Compared with conventional bulk piezoelectric ultrasonic transducers,Piezoelectric Micromachined Ultrasonic Transducers(PMUTs)play an increasingly important role in many fields due to their advantages such as small size,low power consumption,flexible frequency control,and ease of integration with CMOS processes.As application demands continue to grow,related fields have also posed new challenges to key performance metrics of PMUTs,such as bandwidth and sensitivity.The article first introduces the device structure and working principles of PMUTs.Then,based on the cavity fabrication methods,it reviews the process schemes for PMUT fabrication,including etching,sacrificial layer release,surface bonding,and silicon migration methods,along with their respective advantages and limitations.Subsequently,the research status of performance optimization strategies for PMUTs is systematically elaborated from four perspectives:piezoelectric materials,device structure,electrode excitation,and acoustic matching.Specifically,aiming for high piezoelectric coefficients and low dielectric constants,the performance of piezoelectric materials is enhanced through doping,modification,and process optimization.The PMUT structure is improved for better performance using strategies such as vibration mode control,residual stress management,acoustic structure integration,and array layout optimization.By designing the static layout of PMUT electrodes and dynamically controlling excitation signal parameters,effective regulation of resonant frequency,bandwidth,and sensitivity is achieved.In addition,acoustic matching and packaging of PMUTs using external materials or structures are discussed to further improve the acoustic performance of PMUTs.Finally,this paper discusses the major challenges and development trends in PMUT fabrication and performance optimization,providing a reference for future research and practical applications of PMUTs.
Against the backdrop of global carbon peaking and carbon neutrality("Dual Carbon")goals,recycled asphalt pavement(RAP)has emerged as a core pathway for low-carbon development in the road construction sector by reducing carbon emissions associated with raw material extraction and production.Current carbon emission assessments for RAP mostly adopt full life cycle assessment(LCA)models;nevertheless,this methodological framework continues to face challenges including the absence of standardized data protocols,inadequate dynamic responsiveness,and insufficient research on parameter coupling mechanisms.This paper systematically reviews the research progress of LCA applications in this field,which has evolved through three developmental stages:localized accounting,multi-dimensional integration,and dynamic optimization.And it emphasizes the necessity of context-specific emission categorization in accordance with the IPCC Guidelines for National Greenhouse Gas Inventories to enhance accounting adaptability.This study further analyzes the influences of raw material characteristics and RAP content,recycling process types,technological parameters,construction process parameters,and external factors on carbon emissions.Key parametric thresholds are identified,including an optimal RAP content of 60%～80%,and a recommended warm-mix application temperature of 120～130℃.Persistent challenges such as deficient emission baseline data and discrepancies among LCA software outputs are also addressed.Moreover,a hierarchical analysis of influencing factors reveals that recycling process type exerts the highest weight(35%～40%),followed by RAP content(25%～30%).Notably,the emission reduction benefits exhibit pronounced diminishing marginal returns;RAP contents exceeding 80%induce carbon emission rebound effects attributable to degraded pavement performance and increased rejuvenator dosage.Multi-parameter coupling manifests both"synergistic gains"and"offsetting losses":synergistic emission reduction advantages—achieving comprehensive reduction rates of 30%～55%—are observed when high RAP content is combined with in-place cold recycling or when bio-oil rejuvenators are paired with high-efficiency equipment.In contrast,benefit offsetting arises from combinations such as high RAP content coupled with plant-mix hot recycling or cold recycling processes in tropical regions,substantially attenuating the overall emission reduction efficacy.Comprehensive analysis demonstrates that recycled asphalt pavements offer significant advantages in resource circulation and emission reduction.Recycling technology selection,RAP replacement ratio,and construction temperature act as the core governing variables for full-life-cycle carbon emissions,while regional resource endowments,construction equipment configuration,and rejuvenator category serve as critical auxiliary regulating factors.
In the long-term operation of the mechanistic model of a central air conditioning system,key physical pa-rameters may drift due to practical factors such as pipe scaling and equipment aging.this leads to a gradual decline in model accuracy and severely affecting the performance of model-based optimal control strategies.To ensure the long-term reliability of the model and the effectiveness of the control strategy,it is essential to conduct an in-depth perfor-mance analysis of the constructed physical model and perform effective parameter identification based on actual opera-tional data.Building upon the mechanistic model of the central air conditioning system,a sensitivity analysis is per-formed to extract a set of key parameters that significantly influence system performance.An optimization function is es-tablished,aiming to minimize the total error of target variables such as chilled water inlet and outlet temperatures and e-nergy efficiency ratio(EER).The parameter identification problem is transformed into an optimization problem,which is solved using an approximate optimization method.The identification method is validated using actual engineering data.Results demonstrate that this method significantly reduces errors in target variables.Taking EER as an example,before identification,the mean absolute error(MAE)is 1.93,the root mean square error(RMSE)is 1.98,and the Mean abso-lute percentage error(MAPE)reaches as high as 61.4%.After identification,under single-target identification,the MAE drops to 0.008,RMSE to 0.004,and MAPE to 0.29%.Under multi-target identification,the MAE decreases to 0.28,RMSE to 0.51,and MAPE to 9.74%.The validation results confirm the method's effectiveness in improving model accuracy and engineering applicability.
To address the issue of insufficient universality in current partial discharge signal denoising metho-ds,a method based on variational mode decomposition(VMD)is proposed,which achieves adaptive parameter optimization and incorporates discrete wavelet transform(DWT)for denoising.To achieve adaptivity for the method,an optimization model for the best parameter of VDM is established,which adopts a combined"soft threshold"and"hard threshold"approach for kurtosis screening and takes the minimization that differs the de-noised signal from the original signal as the objective function,and the Northern Goshawk Optimization algo-rithm is applied to realize the optimization.The effectiveness of the method is verified through tests on both simulated data and experimental data.The simulated data encompass partial discharge pulses of four typical waveform expressions as well as strong interference consisting of white noise and two narrowband interfe-rences.After processing by the proposed method,the signal-to-noise ratio is improved from-18.46 dB to 12.54 dB.The experimental data are acquired using a partial discharge ultrasonic signal acquisition device developed by the authors in scenarios with high environmental similarity to actual engineering noise condi-tions,which can well reflect on-site engineering interference and hardware circuit noise interference.The par-tial discharges are generated by true-type defects,avoiding deviations from actual insulation defects that may occur with standard partial discharge specimens.After processing by the proposed method,the acquired data achieve a signal-to-noise ratio improvement of 38.19 dB and a noise suppression ratio of 9.88 dB,and yield waveforms with distinct features that are consistent with the typical partial discharge signal features of the de-fect.The effectiveness,necessity,and performance advantages of each step of the proposed method are vali-dated through comparative analysis with the processing results of DWT,EMD(Empirical Mode Decomposition)-DWT,and PSO(Particle Swarm Optimization)-VMD-DWT algorithms.
Planning and deploying unmanned aerial vehicle(UAV)takeoff and landing sites in areas with concentrated hazardous material transportation routes,combined with UAV-assisted joint emergency rescue operations alongside ground-based emergency rescue stations,can significantly enhance the emergency response capabilities for such accidents.This paper investigates the location and allocation problem of UAV landing sites for auxiliary rescue in hazmat transportation accidents.First,a location-allocation model for UAV landing sites under deterministic scenarios was constructed.Second,considering the uncertainty of potential risks along road network segments,distributionally robust optimization(DRO)theory was introduced,and a robust location-allocation optimization model for UAV landing sites was established.Third,an easily solvable approximation method was adopted.Under the constraint of a zero-mean bounded perturbation ambiguity set,the original distributionally robust optimization model was equivalently transformed into an integer programming model,which was then solved using a branch-and-cut algorithm based on Benders decomposition.Finally,numerical examples were conducted to verify the effectiveness of the proposed models and solution algorithm.The results indicate that although the output of the distributionally robust optimization model tends to be relatively conservative,it exhibits stronger robustness and can effectively enhance the overall reliability of the emergency rescue system.The coverage effectiveness of UAV landing sites increases with the number of planned takeoff and landing points,generally following a pattern of diminishing marginal returns.Under conditions where probability distribution information of uncertain parameters is incomplete,the distributionally robust optimization approach outperforms traditional stochastic programming methods.Compared with conventional robust optimization,distributionally robust optimization can incorporate prior information on partial probability distributions,thereby effectively avoiding the issue of overly conservative location-allocation solutions.
