By fine decomposition of hue H,saturation SS,and brightness V components in HSV space,the feature difference was calculated to enhance the color performance and visual effect of the image,so as to effectively eliminate similar color interference.An adaptive threshold segmentation technique,combined with the color features of HSV space,was used to accurately segment the image,and the glass insulator target region and complex background were separated.A dual-scale classification convolutional neural network(CNN)was designed to realize high-precision target recognition of glass insulators under complex background through multi-scale feature extraction and classification.The network combined local details and global context information to further improve the robustness and accuracy of recognition.[Results]The experimental results show that the proposed algorithm has significant advantages in application.In terms of color enhancement,the feature difference calculation in HSV space significantly improves the color contrast and visual effect of the image and effectively eliminates similar color interference.In terms of image segmentation performance,the adaptive threshold segmentation technique can accurately separate the glass insulator target region and the complex background,and the segmentation accuracy reaches a high level.In the aspect of target recognition,the dual-scale classification CNN shows strong anti-interference ability under complex background,and the recognition accuracy of glass insulators is significantly higher than that of traditional methods.[Conclusion]The target recognition algorithm proposed in this study for glass insulators in large substations under similar color interference successfully solves the target recognition problems including insufficient image sharpness and similar color interference through the organic combination of color enhancement,adaptive threshold segmentation,and dual-scale classification CNN.The algorithm has excellent performance in color enhancement,segmentation performance,and anti-interference ability and can recognize glass insulator targets efficiently and accurately,which provides a reliable technical guarantee for the safety monitoring of large substations.

Abstract:[Objective]With the gradual improvement of requirements for motor energy efficiency grade,outer-rotor low-speed permanent magnet motors are widely used in the industrial field,due to their advantages of high torque density,high efficiency,and energy saving.To meet the working conditions of heavy-load start-up and long-term low-speed heavy-load operation of industrial sector,the design of outer-rotor low-speed permanent magnet motors is developing in the direction of improving motor torque density.Accordingly,the issue of high heat generation caused by the high torque density of motors is becoming a focus of research.[Methods]To address the problem of high temperature rise in outer-rotor low-speed permanent magnet motors under heavy-load operation conditions,this paper established the physical model of outer-rotor low-speed permanent magnet motors and calculated the distribution of motor losses.First,based on the basic theory of computational fluid dynamics,according to the heat source distribution and structural characteristics of outer-rotor low-speed permanent magnet motors,the study designed and installed axial and circumferential Z-shaped water-cooled structures in stator bracket near the inner surface of the stator core.The simulation model with water inlet and outlet at the motor bottom was also established.The flow field and temperature field of two water-cooled structures were simulated and analyzed using Fluent software.The circumferential Z-shape structure was determined as a more suitable water-cooled design structure.Second,by the calculation method coupling fluid flow and heat transfer,the temperature field of the motor equipped with a circumferential Z-shaped 9-channel water-cooled structure was analyzed using Fluent software.Whether the water-cooled structure meeting the heat dissipation requirements of the outer-rotor low-speed permanent magnet motors was verified with the maximum temperature of the permanent magnet and insulation.Finally,based on the theoretical analysis,this paper determined the factors influencing heat dissipation in water-cooled structures,including water channel number,cooling water flow rate,and radial width of water channel section.The influences of different factors on motor temperature rise were studied using Fluent software.[Results]The results indicate that the flow rate distribution of the circumferential Z-shaped water-cooled structure is more uniform with a smaller inlet and outlet pressure difference,which is more suitable for outer-rotor low-speed permanent magnet motors.As the number of water channels,cooling water flow rate,and radial width of water channel section increase,the heat dissipation is enhanced.However,after each factor reaches a certain value,the motor temperature tends to stabilize.According to the analysis results,the final design includes 7 water channels with a radial width of 17 mm and a cooling water flow rate of 0.5 m/s.[Conclusion]The research results can provide a theoretical basis for the application of water-cooled systems of outer-rotor low-speed permanent magnet motors in high-load working environments.