NetWork
Establishing an FMEA–HACCP-based access management system for university laboratory projects
SUN Dun;WU Weilin;LI Xiangguo;WANG Xiuhua;PIAO Jin;[Objective] University laboratories serve as core venues for scientific research， teaching， and talent cultivation. Their safety management directly impacts research continuity， teaching stability， and personnel safety. However， laboratory accidents frequently occur in Chinese universities， primarily during the use and storage of hazardous chemicals， as well as during equipment operation. The root causes of these accidents lie in fragmented risk identification， lagging control measures， formalistic access mechanisms， and the inherent limitations of a “post-incident remediation” model. This study establishes a scientific and operational access management system for experimental projects， targeting university research initiatives. The core objective is to “prevent experimental risks at their source and ensure the safe execution of projects.” This transforms laboratory safety management from a reactive response to proactive prevention， thereby guaranteeing secure experimental implementation. [Methods] First， by reverse engineering the root causes of university laboratory incidents and integrating core requirements from the University Laboratory Safety Inspection Checklist (2025 Edition)， we established five-dimensional prerequisites for experimental project access: personnel， equipment， materials， methodology， and environment. Building upon this foundation， we innovatively fused failure mode and effects analysis (FMEA) with hazard analysis and critical control point (HACCP) theory (fusion termed FMEA–HACCP) to construct a “risk identification–quantitative assessment–critical control point (CCP) determination–access verification” coordinated system， where the three-dimensional quantitative model of FMEA (risk priority number (RPN) = severity × occurrence × detectability) quantifies risk grading， whereas HACCP theory identifies CCPs， establishing control plans with key thresholds， monitoring protocols， and dynamic corrective actions to create a closed-loop management mechanism. [Results] After two years of practical implementation in the Agricultural Experiment Teaching Center， this access management system demonstrated significant outcomes: among 62 initial risk points， the high-risk points (RPN ≥ 301) decreased from 18 to 0; the average RPN across the entire process dropped from 286 to 123， representing a 57.0% reduction; three newly identified potential risk points， including “mixed storage of experimental waste liquids” and “operation of new instruments，” were controlled at low-risk levels (RPN ≤ 85) through early intervention， with no safety incidents occurring throughout the process. The compliance rate for CCPs rose from 68.0% to 98.5%， with hazardous chemical accounting， instrument calibration， and firefighting equipment achieving 100% compliance. Minor deviation frequencies decreased from an average of 5 incidents per month to 0.3 incidents， while the corrective response time shortened from 30 to 8 min. At the personnel level， undergraduate safety exam pass rates rose from 75.0% to 98.2%， graduate operational assessment pass rates reached 97.8%， and noncompliant operation incidents decreased by 91.7%. The management model successfully transitioned from “post-incident rectification” to “pre-emptive prevention–process control–post-verification，” thereby reducing the hazard rectification cycle from 72 to 24 h. [Conclusions] Overall， this study successfully establishes an FMEA–HACCP-based access management system for university laboratory projects by leveraging the comprehensive quantitative assessment of FMEA with precise control from HACCP. Centered on the five-dimensional prerequisites: “personnel， equipment， materials， methods， and environment，” this system enables comprehensive systemic risk prevention and control. Its operability， traceability， and scalability are fully validated through practice. The system effectively addresses the shortcomings of traditional management models， significantly reducing experimental risks while enhancing the safety literacy of relevant personnel. Ultimately， it provides a scientific paradigm for university laboratory safety management that can be extended to various laboratory types.
AI for empowering the construction and development of university laboratories in the new era
REN Guanghui;[Objective] As a “source of innovation” and “primary base” for cultivating outstanding talents， university laboratories are directly linked to the quality of higher education and the enhancement of national innovation capacity. With the rapid advancement of artificial intelligence (AI) technology， the intelligent upgrading of university laboratories has become a significant concern. Traditional laboratory models are hampered by multiple bottlenecks: inefficient allocation of experimental resources， with high idle rates of large-scale instruments and equipment and a lack of interdisciplinary sharing mechanisms; outdated laboratory management modes characterized by high manual operation and maintenance costs and weak early warning systems for safety hazards; limited experimental teaching functions that rely on fixed processes， making it difficult to cultivate innovative talents; and limited scientific research collaboration capabilities， including a lack of intelligent platforms that facilitate multi-team and cross-regional cooperation. However， the rapid development of AI technology has provided new possibilities for addressing these challenges. This study aims to systematically explore the core logic， practical paths， and value effects of using AI to update university laboratories and construct a theoretical framework for the deep integration of AI and university laboratory development. It also identifies potential application scenarios for AI technologies in laboratory resource management， experimental teaching reforms， and scientific research. The study proposes laboratory construction standards and development strategies that integrate AI， providing theoretical support and practical references for universities to transform their laboratories toward “intelligence， openness， and collaboration.” [Methods] To effectively address the challenges facing today’s university laboratories， including talent cultivation， research demands， and management efficiency bottlenecks， this study combines bibliometric analysis， theoretical construction， and case-based empirical research. [Results] The results demonstrate that AI’s three core features of perceptual， cognitive， and decision-making intelligence can be effectively applied in key scenarios such as procuring experimental equipment， constructing smart experimental platforms， and managing laboratories. For example， AI technology could optimize hardware management in laboratories and enable the creation of virtual–reality integrated experimental environments through knowledge graphs and digital twins. Additionally， it could promote scientific research collaboration platforms that can transcend disciplinary boundaries， improve research efficiency， and gradually form a positive cycle of “technology optimizing management—management feeding back into teaching—teaching and research collaborating.” The results also clarify the major challenges in upgrading laboratories， including high technical integration barriers， substantial costs for system upgrades， and shortages of interdisciplinary professional talents. To this end， the study proposes building a collaborative promotion mechanism of “demand-driven—technology adaptation—institutional guarantee，” strengthening talent cultivation， and improving ethical norms to advance laboratories toward higher goals of being “intelligent， open， green， and sustainable.” [Conclusions] This study systematically addresses the critical question of “how AI empowers the construction and development of university laboratories.” The results indicate that AI can effectively support the transformation and upgrading of laboratory operation models from “human-driven” to “intelligence-driven.” This conclusion aligns with the strategic orientation of digital transformation in higher education and provides a Chinese solution for global intelligent laboratory construction. It also holds significant importance for promoting the transition of university laboratories from supporters to leaders.
Development of an experimental system for superresolution structured illumination microscopy
LI Meiqi;ZHANG Haojia;[Objective] Superresolution microscopy is a key advancement in optical imaging， allowing researchers to visualize biological structures at the nanometer scale. However， integrating it into practical curricula is challenging due to its high cost， operational complexity， and limited flexibility of commercial systems. This work aims to develop a multimodal superresolution fluorescence microscopy platform that is accessible， reconfigurable， and suitable for education and research， addressing the critical need for hands-on training in advanced imaging techniques within undergraduate and graduate programs. [Methods] To balance system integration and modularity， we employed an optical cage system with structural supports featuring through-holes at different heights， enabling a multiaxis three-dimensional (3D) optical design. This design uses standardized cage-compatible optical components with quick-release interfaces to allow rapid switching among various imaging modalities. The system supports four imaging modes: widefield microscopy (WFM)， total internal reflection fluorescence microscopy (TIRFM)， two-dimensional structured illumination microscopy (2D-SIM)， and 3D-SIM. Each mode can be configured by adjusting the illumination path without disassembling the main structure. The platform includes a laser source， a high numerical aperture objective lens， a precision motorized stage， a sensitive complementary metal-oxide-semiconductor camera， and many basic optomechanical components. All control and image reconstruction workflows are implemented in open-source software， allowing customization and algorithm development. Students can perform experiments ranging from fundamental operations (WFM and TIRFM) to advanced functional challenges (2D-SIM and 3D-SIM) within a single system. Performance validation was carried out using various biological samples， including subcellular structures such as actin filaments and fluorescent beads. [Results] Students successfully performed multimodal imaging of subcellular structures， with the system maintaining stability over repeated reconfigurations. The total cost remained below 100，000 RMB， representing an order-of-magnitude reduction compared to commercial alternatives. The superresolution capability was validated through imaging fluorescent bead samples， where adjacent beads that appeared as a single diffraction-limited spot under conventional widefield microscopy were clearly distinguished using SIM. This resolution enhancement directly demonstrates the system’s ability to surpass the diffraction limit. Additionally， the system succeeded in resolving two adjacent actin filaments within a distance less than the optical resolution limit of conventional microscopy. The system also supports potential upgrades of key components for research applications; for instance， when equipped with higher-performance cameras and objectives， the platform can be used effectively for research in cell biology， materials science， and other fields. [Conclusions] We developed a flexible， low-cost， multimodal fluorescence microscopy platform that effectively bridges the gap between theoretical education and practical application in advanced imaging. Its modular design enables seamless switching between imaging modes， providing students with comprehensive training in optical principles and instrumentation while maintaining research capabilities. This integrated approach not only increases access to superresolution techniques but also fosters innovation through hardware and software extensibility. The platform makes incorporating superresolution microscopy into undergraduate curricula easier， with standardized equipment ensuring instructional consistency and better guidance. It also encourages sharing teaching outcomes and provides a solid foundation for students as they transition into scientific research， effectively combining educational development with research preparation in the field of optical microscopy.
Development and application of an intelligent experimental device with series-parallel double towers for absorption and desorption
YING Huijuan;LI Yang;JI Dengxiang;YU Yunliang;YANG Hui;GAO Ling;[Objective] As a core device for teaching experimental chemical engineering principles， absorption and desorption towers are irreplaceable for helping students understand mass transfer theories and master engineering operations. This study addresses the main problems facing traditional experimental absorption and desorption devices， including potential safety hazards caused by the use of toxic gas mixtures such as acetone and ammonia， single-experiment application owing to the single-tower design， low academic value that lags behind the needs of modern industry， and cognitive obstacles resulting from the non-transparency of stainless steel tower bodies. This research aims to develop a new type of multi-functional， intelligent experimental device that supports the training of future engineers capable of addressing complex engineering challenges within the context of emerging engineering education and to provide innovative methods for teaching experimental chemical engineering principles. [Methods] A university-enterprise joint research and development (R&D) model was used to construct a device structure with stainless steel as the frame and transparent organic glass as the tower body. The core design steps include selecting the carbon dioxide–air mixture as the non-toxic and environmentally friendly system to be absorbed， which conforms to green chemical engineering and the “carbon peaking and carbon neutrality” strategy; designing a water circulation system to realize the recycling of water resources and reduce experimental consumption; innovatively building two same-size tower bodies filled with Raschig rings and Pall rings， respectively， which can realize flexible switching between series and parallel connections through valve control; integrating the system with Internet of Things (IoT) and PID intelligent control technology， and matching it with equipment such as infrared detectors and electromagnetic flowmeters to realize part-process touch operation， real-time data display， and remote operation. [Results] The device achieved breakthroughs in multiple dimensions: the transparent tower body resolves the non-transparency problem of traditional devices， enabling visualization of the internal structure and allowing students to observe the gas–liquid flow state; the series-parallel structure facilitates multi-scenario tasks such as parallel measurement of the packing performance and series mass transfer experiments， enriching the teaching content and improving the experimental efficiency; the non-toxic system and intelligent control eliminate potential safety hazards， conform to the characteristics of modern industrial technology， facilitate digital empowerment in experimental teaching， and provide possibilities for cross-regional teaching. This device has been operating stably at Zhejiang University of Technology for three years， with remarkable teaching effectiveness and recognition from certain universities and peers， and has been successfully promoted to six universities. This year， it also became the designated experimental operation device for the National Final and Northwest Division of the 8th National College Students’ Chemical Engineering Experiment Competition. [Conclusions] The intelligent experimental device with series-parallel double towers for absorption and desorption effectively overcomes the limitations of traditional devices. Through visual presentation， multi-process design， safety upgrade， and intelligent control， it helps students deepen the cognitive connection between mass transfer theories and engineering applications， expands the breadth and depth of experimental teaching， and effectively cultivates students’ comprehensive experimental design and data analysis ability， innovative engineering thinking， and ability to solve complex engineering problems. This device provides effective support for reforming the experimental teaching of chemical engineering principles against the background of emerging engineering education and provides a reference for optimizing and upgrading similar teaching equipment.
Process selection and life cycle assessment of waste gas treatment for university laboratories
ZHANG Junjun;ZHONG Ya;[Objective] Volatile organic compounds (VOCs) are key precursors of particulate matter 2.5 and ozone. Although industrial emissions have significantly decreased under the ongoing Blue Sky Protection Campaign， exhaust gases from university laboratories near residential areas have become a critical concern for environmental quality and public health management. University labs typically use many volatile organic and inorganic reagents and feature numerous exhaust-collection points. This results in characteristics such as high total emissions， complex chemical composition， large air volumes， and low concentrations of laboratory exhaust gases. Currently， research on the effectiveness of treatment methods for university laboratory exhaust gases and the assessment of their full life cycle environmental impacts is lacking， limiting evidence-based guidance for selecting appropriate treatment strategies. [Methods] This study focuses on university laboratory exhaust gases and their treatment processes， evaluating treatment efficiency through pilot-scale and bench-scale tests. For bench-scale tests， xylene with varying humidity levels was used as the simulated exhaust gas， while for pilot-scale tests， a mixture of xylene， ethanol， and hydrochloric acid heated in a water bath inside a fume hood served as the simulated exhaust. Three combined treatment processes—“alkali washing + activated carbon adsorption，” “activated carbon adsorption + alkali washing，” and “SDG (acidic exhaust adsorbent) adsorption + activated carbon adsorption”—were examined to thoroughly assess resource and energy consumption and environmental impacts throughout their entire life cycle. [Results] Under dry conditions with an inlet xylene concentration of 400 mg/m3， the saturated adsorption capacity of activated carbon for xylene was 226 mg/g. At 50% relative humidity (RH)， capacity decreased to 114 mg/g， and at 90% RH， it dropped further to 89 mg/g. The “SDG adsorption + activated carbon adsorption” system showed the highest removal efficiency for mixed VOCs (xylene and ethanol)， reaching 83%， along with 91% efficiency for hydrochloric acid mist. Although the “activated carbon adsorption + alkali washing” setup performed slightly lower， both systems significantly outperformed the “alkali wash + activated carbon adsorption” process in VOC removal. This difference is largely due to the high humidity (～100% RH) introduced by front-stage alkali washing， which promotes competitive water vapor adsorption and reduces activated carbon effectiveness. Life cycle assessment indicated that the “SDG adsorption + activated carbon adsorption” method has the lowest overall environmental impact. Additionally， performing alkali washing after adsorption resulted in better environmental outcomes regarding global warming potential and photochemical ozone creation potential compared to front-stage alkali washing. [Conclusions] Environmental impact analysis showed that moving alkaline washing to after the adsorption stage， which increases exhaust humidity， reduced global warming potential by 1.4% and photochemical ozone creation potential by 41.2%. Moreover， replacing wet alkaline washing with dry acidic exhaust adsorbent decreased global warming potential， photochemical ozone creation potential， acidification potential， and human health hazards by 4.5%， 41.3%， 9.9%， and 2.2%， respectively.
Virtual Simulation Experiment of Fretting Wear on the Inner Ring Fitting Surface of a Vibration Screen Exciter Bearing
CHENG Xiaohan;WANG Meng;LIU Yongjia;CHEN Zhijie;CHANG Chenrui;WU Yusheng;ZHANG Zeyu;[Objective] The exciter of vibrating screens used in coal mines adopts self-aligning roller bearings with a large internal clearance， and its dynamic characteristics are more complex than those of traditional rotating machinery. The synergistic effect of multiple factors— excitation of the eccentric block， large internal clearance of the bearing， and elastic support of the elastic seat—induces vibrations between components. This produces a strong coupling effect and causes drastic variations in the internal load of the bearing. Under the dual action of intense external alternating excitation and internal local contact alternating stress， fretting wear is highly likely to occur on the mating surface between the bearing and the shaft， which poses a serious threat to the safe and reliable operation of coal preparation equipment. At present， research on fretting wear of bearing mating surfaces in vibrating machinery remains at a nascent stage. [Methods] Based on the typical vibration characteristics of vibrating machinery， this paper conducts a virtual simulation experiment on the fretting behavior between the inner ring fitting surfaces of the exciter bearing of a mining vibrating screen using the finite element simulation software ABAQUS. Through simulation， the load environment of the inner ring fitting surface under fretting behavior is reproduced， and the fretting wear model is further improved and established. In addition， the Umeshmotion subroutine suitable for interference fit structures is developed using the ALE mesh adaptation technology in ABAQUS to simulate the wear damage caused by fretting behavior. This paper proposes evaluating the fretting wear of the bearing based on the wear rate with respect to the number of cycles and introduces a dynamic wear coefficient to optimize fretting behavior during the fretting process. Furthermore， the influence of different internal and external factors on fretting wear damage is analyzed， and the changing trends and wear mechanisms of fretting behavior on the inner ring fitting surface are explored. [Results] The fretting simulation of the inner ring mating surface shows that (1) Fretting wear is mainly concentrated in the edge region of the inner ring and exhibits a symmetrical distribution; (2) An increase in rotational speed significantly accelerates the wear process and shortens the duration of the initial wear stage; (3) An increase in the mass of the eccentric block mainly accelerates fretting wear at the edge position and intensifies fluctuations in the wear rate， leading to an increased risk of early failure; (4) Either excessively small or excessively large interference fits aggravate local fretting wear， whereas an intermediate interference fit of 0.036 mm， within the reasonable range， results in the minimum wear amount and wear rate. [Conclusions] Experimental-based analyses of fretting wear on mating surfaces require the coordinated control of multiple variables. Continuous tracking of changes in fretting wear necessitates frequent disassembly; however， the disassembly and assembly of mating surfaces themselves can cause surface wear， which introduces interference. This makes it difficult to study fretting wear on mating surfaces through experimental methods. This paper proposes a research method based on simulation techniques that provides an effective solution to this problem. In addition， by implementing this simulation experiment， students can gain an in-depth understanding of the impact of fretting behavior on mating surface wear. From an engineering application perspective， this approach also enhances students’ understanding of theoretical knowledge related to mechanical design， such as mating mechanisms.
Integrated experimental platform for identifying control parameters of doubly fed induction generators based on hardware-in-the-loop impedance testing
ZHANG Xu;WANG Qun;XU Xin;WANG Jiangtao;WANG Jiyu;XIE Yuhan;FANG Xiaoyu;ZHOU Zhimei;XIE Xiaorong;[Objective] This study aims to enhance the teaching quality of electrical engineering courses and address students’ challenges in traditional laboratory instruction， specifically， difficulties in bridging theory with practice and insufficient hands-on experience with cutting-edge engineering problems. It considers two critical industry trends: increasing penetration of doubly fed induction generators (DFIGs) in power systems and the growing prominence of DFIG-related grid stability issues. Commercial wind turbines typically utilize encapsulated controllers with opaque parameters， which pose considerable challenges for power system stability analyses and controller optimization. To tackle these interconnected issues， this paper designs and develops an experimental platform dedicated to the parameter identification of DFIG controllers. [Methods] The platform leverages an improved particle swarm optimization (PSO) algorithm， and its operation is driven by impedance scanning data obtained from a hardware-in-the-loop (HIL) system. First， the study derives an analytical equivalent impedance model for DFIGs that explicitly considers rotor-side converter (RSC) and grid-side converter (GSC) control loops and phase-locked loop coupling effects. Building upon this model， the study establishes a clear mapping relationship between key controller parameters and the system’s frequency-domain impedance response. The model focuses on four critical controller parameters: for the rotor side， the d-axis current loop proportional gain (KP， d， RSC) and integral time constant (TI， d， RSC)， and for the grid side， the d-axis current loop proportional gain (KP， d， GSC) and integral time constant (TI， d， GSC). The core of parameter identification relies on the enhanced PSO algorithm， which employs Latin hypercube sampling combined with Gaussian perturbations for population initialization to effectively enhance population diversity. During the iterative process， the algorithm adopts a dynamic frequency weighting approach， assigning distinct weights to impedance errors across different frequency bands. This weighting prioritizes frequency ranges that are critical for system stability analysis， thereby ensuring more targeted optimization. Concurrently， the algorithm integrates a dynamic parameter management module， which successfully prevents the algorithm from being trapped in local optima by implementing particle perturbations based on boundary expansion and clustering detection. To ensure that the identification results are comprehensive and accurate， the fitness function integrates three key components: impedance magnitude-frequency error， phase-frequency error， and parameter grouping error. The experimental platform， constructed using the OP4510 real-time simulation system and National Instruments data acquisition boards， can perform standard impedance scans and collect high-precision frequency response data. Experimental tests were conducted on three double-fed wind turbines under varying active power output conditions (1.0， 0.5， and 0.1 per unit). For each test condition， the proposed improved PSO parameter identification method was applied to identify the four key controller parameters. [Results] The results indicate that the improved PSO algorithm effectively fits the measured impedance curves， demonstrating strong approximation capabilities. To enhance the reliability of parameter estimates， identification results across multiple operating conditions were weighted and averaged， yielding robust parameter values. These weighted parameters were then substituted back into the DFIG impedance model for validation. This step revealed significant reductions in impedance fitting errors， confirming the effectiveness of the proposed method and its engineering feasibility. Beyond its research applications， the developed experimental platform serves as a cutting-edge engineering practice tool， addressing a notable gap in current experimental teaching protocols for new energy power systems. By employing a visual， hands-on approach， the platform enables students to develop a deeper understanding of the intrinsic relationships between system impedance， controller parameter identification techniques， and system stability. This enriches electrical engineering students’ practical knowledge and holds significant value for cultivating innovative thinking and the ability to solve complex engineering problems. [Conclusions] The methodological framework and experimental validation presented herein provide a concrete contribution to the field of wind turbine controller analysis and pedagogical development in practical engineering education. By synthesizing advanced algorithmic optimization with real-time HIL experimentation， this study establishes a reproducible and effective paradigm to tackle similar black-box identification challenges in modern power electronic systems while serving as an invaluable resource to bridge the gap between theory and industrial practice.
Intelligent prediction method for the strength of magnesium-based tailings cementation using genetic algorithm-back propagation neural networks
WANG Qizhou;ZHU Zhiguang;LI Yang;ZHENG Bokun;SHI Yong;REN Gaofeng;[Objective] Mining and beneficiation of underground solid potassium salt deposits generate large quantities of solid tailings and old brine solutions. Environmental protection policies prohibit the discharge of solid and liquid waste. Therefore， the general practice is to mix solid and liquid tailings for underground backfilling， along with specific cementitious materials. Understanding the compressive strength of magnesium-based tailings cemented bodies under different proportions of filling materials is crucial for improving ore recovery and ensuring mining safety. However， there is currently no suitable method to predict the strength of these cemented bodies. Moreover， research on potassium salt mines is minimal， with limited available experimental data. In recent years， machine learning has emerged as an effective numerical prediction approach， demonstrating significant promise in material strength prediction. Compared with empirical formulas， machine-learning models offer greater generalizability and accuracy， and they can achieve satisfactory prediction accuracy even when available data are limited. [Methods] To investigate the strength of magnesium-based solid-liquid tailings cemented bodies under different mixture conditions， laboratory experiments， qualitative analysis， grey relational analysis， and intelligent algorithm coupling modeling were conducted. Uniaxial compression tests were performed on cemented specimens composed of solid tailings， old brine， and composite cementitious binders. A qualitative analysis of the factors influencing the uniaxial compressive strength of magnesium-based tailings cemented bodies was performed， and the grey correlation among these factors was analyzed. By comparing the mean absolute error (MAE) and mean square error (MSE) of predictions generated by neural networks with varying numbers of hidden layer neurons， the optimal structure for the back propagation (BP) neural network was determined. As a result， a 3-11-1 neural network structure was established， and a genetic algorithm (GA)-BP coupled prediction model was developed. This model was then applied for intelligent strength prediction of magnesium-based tailings cemented materials. [Results] The analysis revealed grey relational degrees for three key factors: the mass ratio of the composite cementitious binder to MgCl? (0.690)， the mass ratio of old brine to tailings (0.639)， and the mass ratio of fly ash in the composite binder (0.596). All three factors exhibited significant correlations with compressive strength， with the binder to MgCl? mass ratio identified as the most influential parameter. The GA-BP coupled model achieved an R value of 0.9775， MAE of 0.1625， root mean square error (RMSE) of 0.1771， and mean absolute percentage error (MAPE) of 5.24%. By contrast， the traditional BP model yielded an R value of 0.9038， an MAE of 0.2935， an RMSE of 0.3595， and a MAPE of 12.81%. The GA-BP model outperformed the traditional BP model， improving the four evaluation metrics by 8.15%， 44.63%， 50.74%， and 59.09%， respectively. [Conclusions] The findings indicate that the GA-BP model exhibits superior effectiveness and accuracy in predicting the strength of magnesium-based tailings cemented materials compared with the traditional BP model. This study presents a new approach for predicting the strength of magnesium-based solid-liquid tailings cemented bodies， providing a reliable and intelligent method applicable to analyzing and designing backfill materials in potassium salt mines.
Design of a high-power-density dual-active-bridge converter experimental platform
ZHANG Zhixiong;KUANG Weiyou;QIAN Zhong;TIAN Dawei;HE Dingxin;[Objective] The dual active bridge (DAB) converter has become widely used in power electronics education and research because of its advantages of low current stress， broad soft-switching range， and bidirectional power transfer. However， traditional teaching platforms often face limitations such as visualization of control strategies， high-frequency operational stability， and attainable power density. To overcome these issues， this study develops a digital signal processor (DSP)–based high-power-density DAB converter experimental platform that provides students with a deeper understanding of the DAB topology and its control principles， thereby improving experimental teaching and practical engineering outcomes in power electronics. [Methods] Through theoretical analysis， this study establishes systematic models and waveform derivations for single- and dual-pulse-width modulations (PWMs) combined with phase-shift control strategies. The inductor current expressions under various operating conditions， along with normalized power relationships， are analytically derived. These foundations support the implementation of modulation strategies on the experimental platform. Hardware-wise， the platform integrates the power conversion stage， isolated sensing and gate-drive circuits， and comprehensive protection mechanisms. Silicon carbide (SiC) MOSFETs and a planar transformer are used in the power conversion stage to achieve high-frequency， high-efficiency operation and meet high-power-density requirements. The sensing subsystem employs AMC1302， AMC1311， and Hall-effect sensors to enhance isolation accuracy and noise immunity， while the gate-drive subsystem utilizes the UCC21710QDWQ1 to ensure fast， reliable， and safe switching of SiC devices. The software framework revolves around Texas Instruments’ DSP280039 as the main control unit. The integrated advanced PWM， comparator subsystem， and analog-to-digital converter， along with other peripherals， enable duty-cycle regulation， phase-shift synchronization， overcurrent protection， and real-time system monitoring. Using this setup， a hardware prototype is built and tested under various control strategies to verify voltage and current waveforms. A comparative analysis highlights differences in current stress and energy transfer characteristics across the modulation strategies. [Results] Experimental findings show that PWM plus phase-shift modulation significantly reduces current stress during high-frequency operation. Under traditional single phase-shift control， the measured current stress is about 29.3 A. After applying duty-cycle regulation， the current stress drops to 23.0 A with the single-PWM plus phase-shift technique， and to 23.6 A with the dual-PWM plus phase-shift method. These results confirm that PWM-assisted control effectively optimizes current stress and enhances energy transfer， while also demonstrating the platform’s ability to verify various advanced modulation techniques. The high-power-density DAB platform operates stably at high frequencies， features compact system integration， and shows improved conversion efficiency and thermal performance. Furthermore， its complete sensing， driving， and protection mechanisms provide strong immunity to interference and robust fault response， ensuring reliable operation during laboratory teaching and research. [Conclusions] The DSP280039-based high-power-density DAB converter platform is compact， flexible， well-protected， and capable of stable high-frequency operation. It supports fundamental DAB control strategies and the validation of advanced modulation techniques， offering a solid experimental foundation for understanding energy transfer， modulation principles， and soft-switching characteristics. The platform’s excellent dynamic response and thermal stability make it a valuable platform for future developments in multiloop control， multimode modulation， and high-frequency converter research and education.
Design and application of a direct shear testing apparatus for cylindrical rock specimens
CHENG Xiaobing;ZHANG Zhilong;ZHANG Zhaopeng;ZHANG Ru;LIU Yang;CHEN Hongwei;LI Jianqiang;CAO Zian;[Objective] The ongoing advancements of large-scale water conservancy projects， deep ultralong tunnels， and deep geological storage facilities in geotechnical engineering present severe challenges to the safety of rock mass engineering. The shear strength of rock masses is crucial for engineering construction， as numerous instability incidents stem from shear-failure mechanisms. Therefore， the experimental studies of the shear mechanical properties of rock masses are highly necessary. However， traditional rock shear testing machines are only suitable for square specimens， suffering from uneven stress distribution caused by a rigid contact between the loading indenter and specimen pads. To address this problem， this study optimizes the performance of an existing laboratory rock shear testing machine. The modification simultaneously enables direct shear testing on cylindrical specimens and incorporates acoustic emission monitoring. It also improves the uneven distribution of normal stress on the rock specimens. This optimization is crucial for expanding specimen specifications， enhancing rock shear testing accuracy and scalability， and maintaining the long-term stability of rock engineering structures. [Methods] This study focuses on optimizing the TEST60 rock shear testing machine. The main body of the device comprises an operating box equipped with horizontal load bars on both sides to apply shear loads and a vertical load bar at the top to apply vertical loads. The front end of the horizontal load bar connects to upper and lower pads featuring arc-shaped grooves. These pads are secured via mounting holes and incorporate dedicated holes for acoustic emission sensors. The indenter of the vertical load bar is modified into a spherical shape， and springs are uniformly distributed along its lower edge， connecting to the outer wall of the pressure base to ensure uniform pressure distribution. Using this optimized shear testing machine， shear tests are conducted on cylindrical rock specimens with real-time acoustic emission monitoring. During shear testing， the vertical load rod and spherical indenter are manually lowered. Fine adjustments to the spherical indenter are possible throughout this process to ensure uniform vertical loading. [Results] The shear stress displacement curve exhibits typical prepeak and postpeak mechanical characteristics， effectively illustrating the shear failure mechanism of the specimen. Acoustic emission monitoring reveals minimal activity during the initial loading， followed by a highly active phase after the peak. Combined with the spatial localization of acoustic emissions based on phase segmentation， this clearly reveals the damage evolution mechanism in the specimen， from microcrack initiation to macroscopic fracture. The peak values of the shear stress displacement curve and acoustic emission precisely correlate with the test results， consistent with actual rock mechanical properties. This validates the effectiveness and applicability of the optimized rock shear testing machine. The spherical indenter ensures the uniform distribution of normal stress across the rock specimen， and the concealed acoustic emission sensors enable multimethod collaborative monitoring. [Conclusions] The optimized rock shear testing machine overcomes the limitations of traditional machines， notably their applicability to only square specimens， by incorporating cylindrical pads with holes for acoustic emission sensors， thereby expanding the range of suitable specimen dimensions. The vertical loading head is upgraded to a spherical design， resolving the issues of uneven normal stress distribution and enhancing the accuracy of rock mechanical parameter testing. The optimized direct shear testing apparatus， when combined with techniques such as digital image correlation， enables further investigation into shear failure mechanisms， enabling the execution of a broader range of rock mechanical tests.