[Objective]In-service oil and gas storage tanks often require local shell-plate replacement after prolonged operation.Repair welding imposes complex,non-uniform thermal cycles that can generate high residual stresses and degrade the microstructure and toughness of the coarse-grained heat-affected zone(CGHAZ).This study aims to(i)reveal the thermal-mechanical evolution during multi-layer,multi-pass repair welding of a low-alloy high-strength steel tank wall,(ii)quantify the spatial distribution of residual stresses in the circumferential and axial directions,and(iii)establish a process-cooling-microstructure relationship based on the cooling time t8/5,so that repair parameters can be optimized for safer service performance.[Methods]A three-dimensional finite element(FE)model representing a sector of a thin-walled storage tank was constructed for a representative repair scenario involving butt and T-joint configurations.The weld build-up was reproduced as a three-layer,single-pass sequence using element activation to simulate progressive filler deposition.A double-ellipsoidal moving volumetric heat source was adopted to represent gas tungsten arc welding(GTAW)heat input,and temperature-dependent convection and radiation were applied as boundary conditions.A sequentially coupled thermo-mechanical strategy was implemented:transient temperature fields were solved first and then mapped into an elastic-plastic mechanical analysis to predict residual stress after cooling.To evaluate the credibility of the model for field application,predicted stress profiles were compared with on-site measurements obtained by a coercive force-based method.Thermal cycles were extracted at CGHAZ-relevant locations to calculate t8/5,and parametric simulations were performed by varying current and travel speed.Continuous cooling transformation(CCT)behavior was assessed with JMatPro to interpret the tendency toward different transformation products under various t8/5 values.In addition,commonly used empirical formulas for t8/5 were compared with FE predictions to identify a suitable approach for thin-wall tank GTAW repair.[Results]The thermal model produced a stable molten-pool response under the selected parameters,with peak temperatures on the order of 2 100℃and continuous fusion along the modeled passes.The residual stress field was strongly localized:tensile stresses concentrated within the weld metal and adjacent HAZ,while balancing compressive stresses formed in the surrounding plate.Peak tensile residual stresses occurred near the HAZ,reaching approximately 428 MPa in the circumferential direction and approximately 408 MPa in the axial direction,indicating that near-weld regions dominate the structural risk during service.Along the weld path,stresses were comparatively smooth in the stable central segment,whereas across the weld,the gradients were steep,consistent with sharp thermal gradients and constrained shrinkage.Agreement between simulation and field measurements showed consistent trends and comparable magnitudes,with deviations within roughly 20%.The FE-computed t8/5 increased monotonically with heat input:for the studied window(approximately 0.80-1.28 kJ/mm),t8/5 rose from about 5 s to nearly 13 s,demonstrating that practical parameter adjustment can substantially alter the cooling severity.Among the examined empirical approaches,a thin-plate formulation provided the closest match to FE trends,whereas other formulas showed larger deviations for the present geometry.CCT-based interpretation suggested that shorter t8/5(faster cooling)increases the tendency toward bainitic/martensitic products and martensite-austenite(M-A)constituent formation,which may reduce impact toughness and raise cracking susceptibility in the CGHAZ,whereas excessively prolonged t8/5 may increase the risk of softening depending on service requirements.[Conclusions]A validated FE framework for multi-pass GTAW repair welding of low-alloy high-strength steel storage tank walls was developed to jointly evaluate temperature history,residual stress,and t8/5-controlled microstructure tendencies.The study confirms that tensile residual stress risk is concentrated in the weld and HAZ and that t8/5 can be systematically regulated through current and travel speed to support CGHAZ microstructure control.The combined FE-CCT workflow provides quantitative guidance for selecting balanced repair parameters that mitigate residual stress concentration while maintaining acceptable microstructure and mechanical performance.
[Objective]In carbon capture,utilization,and storage operations,the continuous injection of high-pressure CO2 into subsurface reservoirs exposes wellbore tubing to high-pressure,low-temperature,and corrosive CO2-rich environments.This increases the risk of local damage or perforation.If a tubing leak occurs,high-pressure CO2 is instantly released into the annular space between the tubing and casing,forming a highly transient confined jet.Unlike free jets,the confined jet in an annulus is strongly affected by geometric constraints,especially during the early leakage stage before wall impingement.This early-stage flow is characterized by rapid evolution,strong unsteadiness,and complex mixing,which are challenging to measure directly in field conditions.Therefore,it is necessary to investigate the early development characteristics of confined CO2 jets induced by tubing leakage under controlled experimental conditions.[Methods]A laboratory-scale experimental system utilizing a Z-type schlieren optical configuration was established to visualize the early-stage confined jet formed by CO2 leakage in a coaxial annular geometry.The actual wellbore structure was simplified into a concentric tubing-casing model,focusing on jet evolution prior to interaction with the casing wall.High-purity CO2 served as the working fluid.Experiments were conducted under representative operating conditions:a supply pressure of 4 MPa,a leakage orifice diameter of 1 mm,and an ambient temperature of approximately 18℃.High-speed schlieren image sequences were acquired at 24 390 frames/s to capture jet initiation and early development.Based on these schlieren images,a plume identification and jet-front displacement extraction method suitable for high-frame-rate image sequences was developed.This method combines background subtraction and adaptive threshold segmentation to extract candidate plume regions.A nozzle connectivity constraint ensures physical consistency between the identified plume and the leakage origin,whereas a temporal consistency veto mechanism suppresses abnormal segmentation results caused by wall-related schlieren interference and transient noise.A calibrated pixel-to-length conversion factor is used to convert the axial displacement of the jet front into physical distance,and the cumulative average propagation velocity is calculated.[Results]The experimental results show that the confined CO2 jet exhibits pronounced,rapid axial development immediately after leakage initiation.During the early stage,the jet front advances quickly along the annular axis,driven primarily by high initial momentum at the leakage orifice.As the jet develops,geometric confinement becomes increasingly significant,resulting in a gradual reduction in axial propagation rate and the emergence of lateral spreading and recirculation.Quantitative analysis indicates that the cumulative average jet-front velocity increases rapidly during the early stage and then approaches a quasi-stable level of approximately 60 m/s before wall impingement occurs.The calculated velocity's evolution trend is consistent with the schlieren-observed plume development,confirming the reliability of the proposed identification and measurement method.[Conclusions]A schlieren-based experimental approach,combined with a robust plume identification and jet-front tracking method,is developed to investigate early-stage confined CO2 jets induced by tubing leakage.Without relying on complex physical modeling assumptions,this method effectively suppresses wall-induced schlieren artifacts and transient disturbances,enabling the stable extraction of plume morphology and jet-front displacement from high-speed image sequences.The experimental results provide valuable insights into the transient evolution characteristics of confined CO2 jets in annular geometries,thereby offering a reliable experimental and analytical basis for further studies on leakage behavior and flow characterization in CO2 injection wells.
[Objective]The Vienna rectifier is widely used in three-phase power conversion systems owing to its advantages such as high efficiency and low harmonic distortion.However,its inherent topology limits its operation exclusively to conditions in which the grid voltage and current remain in phase.When a phase difference exists between the voltage and current,notable distortions arise in the AC current,particularly around the zero-crossing points,which severely affects the power quality and system stability.Such distortion increases total harmonic distortion,introduces electromagnetic interference,and reduces rectifier reliability.Therefore,analyzing the mechanism underlying zero-crossing distortion and developing an effective suppression strategy are essential for improving the performance of Vienna rectifiers in practical applications.[Methods]To address the zero-crossing distortion problem in the Vienna rectifier,this study proposes an improved direct power control(DPC)strategy integrated with dual second-order generalized integrators(DSOGIs)for frequency and phase synchronization.The mathematical model of the Vienna rectifier in the αβ coordinate system and the conventional DPC strategy in the same coordinate system are analyzed.The cause of current zero-crossing distortion is examined from a vector perspective,revealing that the distortion primarily arises from the phase mismatch between the grid voltage and the input current during switching transitions.Specifically,the three-level topological structure of the Vienna rectifier imposes inherent constraints on current commutation,aggravating phase asynchrony as the current approaches zero and resulting in irreversible current clamping and waveform distortion.Based on this analysis,a DSOGI-based frequency-locked loop is introduced to accurately track the grid frequency and a DSOGI-based phase-locked loop(PLL)is employed to lock the current phase.The phase adjustment is implemented through the real-time modification of the reference voltage vector in the DPC strategy,in which the phase information obtained from the DSOGI-PLL dynamically corrects the reference voltage angle,ensuring that the input current follows the grid voltage phase with high fidelity.By adjusting the phase relationship between the output voltage and current,the proposed method maintains in-phase operation,thereby reducing the distortion region around the current zero-crossing points.Voltage and current vector diagrams of the Vienna rectifier before and after the improvement are presented,along with the control block diagram of the current zero-crossing distortion suppression strategy for the DPC of the Vienna rectifier.[Results]The proposed DPC-based zero-crossing distortion suppression strategy is validated through detailed simulation studies.The results demonstrate that the method markedly reduces current distortion near the zero-crossing points.The output current quality is considerably enhanced,with a reduction in harmonic components and a more sinusoidal current waveform.[Conclusions]The simulation results verify the effectiveness and robustness of the proposed method,demonstrating its potential for enhancing the performance of Vienna rectifiers in practical power conversion systems.This strategy provides a feasible solution for suppressing zero-crossing distortion while preserving the advantages of traditional DPC,offering valuable insights for further research and application in high-performance rectifier designs.
[Objective]High-speed maglev trains,which combine the advantages of high speed,low noise,and low maintenance cost,have become integral to modern transportation systems owing to their electromagnetic suspension guidance and linear motor drive characteristics.However,during high-speed operation,long-wavelength track irregularities induced by deflection under load constitute the primary source of vibration excitation.These excitations exhibit broad frequency bandwidth,vary dynamically with vehicle speed,and contain multiple harmonic components,markedly compromising ride comfort and posing potential safety risks,representing a key technological bottleneck limiting the performance enhancement of high-speed maglev trains.Traditional control methods,such as neural network-optimized PI control and robust control,have partially improved vibration suppression through parameter optimization or frequency-domain design;however,they struggle to adapt to the dynamic frequency variations associated with long-wavelength irregularities,failing to achieve adaptive control for time-varying multi-frequency vibrations,thus falling short of fundamentally resolving the problem.To address this challenge,this paper focuses on the single-electromagnet suspension system of high-speed maglev trains and develops targeted control strategies.[Methods]First,based on track beam irregularity characteristics and electromagnetic mechanics principles,while neglecting minor disturbances such as magnetic reluctance and leakage,a physical model of the single-electromagnet suspension system is established,clarifying the mathematical relationships among coil voltage,current,suspension gap,and electromagnetic force.Through Taylor expansion around the static equilibrium point and by ignoring higher-order small terms,the system is linearized.Using Newton's second law,the open-loop dynamic equation of the suspension system is derived,thereby laying a theoretical foundation for controller design.Second,to address the core issue that the fundamental frequency of long-wavelength irregularities varies dynamically with speed,a second-order generalized integrator frequency-locked loop(SOGI-FLL)is designed:the SOGI extracts specific frequency components,while the FLL detects frequency deviation in real time and adjusts resonance characteristics,enabling precise and real-time identification of the fundamental frequency and providing the basis for adaptive control parameter adjustment.Building on this foundation,a fractional-order repetitive control method based on Lagrange interpolation finite impulse response(FIR)filtering is proposed.To overcome the internal model tracking error that arises when the delay order n in traditional repetitive control is non-integer,n is decomposed into integer and fractional parts.An integer delay is realized via an integer-period delay module,whereas a fractional delay is approximated using a FIR filter designed through Lagrange interpolation,thereby accurately matching the excitation frequency.A second-order Butterworth low-pass filter is introduced to suppress high-frequency resonance,and a phase compensation term zk corrects phase lag,completing the repetitive control architecture.This architecture is then integrated with displacement-velocity-acceleration state feedback control to form a synergistic strategy.To validate its effectiveness,a Simulink simulation platform was built with parameters including a reference suspension gap of 0.01 mm,a sampling frequency of 1 kHz,and a beam span of 24 m.A half-sine wave track deformation of 0.05 mm was simulated at speeds of 100 km/h and 400 km/h for comparative testing.[Results]The results demonstrate that the proposed method achieves both marked vibration suppression and excellent frequency adaptability.At 100 km/h,the electromagnet vibration amplitude under traditional control reached 4 mm,while the proposed method reduced it to 0.6 mm.Under high-speed conditions of 400 km/h,traditional control exhibited severe vibration,whereas the proposed method enabled rapid,stable convergence of the suspension gap to 10 mm without overshoot and with fast dynamic response,fully adapting to the frequency dynamics demanded at high speeds.By employing the SOGI-FLL to track the fundamental frequency in real time and integrating a fractional-delay compensation mechanism,the method precisely matches the disturbance fundamental frequency and suppresses all harmonic components,overcoming the technical limitations of conventional approaches.[Conclusions]The proposed control method overcomes the limitations of traditional control technologies in suppressing time-varying multi-frequency disturbances.It achieves accurate and efficient suppression of vibrations induced by long-wavelength track irregularities in high-speed maglev trains through the organic integration of dynamic modeling,fundamental frequency adaptive identification,and fractional-order repetitive control architecture.Offering marked vibration suppression,excellent dynamic response,and strong frequency adaptability,the method effectively enhances ride comfort and operational safety of maglev trains at medium-to-high speeds,providing essential theoretical and technical support for the engineering application of these technologies.
[Objective]As new power systems characterized by high renewable energy penetration continue to evolve,accurately and efficiently identifying photovoltaic(PV)model parameters has become critical for performance prediction,optimal operation,and intelligent maintenance of PV power plants.Classical models,including the single-diode model(SDM),double-diode model(DDM),and module-level models,contain multiple nonlinear parameters that resist precise estimation.Conventional parameter identification methods frequently exhibit slow convergence,premature stagnation,and poor robustness.Meanwhile,in engineering education,the inherent complexity of nonlinear modeling and metaheuristic optimization poses considerable challenges for students seeking to master the full identification workflow.Against this backdrop,the present study proposes an improved parameter identification algorithm offering enhanced accuracy and stability,alongside a modular virtual simulation-based experimental teaching platform that bridges algorithm research and educational practice in the context of new power systems.[Methods]An improved supply-demand optimization algorithm that incorporates a fitness-distance balance(FDB)mechanism and a half-uniform initialization strategy,designated FDB-SDO,is developed.The FDB strategy assesses candidate solutions by jointly weighing normalized fitness values and distance information relative to the current best solution,thereby maintaining an effective balance between global exploration and local exploitation.The half-uniform initialization strategy broadens population diversity during the early iterations,mitigating the excessive aggregation that purely random initialization can produce.Mutation reinforcement and opposition-based learning are additionally embedded to counteract premature convergence and bolster global search capability.FDB-SDO is applied to identify the parameters of SDM,DDM,and two representative PV module models.Root mean square error(RMSE)serves as the objective function,quantifying the fitting accuracy between simulated and experimentally measured I-U data.Performance is verified through comparisons with several well-established metaheuristic algorithms under identical parameter settings.Concurrently,a modular virtual simulation-based experimental teaching platform is constructed that decomposes the identification process into discrete modules spanning model cognition,objective function construction,algorithm implementation,multi-strategy improvement,convergence analysis,and result evaluation.[Results]Experimental results demonstrate that FDB-SDO attains competitive or superior RMSE values while requiring fewer objective function evaluations than the comparison algorithms.For both SDM and DDM,the proposed method produces lower or equivalent fitting errors at reduced computational cost,reflecting improved efficiency.In the more demanding DDM scenario involving seven parameters,FDB-SDO exhibits stronger robustness and more precise estimation of diode-related parameters.For module-level models,the algorithm sustains stable convergence and consistent identification accuracy.Statistical analysis across multiple independent runs yields smaller standard deviations,confirming enhanced stability.The I-U and P-U curves reconstructed from the identified parameters closely reproduce the experimental measurements,validating the reliability of the proposed approach.From an educational standpoint,the modular platform allows students to visualize convergence trajectories,benchmark algorithm performance,and examine how individual strategies affect identification accuracy.[Conclusions]FDB-SDO effectively accelerates convergence,elevates identification accuracy,and strengthens robustness in PV model parameter extraction.Through the integration of fitness-distance guidance and diversity-preserving initialization,the algorithm mitigates premature convergence and reinforces global search capability.The accompanying modular virtual simulation teaching design converts a complex optimization problem into structured,progressive learning tasks that connect theoretical modeling with engineering application.This framework offers a scalable instructional paradigm for nurturing innovative talent in renewable energy engineering within the context of new power systems.
[Objective]Carbon fiber reinforced polymer(CFRP)thin-walled structures are widely used as core materials for lightweight energy-absorbing components in the aerospace and automotive industries owing to their exceptionally high specific stiffness,specific strength,and specific energy absorption(SEA).However,CFRP tubes face limitations in engineering applications,including high manufacturing costs and a tendency toward sudden brittle failure during crushing.To address these shortcomings,combining highly ductile metals with CFRP to form multi-material hybrid tubes has become an active area of research.Because traditional methods and costly physical experiments cannot readily quantify internal damage mechanisms,this study aimed to develop a high-precision finite element simulation platform.The platform overcomes technical barriers such as three-dimensional progressive damage modeling,thereby strengthening students'ability to analyze crushing failure mechanisms and multi-material composite effects.[Methods]This study adopted a combined experimental and numerical approach.First,quasi-static axial crushing experiments were performed on CFRP square tubes prepared through a vacuum bag hot-pressing process.The tubes,consisting of five alternating layers of 0° and 90° carbon fiber prepreg,provided real force-displacement responses and macroscopic deformation modes for baseline verification.Second,the finite element simulation platform was built using the ABAQUS/Explicit solver.To accurately characterize intralaminar damage in composite materials,a VUMAT user-defined material subroutine was developed through Fortran programming to implement the updated three-dimensional Hashin progressive failure criterion.This mathematical model evaluates fiber tension/compression and matrix tension/compression damage in real time,executing stiffness degradation and element deletion once the ultimate failure thresholds are reached.In addition,cohesive elements governed by a traction-separation law and a quadratic nominal stress damage criterion were inserted between composite plies to simulate interlaminar delamination.Finally,using the verified platform,an extended study on aluminum/carbon fiber(Al/CFRP)hybrid tubes was conducted.Two distinct configurations were designed based on the stacking sequence:the C-A tube(an aluminum outer tube with a CFRP inner layer)and the A-C tube(a CFRP outer tube with an aluminum inner layer).The dynamic evolution processes and interfacial coupling mechanisms of these structures under identical axial crushing conditions were systematically analyzed and compared.[Results]The experimental and simulation results indicated the following:1)The platform accurately replicated the macroscopic progressive instability and microscopic brittle fracture of CFRP tubes,with the errors in core energy absorption indicators held within 5%;2)As the lamination sequence changed,the failure modes exhibited entirely opposite composite effects;3)The A-C configuration experienced early outward tearing,leading to premature loss of lateral constraint and rapid disappearance of the composite effect;4)Conversely,the C-A configuration effectively suppressed the peeling and chaotic delamination of the internal CFRP layer through the strong hoop constraint provided by the external aluminum tube.Compared with the CFRP tube,the energy absorption(EA),mean crushing force(MCF),and SEA of the C-A configuration increased by 269.42%,269.61%,and 37.47%,respectively.[Conclusions]The platform proves to be a highly reliable tool for analyzing the complex failure mechanisms of anisotropic materials.The study demonstrates that introducing metal into composite structures fundamentally mitigates sudden brittle failure,achieving deep interfacial synergy.Furthermore,the teaching cases developed through this platform enable students to systematically master crushing experimental methods,finite element modeling,and comparative analysis strategies within a limited timeframe.This approach overcomes the observational limitations of physical experiments and considerably deepens students'understanding of synergistic failure mechanisms in complex engineering structures.
Objective Airtightness, the ability of a product to prevent gas leakage, is a critical performance indicator for high-performance equipment in industries such as aerospace, shipping, and chemical engineering. During the manufacturing process, airtightness testing of key components is essential. The liquid application method is widely used in batch manufacturing due to its low cost and operational simplicity. Traditional manual inspection, although a common method, is highly subjective, difficult to quantify, and inefficient. It also carries a high risk of missed or incorrect judgments due to inspector fatigue. To address these limitations, this study proposes a quantitative airtightness testing system based on the liquid application method and computer vision technology to transition from manual observation to objective, automated detection. Methods The proposed system, developed using the PyQt5 framework, integrates image acquisition, parameter configuration, camera calibration, intelligent soap-bubble detection, bubble size measurement, and result visualization. It runs on a platform equipped with a GTX1050Ti graphics card and supports up to six cameras simultaneously for comprehensive multi-view surface coverage. For accurate physical measurement, a camera calibration method using standard spheres is employed. By placing 5 mm standard spheres on the product surface, the system applies Canny edge detection and Hough circle transform to extract sphere contours and compute local pixel-to-millimeter ratios. To compensate for surface curvature and lens distortion, a cubic spline interpolation algorithm is used to establish a global mapping across the detection area. For bubble detection and segmentation, particularly for bubbles with irregular shapes, varying sizes, and complex backgrounds, a lightweight YOLOv11 instance segmentation network is adopted. A dataset of more than 1,200 images collected from real experimental environments is constructed for training. Through transfer learning and iterative optimization, model parameters are reduced to approximately 40% of the standard version while maintaining high detection accuracy. Following segmentation, the maximum chord length method calculates the equivalent diameter of each bubble, avoiding inaccuracies associated with traditional equivalent circle assumptions. Results Experimental validation was conducted on real product components with known airtightness defects. A total of 627 images were collected from six viewing angles. The system demonstrates stable detection and tracking of soap-bubble generation and evolution. It achieves a detection accuracy of 95.3% for bubbles exceeding a predefined threshold, with average and maximum measurement errors of 0.19 mm and 0.30 mm, respectively. The optimized YOLOv11 model maintained high segmentation accuracy while achieving an inference speed of over 15 frames/s. The multi-camera configuration and calibration method ensure reliable measurement consistency across the detection area, effectively supporting quantitative leakage analysis. Conclusions The proposed system transforms the traditional subjective soap-bubble inspection method into an objective, quantitative, and automated approach for airtightness testing. By integrating multi-camera imaging, precise calibration, deep learning-based bubble segmentation, and the maximum chord length method, the system substantially improves detection reliability.
[Objective]This study aims to develop and evaluate a low-cost suppression jamming experimental platform using software-defined radio(SDR)technology.The platform's ability to generate multiple types of suppression jamming signals in a controlled wired-loop environment was assessed.The proposed platform provides a flexible,configurable,and cost-effective solution for evaluating GNSS receiver anti-jamming performance,while avoiding the complexity and high cost of commercial jamming equipment.[Methods]A suppression jamming experimental platform was designed and implemented using GNU Radio and HackRF hardware.It features a modular architecture comprising parameter configuration,jamming signal generation,signal selection,waveform monitoring,and radio frequency(RF)transmission modules.Four representative suppression jamming waveforms were generated:continuous wave,frequency-swept,pulse,and band-limited Gaussian noise jamming.Jamming parameters such as center frequency,sweep characteristics,pulse repetition pattern,and output power could be configured via software interfaces.To verify signal generation accuracy,a spectrum analyzer and oscilloscope were used to evaluate the frequency-domain characteristics and pulse-modulation timing performance of the generated signals.The platform was further integrated into a wired-loop test environment,enabling direct injection of jamming signals into the RF input of GNSS receivers.This configuration provides a repeatable and controllable testing environment,eliminating uncertainties caused by wireless propagation.To assess jamming effectiveness,comparative experiments were conducted between the proposed low-cost SDR platform and a commercial high-cost jammer under identical test conditions.The carrier-to-noise density ratio(C/N0)variation and attenuation characteristics of the receiver were selected as the primary evaluation metrics.In addition,dynamic interference experiments were performed to investigate platform performance under motion conditions and evaluate its capabilities in continuous jamming signal generation.[Results]Experimental verification demonstrated that the generated suppression jamming signals exhibit spectral characteristics and temporal behaviors consistent with theoretical design expectations.Measured center frequencies,bandwidths,sweep patterns,and pulse timing parameters showed good agreement with configured values,confirming the accuracy of signal generation and modulation processes.Comparative testing indicated that the proposed SDR platform and the commercial jammer produced similar interference effects on GNSS receivers.Within the GPS L1 frequency band and over a jammer-to-signal ratio range of 30-60 dB,both systems resulted in nearly identical trends of receiver C/N0 degradation.As interference intensity increased,both platforms progressively deteriorated signal quality,eventually leading to receiver tracking failure and signal loss-of-lock.The observed attenuation characteristics and loss-of-lock thresholds exhibited strong consistency between the two jamming sources.Dynamic experiments further demonstrated that the proposed platform can continuously generate stable suppression jamming signals during motion without considerable frequency drift or power fluctuation.The platform effectively degraded receiver tracking performance and maintained stable interference throughout the test.These results verify the reliability and practicality of the proposed system for dynamic anti-jamming experiments.[Conclusions]A low-cost SDR-based suppression jamming experimental platform was successfully developed and validated.Experimental results demonstrate that the proposed platform accurately generates multiple suppression jamming waveforms,achieving interference effects comparable to commercial high-cost jammers in the GPS L1 band.The platform offers several advantages,including low implementation cost,flexible architecture,convenient software configuration,and strong scalability.By supporting various suppression jamming modes and providing stable operation in both static and dynamic environments,it serves as an effective experimental tool for evaluating GNSS receiver anti-jamming performance,analyzing interference mechanisms,and other applications in navigation research.
[Objective]Traditional gas sensing methods,such as semiconductor and electrochemical sensors,face significant limitations in harsh and confined environments due to cross-sensitivity,poor electromagnetic compatibility,and their inability to meet stringent intrinsic safety standards.Although photoacoustic spectroscopy offers high selectivity and sensitivity,its reliance on resonant cavity dimensions and electromagnetic microphones restricts its use in space-constrained settings.To address these limitations,this study develops a gas sensing device that integrates high sensitivity,compactness,intrinsic safety,and electromagnetic immunity for reliable trace gas monitoring in critical industrial safety and process control applications.[Methods]The proposed sensor employs a dual-enhancement mechanism within a miniaturized 1 mL nonresonant photoacoustic cell.A Herriott-type multipass configuration,featuring two coaxially aligned concave mirrors,extends the effective optical path length to approximately 440 mm.This represents an order-of-magnitude enhancement over a single-pass configuration,significantly amplifying light-gas interaction and improving photoacoustic excitation efficiency without increasing the physical dimensions of the cell.For high-sensitivity signal detection,a cantilever-enhanced fiber-optic Fabry-Perot acoustic sensor is integrated into the photoacoustic cell.The cantilever's vibration modulates the Fabry-Perot cavity length,formed between its surface and the end face of the optical fiber ferrule,enabling all-optical detection of the photoacoustic signal.The complete measurement system employs a distributed-feedback laser as the excitation source,wavelength-modulated at half the cantilever's resonant frequency.This enables second-harmonic detection,effectively suppressing fundamental frequency noise.[Results]Comprehensive experiments were conducted to evaluate the sensor's performance using methane as the target analyte.Frequency response measurements demonstrated clear resonant enhancement,with the second-harmonic photoacoustic signal amplitude peaking sharply at the cantilever's resonance frequency of 1 970 Hz,confirming the optimal operating conditions.Spectral analysis of the time-domain signal acquired at a methane concentration of 50 µL/L revealed a distinct frequency component that matched the second harmonic of the modulation frequency,validating successful signal excitation and detection.The sensor exhibited an excellent linear response across methane concentrations ranging from 10 to 50 µL/L,with a calibration sensitivity of 36.73 pm/(µL·L-1),demonstrating high measurement repeatability and linearity.Based on the characteristic absorption coefficient of methane at 1 650.9 nm,the normalized noise equivalent absorption coefficient was calculated to be 9.96×10-10 cm-1¹ W/Hz1/2,representing state-of-the-art performance for non-resonant photoacoustic systems.Allan-Werle deviation analysis further revealed that extending the integration time to 100 s could improve the minimum detection limit to 7.24 nL/L,demonstrating excellent long-term stability.[Conclusions]This study successfully demonstrates a miniaturized all-optical photoacoustic gas sensor that enhances optical absorption via a Herriott-type multipass cell with mechanical resonance amplification via cantilever-enhanced fiber-optic acoustic detection.The sensor exhibits excellent methane-detection performance within a compact 1 mL photoacoustic cell volume.Its all-optical architecture ensures immunity to electromagnetic interference,intrinsic safety in explosive environments,low transmission loss for remote monitoring,and passive operation at the sensing end.Moreover,the requirement for only a 1 mL sample volume makes it particularly valuable for trace gas analysis when sample conservation is essential,as well as for online laboratory testing.Educationally,this sensor system integrates fundamental principles such as the photoacoustic effect,multi-pass optical path design,fiber-optic interferometry,and mechanical resonance.It provides an ideal experimental platform for students in optical system design,signal processing,and interdisciplinary applications.The demonstrated dual-enhancement strategy offers a promising technological pathway for developing next-generation high-performance miniature gas sensors suitable for harsh environments.
[Objective]Three-dimensional(3D)printing,widely referred to as additive manufacturing(AM)in industrial and academic communities,has emerged as a revolutionary manufacturing approach for fabricating complex,high-performance metallic components.Among various metallic materials suitable for AM,the Ti6Al4V(TC4)alloy stands out owing to its high specific strength,excellent corrosion resistance,and favorable biocompatibility.These properties collectively make it an indispensable structural material in the aerospace,biomedical,and marine engineering sectors.The marine environment constitutes a particularly demanding service condition in which structural materials are subjected to the combined effects of mechanical wear and aggressive electrochemical corrosion;this synergistic degradation process is defined as tribocorrosion.For potential marine applications(e.g.,propulsion system components,underwater connectors,valve parts,and offshore platform fittings),elucidating the tribocorrosion behavior of AM-fabricated TC4 alloy is critical for predicting its service life and ensuring structural integrity in practical engineering.[Methods]A simulated seawater solution was formulated in accordance with the ASTM G1148-98 standard for experimental tests,and its pH was adjusted to 8.2 with 0.1 mol/L HCl and NaOH aqueous solutions;ultrapure water was employed as the control medium.The chemical composition of the as-received AM-fabricated TC4 alloy was determined by inductively coupled plasma optical emission spectrometry,while the contents of oxygen,nitrogen,and hydrogen were quantified by mass spectrometry.Tribocorrosion tests of the AM-fabricated TC4 alloy in simulated seawater and ultrapure water were performed on an MFT-5000 multifunctional tribo-tester using a 5 mm diameter silicon nitride ceramic ball as the tribological counterbody.TC4 alloy was wire-electrode cut into cuboid specimens with dimensions of 10 mm×10 mm×10 mm for the tests.Open-circuit potential(OCP)and polarization curves during the tribological wear process were monitored using in situ electrochemical methods.After the tribocorrosion tests,the surface topographies of the tested TC4 specimens were characterized by white-light interferometry for 3D surface profiling and wear volume analysis.Morphological characterization of the worn surfaces was carried out using scanning electron microscopy,and the elemental composition and chemical states of the worn surface films were analyzed by X-ray photoelectron spectroscopy.[Results]This study investigated the tribocorrosion behavior of AM-fabricated TC4 alloy in simulated seawater and systematically examined the correlations among applied load,sliding frequency,friction coefficient,and wear loss of the alloy.In situ electrochemical tests were simultaneously conducted to monitor the OCP evolution and polarization curves during the tribocorrosion process.The results showed that under the same applied load in aqueous media,the friction coefficient of TC4 decreased as the sliding frequency increased.At constant load and sliding frequency,the friction coefficient of the alloy in simulated seawater was considerably lower than in ultrapure water.Wear volume loss decreased gradually with increasing sliding frequency,while the wear volume loss in simulated seawater was markedly higher than in ultrapure water under identical test parameters.During tribocorrosion,the OCP of the alloy exhibited a trend of rapid decline,followed by stabilization.The OCP values and polarization curves of the alloy in simulated seawater shifted toward more negative potentials than those in ultrapure water.The dominant wear mechanism of AM-fabricated TC4 alloy in simulated seawater was identified as a synergistic interaction between oxidative wear and adhesive wear.[Conclusions]The tribocorrosion performance of AM-fabricated TC4 alloy depends strongly on the mechanical parameters(load and sliding frequency)and the chemical nature of the service environment.Although the simulated seawater environment paradoxically reduces the friction coefficient of the alloy,it markedly exacerbates overall material degradation arising from the pronounced synergistic effect of electrochemical corrosion and mechanical wear.This comprehensive insight into the tribocorrosion behavior and degradation mechanisms of AM-fabricated TC4 alloy is critical for the reliable and safe application of AM-fabricated titanium alloys in next-generation marine and offshore engineering systems.Furthermore,the findings provide a fundamental experimental basis for subsequent performance optimization and surface modification of AM-fabricated titanium alloys for marine service.
[Objective]Conventional mechanical property testing methods,including uniaxial tensile and impact tests,are predominantly destructive.These methods are inherently disruptive,cumbersome to perform,and often impractical for the in-service inspection of pressure vessels in engineering environments.While the indentation technique has demonstrated potential as a micro-destructive testing method that derives mechanical properties from load-displacement curves,its application remains largely limited to industrial evaluations.Consequently,current engineering education frameworks are not fully aligned with modern practices,resulting in students receiving limited exposure to modern mechanical property testing technologies.To address this critical discrepancy,the present study developed an integrated mechanical testing apparatus based on the continuous spherical indentation method.The objective was to establish a unified platform that integrates laboratory validation with real-world field operations,thereby enhancing the practical and innovative skills of engineering students.[Methods]To achieve these objectives,the experimental device was designed with a highly modular configuration,encompassing mechanical,control,and data processing modules.The mechanical module features a lightweight alloy frame that is integrated with versatile fixtures,including U-shaped and magnetic clamps.These fixtures are suitable for both flat and curved specimens commonly encountered in engineering environments.The control module employs a hybrid software-hardware configuration,integrating a microcontroller with high-precision sensors for displacement and tension-compression measurements.This configuration ensures accurate signal capture while mitigating electromagnetic interference.The data processing unit incorporates a continuous spherical indentation algorithm that facilitates the automated conversion of raw load-depth data into true stress-strain curves.This capability enables the direct extraction of critical mechanical parameters.Validation was conducted through a series of comparative experiments on three representative pressure vessel steels.The following materials were utilized:Q235B,45 steel,and Q345R.A spherical indenter with a diameter of 1.5 mm was used under a preload of 5 N,and the loading rate was maintained at 0.4 mm/min.Eight consecutive loading-unloading cycles were executed,and the outcomes were systematically compared with those of standard uniaxial tensile tests.Furthermore,a simulated field experiment was designed to assess a large-scale Q245 carbon steel pressure vessel to evaluate local mechanical properties across the base metal,heat-affected zone,and weld seam.[Results]The validation experiments demonstrated the high accuracy and reliability of the developed indentation testing device.A comparison was made between the indentation-derived data and the corresponding standard uniaxial tensile test outcomes for the three selected vessel steels.The relative errors for yield strength ranged from-4.29%to 4.80%,while those for tensile strength ranged from-2.68%to 3.32%.The maximum recorded deviation remained below 5%threshold,thereby satisfying the stringent precision requirements for both academic experiments and engineering applications.Furthermore,the on-site simulation experiment successfully differentiated the mechanical properties variations within the welded joints of the Q245 steel vessel.Micro-destructive testing revealed the performance gradient,indicating a stable and uniform base metal,as well as pronounced performance fluctuations within the weld seam due to thermal processing.These findings were accompanied by intermediate properties observed in the heat-affected zone.From a pedagogical perspective,these practical implementations enabled students to develop a nuanced understanding of the discrepancies between idealized laboratory conditions and the intricate dynamics present in complex field environments.This enhanced their awareness of engineering norms and rapid deployment strategies,thereby fostering a more comprehensive and nuanced understanding of engineering practices.[Conclusions]The development and deployment of this indentation-based testing apparatus effectively addresses the prevalent limitations associated with conventional destructive testing in educational settings.The integration of interdisciplinary methodologies and advanced sensing technologies enables the device to serve a dual purpose,supporting fundamental experimental teaching and complex engineering field simulations.The high-precision acquisition of load-depth information,in conjunction with flexible structural adaptations,demonstrates significant practical engineering value.The integration of this apparatus into the curriculum has been demonstrated to have a substantial impact on students'technical proficiency and problem-solving skills.It offers a robust and innovative instructional paradigm that has been tailored to the contemporary engineering education landscape.
[Objective]Internal leakage in the early stages within valves of hydropower-unit auxiliary systems(e.g.,compressed-air subsystems)is typically minor,concealed,and heavily influenced by operating-condition variability,making it difficult to detect using fixed empirical thresholds.Supervised learning approaches are similarly constrained by the scarcity of on-site fault samples.This study establishes a practical acoustic anomaly-detection framework that(i)learns healthy valve acoustic signatures primarily from normal data,(ii)remains applicable across multi-pressure operating conditions,and(iii)supports robust alarm decision-making through condition-aware and online-adaptive thresholding.[Methods]A controllable experimental platform was constructed by replicating the layout of a hydropower-station auxiliary compressed-air system and incorporating a real in-service DN200 hemispherical valve as the test object.Valve sounds were recorded using two microphones placed symmetrically around the valve body at approximately 1.0-1.1 m,with synchronized timestamps to ensure consistent multi-channel acquisition.Data were collected under multiple pressure levels ranging from 0.2 to 0.7 MPa.Two representative states were examined:a fully closed valve representing the normal condition and a 10%opening used to emulate internal leakage.To improve the stability of acoustic inputs,multi-channel waveforms were aligned via cross-correlation,DC offsets were removed,and a band-pass filter was applied to retain diagnostically relevant frequency content.Recordings were then resampled to a unified sampling rate,and segments exhibiting clipping or prolonged saturation were discarded.The processed signals were segmented using a fixed-length sliding window(approximately one second per segment)with different strides for training and evaluation,allowing normal data to provide sufficient training diversity while preserving high temporal coverage during testing.Log-Mel spectrograms were extracted as compact time-frequency representations through short-time Fourier transform,Mel filter-bank projection,logarithmic compression,and per-channel standardization based solely on normal data statistics.On the modeling side,a self-supervised dual-path Transformer(SSDPT)was employed to alternately capture dependencies along the time and frequency dimensions,enabling fine-grained characterization of leakage-induced spectral structures and their temporal evolution.Training combined a discriminative identification objective(learning to recognize normal operating signatures across groups and conditions)with a reconstruction objective under random patch masking,encouraging robust representation learning without requiring extensive labeled fault samples.During inference,anomaly scores were computed primarily from classification-based confidence decay(i.e.,reduced confidence in the learned healthy identity implies greater abnormality),and this scoring strategy was compared against reconstruction-inclusive alternatives.[Results]The classification-based score provided the most reliable separation between normal and leakage segments.Across the complete dataset,the overall area under the receiver operating characteristic(ROC)curve reached 0.707,while the partial area under the curve(AUC)in the low-false-alarm region(false positive rate≤0.10)reached 0.417,indicating meaningful discrimination capability under practical low-false-alarm constraints.Performance was,however,strongly pressure-dependent:medium and high pressures exhibited clearer separability and more stable high-score tails associated with leakage,whereas certain low-and mid-pressure conditions showed substantial score overlap between normal and leakage segments,limiting the effectiveness of a single global threshold.Pressure-wise analysis revealed near-complete separability at the highest pressure level and useful separability at some medium pressures,while other lower-pressure settings approached chance-level ordering except for a small subset of strongly abnormal segments detectable at very low false-positive rates.To translate scores into actionable alarms,two complementary thresholding mechanisms were developed.First,pressure-level-specific thresholds were designed to compensate for systematic distribution shifts across pressures and to reduce mismatches caused by mixed-condition score scaling.Second,an online adaptive threshold scheme was formulated to update alarm boundaries during long-term operation by tracking a rolling high quantile of recent scores and calibrating robustness via median absolute deviation,thereby improving stability against gradual background drift and intermittent disturbances.[Conclusions]This study demonstrates that SSDPT-based self-supervised acoustic anomaly detection can serve as a feasible,engineering-oriented approach for internal leakage monitoring in hydropower auxiliary valves when fault labels are limited.Multi-pressure experiments confirm that operating conditions markedly affect score distributions and detection separability,making condition-aware thresholding essential for reliable deployment.The proposed pressure-level and online adaptive threshold strategies enhance decision robustness across operating regimes and over time.Remaining challenges are concentrated in low-pressure scenarios,where leakage signatures may be weak or masked by background noise.Future work may address these regimes through richer sensing configurations,acoustic-vibration multimodal fusion,and validation within longer-term field monitoring pipelines.
[Objective]The fundamental operational nature of modern internal combustion engine(ICE)systems is intrinsically defined by an extremely strong degree of nonlinearity,as well as the intricate and multifaceted complex characteristics associated with high-dimensional interactions and multi-parameter coupling effects.The inherent physical complexities of the subject make it exceptionally difficult for traditional experimental teaching methodologies to successfully achieve high-precision combustion prediction or to realize the effective improvement of overall system energy efficiency in a consistent manner.One reason for this is that such methodologies often lack the necessary capacity for handling such massive amounts of multivariate data effectively.Within the specific context of contemporary engineering education,it has become imperative and critical to develop practical,technically effective,and systematic teaching frameworks.The overarching objective of these frameworks is to systematically foster and cultivate students'essential capabilities in the fields of computational modeling and strategic decision-making.This enables them to successfully navigate the multifaceted challenges involved in dealing with the modeling and optimization of the complex engineering problems encountered in real-world scenarios.[Methods]This teaching reform is a direct and targeted response to identified pedagogical and technical challenges.It proposes an innovative,comprehensive,and integrated method specifically designed for the purposes of ICE combustion prediction and smart energy management.This method integrates advanced machine learning technologies into the existing curriculum in a seamless and deep way.The methodological framework is meticulously implemented through a rigorous,structured,and sequential process.First,a systematic sampling design strategy is meticulously executed for the specific test content to ensure comprehensive data coverage of the operating space.The extensive datasets obtained from these comprehensive experimental tests are then utilized as the foundational material to train sophisticated machine learning models.Following this preliminary data acquisition phase,a robust and high-fidelity ensemble tree surrogate model is computationally constructed to achieve high-precision fitting and accurate prediction of critical ICE performance indicators.This surrogate model serves as a reliable digital proxy for the physical engine system.Finally,advanced multi-objective optimization algorithms are integrated into the workflow to process these predictive models,with the aim of generating a Pareto optimal solution set that represents the ideal mathematical balance between conflicting operational goals.[Results]The results of the quantitative optimization derived from the implementation of the present study provide compelling and robust empirical evidence that demonstrates the practical effectiveness of the framework.The experimental data unequivocally demonstrate that,under the stipulated trade-off operating condition,ICE's fuel consumption is significantly reduced by a substantial margin of 14.5%.Concurrently,the environmental performance has been notably enhanced,with exhaust emissions of hydrocarbons(HC),carbon monoxide(CO),and nitrogen oxides(NOx)reduced by 7.0%,5.7%,and 32.8%,respectively.This outcome validates the efficacy of the proposed optimization strategy.[Conclusions]In summary,the test teaching reform framework was successful in establishing a complete,comprehensive,and logical closed loop encompassing the iterative stages of"experimental design-data modeling-optimization feedback."This structured pedagogical approach enables students to have a solid foundation in the scientific research process,extending seamlessly from the initial phases of test design to the final and critical stages of data analysis and parameter optimization.Moreover,it considerably augments their comprehensive capacity to solve complex scientific problems by efficaciously bridging the divide between theoretical academic knowledge and practical engineering application.This teaching reform offers a valuable,replicable,and promotable paradigm for the future teaching reform practice in energy and power-related majors.
[Objective]To address the inadequacies in management of safety hazards at Sun Yat-sen University,this study innovatively introduced and extensively applied the plan-do-check-act(PDCA)cycle theory.This approach was used to construct a precise,sustainable,and systematic closed-loop management system for laboratory safety hazards.The core value of this system lies in achieving full-cycle,dynamic governance of safety hazards and progressively enhancing the hazard management capabilities of university laboratories.[Methods]A four-dimensional dynamic management model was constructed to encompass strategy formulation,rectification implementation,inspection and supervision,and continuous improvement.This model established a hierarchically nested and collaboratively interconnected management network,ensuring comprehensive closed-loop management control of the four core elements of laboratory safety:personnel,materials,environment,and management.A target-oriented strategy was formulated in Stage P by identifying key hazards,conducting multidimensional root-cause analysis,establishing actionable rectification objectives,and systematically planning hazard identification and rectification strategies.This approach clarified the overall direction,defined key tasks,and outlined principles for resource allocation.In Stage D,precision in implementation was achieved through a graded early-warning mechanism that distinguished different risk levels and guided differentiated rectification measures.Thorough process tracing was mandated to identify root causes and assign explicit accountability,ensuring accurate corrective action and responsibility alignment.In Stage C,a dual-track mechanism integrating data-driven verification and supervision was established to monitor and validate the sustained effectiveness of hazard rectifications.In Stage A,effective practices that had been proven through implementation were systematically consolidated,and safety culture development was reinforced to elevate overall safety awareness and competence.Meanwhile,unresolved issues and newly emergent risks underwent in-depth root-cause analysis to support the optimization of rectification strategies,thereby driving the management system toward higher levels of refinement and adaptability.[Results]The implementation of a closed-loop management system for laboratory safety hazards based on the PDCA cycle theory yielded substantial outcomes:(1)The overall number of laboratory safety hazards markedly decreased,the occurrence rate of repeated hazards was substantially reduced,the upward trend of basic safety hazards was contained,and critical hazards were effectively controlled;(2)The capacity of individual laboratories to manage risk sources,identify and rectify safety hazards,and respond to emergencies was significantly enhanced,demonstrating a strengthened ability for autonomous safety prevention and control;(3)The closed-loop management system comprehensively encompassed the core elements of personnel,materials,environment,and management.It established 13 distinct and operable closed-loop pathways that ensured the substantive implementation,rigorous verification,and continuous tracking of corrective actions.[Conclusions]The implementation of the PDCA cycle management resulted in a substantial reduction in the overall incidence of safety hazards and the occurrence rate of repeated hazards.Additionally,it enhanced laboratories'capacity for self-identification,prevention,and continual improvement.Consequently,the effectiveness of closed-loop safety hazard management was substantially improved.The PDCA cycle management system provides a replicable tiered management framework and a precise rectification pathway,offering a valuable reference for improving laboratory safety management systems in universities.
[Objective]Nuclear science and technology is a strategic field that underpins national security,drives energy transition,and enables cutting-edge technological breakthroughs.Accelerating the development of world-class nuclear research institutions with clear positioning,flexible governance mechanisms,and efficient collaboration structures is crucial for strengthening national security,advancing energy technology transformation,and securing strategic technological leadership.Currently,China's major nuclear research platforms face several practical challenges,including rigid institutional mechanisms,underutilized innovation system efficiency,and insufficient collaborative innovation.This study provides theoretical support and practical guidance for China in establishing a modern nuclear research system tailored to national conditions and in achieving high-level self-reliance in nuclear science and technology.[Methods]This paper employs a comprehensive methodological approach integrating literature review,comparative analysis,case studies,and inductive reasoning.It systematically traces the developmental trajectory of the nuclear-related national laboratory system in the United States.By examining three core dimensions—governance architecture,contract management,and operational mechanisms—the study distills key practices in operational management.Drawing on the actual conditions of China's nuclear innovation platforms,targeted insights and recommendations are proposed across four dimensions:constructing a modern governance framework,optimizing resource allocation models,cultivating talent development ecosystems,and upgrading mechanisms for transforming scientific and technological achievements.[Results]The findings indicate that the development of nuclear laboratories in the United States has consistently aligned with national strategic needs,evolving from emergency-driven establishment to institutionalized development,and from single-function entities to comprehensive innovation enablers.This evolution can be divided into three phases:embryonic inception(World War Ⅱ era,1943-1946),institutionalization(Atomic Energy Commission era,1947-1974),and integration and expansion(Department of Energy era,1977-present).At present,the United States has formed an operational management system tailored to the nuclear sector's highly sensitive,heavily regulated,long-cycle,and security-critical characteristics.This system includes a government-owned,contractor-operated governance model and a full-cycle contract management framework adapted for nuclear regulation;a"dual-track"resource allocation model supporting nuclear capability development;and an open collaborative innovation mechanism that balances nuclear safety with operational efficiency.These systematic practices provide a valuable reference for similar institutions worldwide.China's nuclear laboratory development has entered a critical stage,requiring accelerated progress in institutionalization and the establishment of legal entities to build a modern governance structure with clearly defined responsibilities and accountability.Innovative investment and funding management models are needed to establish a mission-driven resource allocation system.Talent recruitment,cultivation,and incentive mechanisms should be further optimized to foster a globally competitive nuclear research ecosystem.In addition,an open and collaborative innovation environment should be developed,supported by a refined innovation system that effectively integrates research outputs with practical applications.[Conclusions]The success of nuclear laboratories in the United States lies in sustained institutional innovation,which has produced a hybrid organizational model that effectively fulfills national missions,safeguards academic freedom and scientific exploration,and ensures efficient operational capabilities.This model addresses three fundamental tensions:balancing national oversight with professional autonomy,reconciling long-term strategic objectives with short-term performance demands,and harmonizing security and confidentiality requirements with innovation and openness.In response to emerging challenges and strategic demands,China must develop a dynamic and modern nuclear science and technology innovation system that promotes frontier breakthroughs.Such a system should be firmly grounded in China's national conditions and the unique characteristics of the nuclear sector,while selectively and critically drawing on relevant United States experiences and practices.
[Objective]In materials science education,crystallography is a fundamental but challenging subject for undergraduate students.Traditional teaching methods often leave students confined to abstract theories—they may memorize lattice parameters and calculate interplanar spacings yet fail to establish an empirical linkage between microscopic crystal structures and macroscopic material properties.This"cognitive gap"severely hinders their understanding of the core principle that"structure determines properties."To address this issue,this study develops a comprehensive,semester-long experimental project centered on crystal structure engineering of vanadium-based cathodes for aqueous zinc-ion batteries(AZIBs).The project aims to transform cutting-edge research into a teachable investigative framework,thereby bridging theory and practice,enhancing students'quantitative structure-analysis skills,and cultivating their ability to solve complex materials-science problems.[Methods]The experiment follows a project-based learning model conducted over an entire semester and divided into four progressive stages.First,students learn the fundamentals of AZIBs,vanadium oxide cathodes,and crystallography,and then design a detailed experimental plan for Mn2+intercalation into VOH layered structures.Second,they synthesize two materials—pristine VOH and Mn-intercalated VOH(Mn-VOH)—via a hydrothermal method,followed by standard characterizations such as X-ray diffraction(XRD)and scanning electron microscopy.Third,students are introduced to the research-grade GSAS-II software and perform Rietveld full-pattern fitting refinement of the collected XRD data.They learn to establish structural models,refine parameters stepwise(e.g.,scale factor,background,zero shift,unit cell parameters,profile parameters,atomic coordinates,occupancy,and thermal factors),and extract quantitative crystallographic data(e.g.,lattice constants,cell volume,interlayer spacing).Fourth,students assemble coin cells,evaluate their electrochemical performance through cyclic voltammetry(CV),cycling stability,and electrochemical impedance spectroscopy(EIS),and finally integrate the refined structural parameters with the electrochemical data to prepare a comprehensive report demonstrating the structure-property correlation quantitatively.[Results]The designed experiment successfully converted an abstract concept into a tangible,data-driven inquiry.XRD patterns showed that the(001)interlayer spacing of VOH increased from 11.43 to 12.81 Å after Mn2+intercalation.Rietveld refinement(reliability factors Rwp<10%)quantitatively revealed that the c-axis lattice parameter expanded by 15.7%(from 11.13 to 12.88 Å),while the unit cell volume enlarged from 988.84 to 1 146.22 Å3.This structural evolution directly correlated with enhanced electrochemical performance.Specifically,the Mn-VOH cathode exhibited reduced polarization(small peak potential separation in CV curves),significantly improved cycling stability(negligible capacity decay after 3 000 cycles at 5 A g-1,compared with only 60.18%capacity retention for pristine VOH),and fast Zn2+diffusion kinetics,as evidenced by low charge-transfer resistance and a steep Warburg region in EIS measurements.Throughout the project,students not only mastered routine experimental skills,including hydrothermal synthesis,electrode preparation,and battery assembly,but also achieved a significant advancement in analytical capability—from merely"collecting XRD patterns"to"quantitatively solving crystal structures"using digital refinement tools.They directly observed how Mn2+pillars expand the interlayer space and stabilize the lattice,thereby justifying the improved electrochemical performance.[Conclusion]This teaching reform effectively bridges the gap between fundamental crystallography education and advanced materials research.By integrating a real scientific problem and state-of-the-art analytical techniques,such as GSAS-II-based Rietveld refinement,into a structured and extended experimental project,the approach fosters a deep understanding of structure-property correlations.The project provides a replicable model for"digital-integrated"practical teaching and helps cultivate the innovative thinking and problem-solving competencies essential for future materials scientists.
[Objective]Asphalt mixture is a widely used pavement material in road and airport engineering.The incorporation of fibers has been demonstrated to substantially enhance the performance of asphalt mixtures in road applications.Nevertheless,research on the uniaxial compressive performance of fiber-reinforced asphalt mixtures and the microscopic enhancement mechanism of fibers remains limited.[Methods]The basalt fiber-reinforced asphalt mixture is selected as the research object.Material tests on basalt fiber-reinforced asphalt mixtures,including the Marshall test and uniaxial compressive tests,are conducted to study the effect of basalt fiber content on the physical and mechanical properties of asphalt mixtures,such as bulk density,stability,flow value,and compressive strength.Based on discrete element simulation,a numerical model for basalt fiber-reinforced asphalt mixture is established,in which the fibers are modeled as clumps,aggregates with diameters larger than 2.36 mm are represented as balls,and the asphalt mortar,composed of aggregates smaller than 2.36 mm and base asphalt,is simulated using a contact model.The discrete element model is verified with the test results,and the contact parameters are calibrated.An investigation is conducted into the process of crack formation,development,and failure of the asphalt mixture under uniaxial compression.[Results]The results show that(1)with an increase in fiber content,there is a gradual decrease in bulk density and voids filled with asphalt,while the void content of the asphalt mixture,optimum asphalt binder content,and mineral aggregate voidage of the asphalt mixture increase.(2)The incorporation of basalt fibers significantly enhances the mechanical properties of asphalt mixtures,including Marshall stability,flow value,and compressive strength.In comparison with asphalt mixtures devoid of fiber,the uniaxial compressive strength increased by 13.2%,43.3%,and 8.3%at fiber content levels of 0.2%,0.3%,and 0.4%by weight,respectively.(3)Fiber content exerts a substantial influence on the axial compressive performance of asphalt mixtures.The maximum compressive strength value is observed at 0.3%fiber content for the asphalt mixtures examined in this study.When the basalt fiber content is below 0.6%,the compressive strength initially increases and then decreases with rising fiber content;however,it remains higher than that of mixtures devoid of fiber.In contrast,when the fiber content exceeds 0.6%,the compressive strength decreases below that of mixtures devoid of fibers.(4)The discrete element model of basalt fiber-reinforced asphalt mixtures accurately simulates the uniaxial compression process.[Conclusions]Microstructural analysis reveals that increasing basalt fiber content significantly reduces edge fragmentation and crack propagation in asphalt mixture specimens,while the number of interparticle contacts increases markedly.Analysis indicates that the discrete element model of asphalt mixtures developed using discrete element modeling software can accurately simulate the internal microscopic mechanisms during uniaxial compression,revealing the influence of basalt fibers on contact evolution and crack propagation within the mixture.The addition of an appropriate amount of basalt fibers enhances the physical and mechanical properties of asphalt mixtures.However,excessive fiber content may lead to fiber aggregation phenomena,resulting in performance degradation.Therefore,for practical engineering applications,the optimal fiber dosage should be determined through experimental testing and theoretical analysis based on specific conditions.The findings of this study have significant reference value for the design of fiber-asphalt mixture pavements and airport runways.
[Objective]Hongyancun Station on Chongqing Rail Transit Line 9 has a maximum burial depth of 106.37 m.It is a two-story underground mined station,with the concourse level on the first underground floor and the platform level on the second underground floor.The station has a total length of 262.3 m and a total width of 21.8 m.Adopting a single-arch double-layer structure,the tunnel features composite lining,with a net excavation width of 24.34 m,an excavation height of 21.23 m,and a cross-sectional area of 436 m2.The tunnel crown consists of strongly weathered silty mudstone classified as Grade IV surrounding rock,with rock mass integrity coefficients ranging from 0.70 to 0.78 and a saturated compressive strength of 10.5 MPa that exhibits obvious strength variability.The tunnel body is composed of moderately weathered silty mudstone classified as basic Grade III surrounding rock,with rock mass integrity coefficients ranging between 0.76 and 0.81 and a saturated compressive strength of 31.5 MPa.The arch cover method was adopted to overcome construction difficulties and ensure safety for the shallow-buried,super-large-section metro station in upper-soft and lower-hard rock strata.[Methods]Field monitoring was carried out by installing measuring points on the ground surface,tunnel vault,and both side walls to measure ground subsidence and tunnel deformation.The stress and deformation characteristics of the optimized arch cover construction method were then analyzed.[Results]The research results show that the arch cover construction method was optimized.Following the principle of"upper part prior to lower part,side parts prior to central part",the super-large-section excavation was divided into 11 parts.Large arch feet were utilized to bear upper loads and form an arch cover stress system,thereby eliminating stress concentration that occurred before the primary support of conventional large-section stations reached the base.The optimized sequential excavation and timely closure into a ring structure considerably reduced the temporary support workload,created space for mechanized operations during middle and lower bench construction,and enabled safe and rapid construction of super-large-section tunnels.This construction method fully mobilized the self-bearing capacity of the surrounding rock and lowered stress on the primary support.A single-pass excavation technology for the left and right pilot tunnels at the tunnel arch was also proposed.[Conclusions]Monitoring data indicate that during bench base excavation,the excavation sequence of central grooving and side wall surrounding rock exerts minor influences on ground subsidence,surrounding rock deformation,rock stress,and primary support safety.The innovative construction method has potential for wider application to similar engineering projects.
[Objective]Tunnels in high-intensity seismic regions are susceptible to severe damage from earthquakes,such as lining cracking,spalling,invert uplift,and progressive plastic deformation,which jeopardize structural safety and serviceability.Although seismic isolation layers have been widely applied to mitigate tunnel seismic responses,most studies have primarily focused on the interaction between the secondary lining and isolation layer,often overlooking the role of primary support,an essential component in tunnel construction.This study aims to elucidate the dynamic response characteristics and damage evolution of tunnel linings when seismic mitigation measures are implemented under realistic primary support conditions.By integrating primary support into the mitigation system,the study experimentally evaluates the effectiveness of a composite configuration comprising the secondary lining,seismic isolation layer,primary support,and surrounding rock,providing guidance for resilience-oriented tunnel seismic design.[Methods]The Jiedexiu No.2 Tunnel on the Lhasa-Nyingchi Railway was selected as a representative case for a series of shaking-table model tests.A gravity-distorted similarity model was developed based on the Buckingham π theorem,featuring a geometric similarity ratio of 1:40,alongside appropriate scaling of elastic modulus,density,displacement,and acceleration.The surrounding rock and overburden were simulated using Grade V phyllite and gravelly-breccia soils,respectively.The secondary lining was modeled with gypsum to simulate C30 concrete,whereas sponge rubber material was used as the seismic isolation layer.Basalt fiber-reinforced polymer anchors were employed to simulate rock bolts in the primary support.Horizontal excitation was applied using the El Centro earthquake wave with peak ground accelerations of 0.1 g,0.2 g,0.3 g,and 0.4 g.Acceleration sensors and strain gauges were arranged symmetrically to compare a conventional section with a mitigated section that incorporated the isolation layer and primary support.Dynamic responses were analyzed in the time and frequency domains,which included acceleration time histories,Fourier spectra,and acceleration response spectra.To quantify cumulative damage,a plastic deformation index(PDI),defined as the ratio of residual strain to peak dynamic strain,was introduced to classify damage evolution into elastic,elasto-plastic,and plastic stages.[Results]Results show that the seismic isolation layer significantly reduces the peak amplitudes of acceleration response spectra and Fourier spectra without altering their overall shapes.Under low-intensity excitation(0.1 g),the response spectra exhibit multipeak characteristics with a predominant period of approximately 0.06 s.As ground-motion intensity increases,the spectra transition to a single-peak pattern,accompanied by a lengthening of the predominant period to approximately 0.08 s,indicating enhanced system nonlinearity and amplification of low-frequency components.Dynamic strain measurements reveal that the mitigated section consistently experiences lower strain peaks and slower strain accumulation,particularly at the crown and invert,whereas the haunch exhibits the weakest mitigation effect due to strong boundary constraints.PDI analysis indicates that the conventional section enters a plastic-dominated state when excitation exceeds 0.3 g,whereas the mitigated section remains primarily in the elasto-plastic stage with substantially lower PDI values.Post-test observations confirm that damage in the mitigated section is markedly reduced compared to the conventional section.[Conclusions]Experimental results demonstrate that a seismic mitigation configuration that explicitly considers primary support and incorporates a seismic isolation layer can effectively improve tunnel seismic performance under high-intensity earthquake loading.This composite system reduces spectral amplitudes and plastic deformation demand while preserving the fundamental spectral characteristics of the lining response.The mitigation effect is most pronounced at the invert and crown,indicating that the haunch remains a critical area requiring additional design attention.The proposed approach provides a practical experimental basis for energy-dissipation-oriented seismic design and retrofitting of tunnels in earthquake-prone regions.
[Objective]With the increasing frequency and intensity of flood disasters,distribution networks are exposed to complex cascading failures caused by the strong coupling between power and transportation systems.However,conventional experimental pedagogy in electrical engineering focuses primarily on deterministic operation and single-system analysis,which limits students'understanding of disaster-induced risks,recovery processes,and resilience-oriented decision-making.This study aims to design an experimental teaching platform for enhancing the resilience of distribution networks in flood-disaster scenarios by explicitly considering power-transportation coordination.The goal is to support interdisciplinary learning and improve students'ability to analyze and manage complex coupled systems under extreme conditions.[Methods]An integrated experimental platform is developed by coupling distribution and transportation networks with emergency resource systems within a unified modeling framework.The evolution of flood disasters is first described using a grid-based hydrological model,which captures the spatial and temporal accumulation of surface water.The simulated flooding depth is then mapped to the probability of failure of distribution nodes and degradation of road traffic,enabling the construction of a coupled power-transportation failure model.Emergency resources,including repair crews and mobile energy storage systems(MESS),are incorporated to represent both structural repair and temporary power supply capabilities.To reflect uncertainty in the impact of disasters,Monte Carlo simulation is employed to generate multiple failure scenarios.By implementing scenario clustering,the computational burden is reduced while preserving representative and high-risk characteristics.Based on these scenarios,a two-stage emergency scheduling framework is established,in which pre-disaster resource allocation and post-disaster dynamic dispatch decisions are jointly optimized.A risk-aware strategy based on conditional value-at-risk(CVaR)is introduced to emphasize low-probability but high-impact scenarios.The platform enables comparative experimental analysis by varying key dimensions,including the consideration of road flooding effects and the number of MESS units and repair crews deployed.[Results]The simulation results under different flood disaster scenarios demonstrate that the proposed platform effectively captures the influence of cross-system coupling on the resilience of the distribution network.When road flooding and traffic degradation are considered,the arrival of emergency resources is delayed,and the power restoration process is significantly slowed,leading to larger resilience loss.Increasing the number of MESS units improves early-stage power supply by providing temporary support to critical loads,which helps mitigate initial service interruptions.In contrast,increasing the number of repair crews mainly accelerates mid-and late-stage structural recovery,enabling the system to reach full restoration earlier.The best overall performance is achieved through the coordinated deployment of repair crews and MESS,combining early power support with faster recovery.Moreover,the CVaR-based scheduling strategy provides enhanced robustness by prioritizing high-impact disaster scenarios,resulting in more stable recovery trajectories across different scenarios.These results clearly illustrate the complementary roles of transportation conditions,emergency resources,and risk-aware decision-making in resilience enhancement.[Conclusions]The proposed experimental teaching platform integrates flood-disaster modeling,coupled power-transportation failure analysis,and risk-aware emergency scheduling into a coherent framework.It transforms abstract concepts of system resilience into observable experimental phenomena,enabling students to intuitively understand how disaster evolution,transportation accessibility,and resource coordination jointly affect the recovery of distribution networks.The platform effectively supports comparative experiments and decision analysis in uncertainty,fostering interdisciplinary thinking and resilience-oriented engineering skills.This study provides a practical reference for advancing experimental teaching reform in electrical engineering and cultivating students'ability to analyze and manage complex coupled energy systems in the event of extreme events.
