The global energy landscape is undergoing a profound transformation,with wind energy,especially wind power,gaining increasing prominence due to its clean,renewable nature.However,as the installed capacity of wind powe...The global energy landscape is undergoing a profound transformation,with wind energy,especially wind power,gaining increasing prominence due to its clean,renewable nature.However,as the installed capacity of wind power continues to expand,the disposal of waste wind turbine blades(WWTB)has emerged as a significant challenge.These blades are predominantly composed of epoxy resin(EP)polymers,carbon fibers(CFs),and glass fibers(GFs).Improper disposal not only exacerbates environmental concerns but also leads to the loss of valuable resources,particularly carbon-based materials.Pyrolysis technology,a versatile and environmentally sustainable method for resource recovery,has garnered considerable attention in the context of WWTB disposal.This work presents a comprehensive review of the pyrolytic recycling of WWTB,focusing on the principles and classifications of pyrolysis technology,key factors influencing the pyrolysis process,as well as the pyrolysis methods,equipment,products,and their applications.Through an in-depth analysis of the current research on the pyrolytic recycling of WWTB,this review identifies critical unresolved issues in the field and provides a forward-looking perspective on emerging research trends.展开更多
The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbi...The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbine,developed and installed by China Three Gorges Corp.(CTG),is located in the Phase II Liuao offshore wind farm,more than 30 km off the coast of Fujian in waters deeper than 40 metres.The 20-mw unit successfully completed commissioning and started operation on 5 February,CTG announced.展开更多
Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-...Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.展开更多
Paying an additional RMB 2 could have your next milk tea delivered by drone to your balcony in just five minutes.This small fee represents the vast potential of the trillion-yuan lowaltitude economy.
Cyperus esculentus(C.esculentus),a desert-adapted plant species with both ecological and economic value,has been widely cultivated in northern China's sandy regions.However,limited studies have investigated the pe...Cyperus esculentus(C.esculentus),a desert-adapted plant species with both ecological and economic value,has been widely cultivated in northern China's sandy regions.However,limited studies have investigated the performance of composite shelterbelts that integrate C.esculentus.This study systematically evaluated five shelterbelt models—Populus euphratica(P.euphratica),P.euphratica–C.esculentus composite,P.euphratica–nylon net–C.esculentus composite,Tamarix chinensis(T.chinensis),and T.chinensis–C.esculentus composite—using wind tunnel experiments and field observations.Sediment flux was measured at a normalized downwind distance(x/h)of 5,where x refers to the distance from the front edge(upwind side)of the shelterbelt for upwind measurements,and the distance from the rear edge(downwind side)for downwind measurements,and h represents the canopy height.Wind velocity was measured at x/h of–2,–1,1,2,3,5,and 7,and sand flux was measured at x/h=5,under initial wind velocities of 8.0 and 12.0 m/s.The results indicated that the P.euphratica–nylon net–C.esculentus composite was the most effective in reducing wind velocity,followed by the P.euphratica–C.esculentus composite.In contrast,the P.euphratica and T.chinensis exhibited relatively weaker wind reduction capabilities.Regarding sand flux,under moderate wind velocity(8.0 m/s),both the P.euphratica–C.esculentus composite and P.euphratica–nylon net–C.esculentus composite demonstrated the lowest sand flux values.However,under high wind velocity(12.0 m/s),the P.euphratica–nylon net–C.esculentus composite significantly outperformed the other shelterbelt models in sand retention,highlighting its superior windbreak and sand fixation efficacy.Field observations further validated the windbreak and sand fixation effects of C.esculentus.Comparisons between the bare sand plot and C.esculentus plot within protective forests demonstrated that planting C.esculentus can provide substantial ecological benefits in windbreak and sand-fixation.These findings,reinforced by field observations,strengthen the wind tunnel experiment results and highlight the critical role of C.esculentus in enhancing the performance of composite shelterbelts for desert ecological restoration.展开更多
Wind disturbance has emerged as a potential eco-friendly method for seedling cultivation.In this study,an electromechanical device was designed and built to investigate the effects of airflow on the micro-environment ...Wind disturbance has emerged as a potential eco-friendly method for seedling cultivation.In this study,an electromechanical device was designed and built to investigate the effects of airflow on the micro-environment and physiological activities of tomato seedlings in seedbeds by controlled experiments.The results indicated that airflow could enhance CO_(2) concentration near the seedling canopy,accelerate water evaporation from the seedling substrate,and reduce fluctuations in the temperature and humidity in microclimate.The photosynthetic rates of leaves at the 4th,7th,and 10th positions in seedlings subjected to airflow increased by 25.04%,8.23%,and 8.47%,respectively,whereas the transpiration rates increased by 15.59%,22.28%,and 13.26%,respectively when compared to the control group.Additionally,the strong seedling index of seedlings treated with airflow and exogenous iron element increased by 26.02%and 31.5%,respectively.Compared to seedlings treated with exogenous iron element,the geometric mean diameter of the pith tissue cells in the stems of seedlings subjected to airflow disturbance was reduced by approximately 18.66%,while the elastic modulus and bending strength of the stems increased by 10.01%and 5.89%,respectively.Similarly,the volume of root tissue cells decreased by 19.22%,but the elastic modulus of the roots increased by 6.46%.This study confirms that airflow significantly enhances seedling resilience to abiotic stress,yielding similar or better outcomes than exogenous iron application.It provides both theoretical and practical support for using airflow disturbance as a green technology for cultivating robust seedlings.展开更多
Ice crystal icing is an important cause of accidents in aircraft engines.Ice formation in aircraft engines can cause internal blades to freeze,affecting the quality of the air flow field and blocking the flow path.On ...Ice crystal icing is an important cause of accidents in aircraft engines.Ice formation in aircraft engines can cause internal blades to freeze,affecting the quality of the air flow field and blocking the flow path.On the other hand,the entry of ice crystal particles into the combustion chamber can cause a decrease in temperature or even flameout,leading to engine surge or shutdown.Therefore,it is necessary to conduct multiphase flow tests on ice crystals for aircraft components such as aircraft engines.Conducting ice crystal multiphase flow tests on aircraft is an effective research method,but it requires the construction of an ice crystal multiphase flow test platform that meets relevant technical requirements.The paper focuses on the relevant experimental requirements and combines wind tunnel test structures to conduct multiphase flow numerical simulations on various forms of jet pipelines,obtaining particle motion distribution results.After comparison,the optimal form of jet structure is obtained,providing the best selection scheme for the design of relevant wind tunnel structures.展开更多
In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A fi...In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.展开更多
This study investigates the vertical variations of aerosol size distribution(0.06-1??m)and cloud condensation nuclei(CCN)spectra over the Southern Ocean(SO)using aircraft observations from the SOCRATES campaign.Result...This study investigates the vertical variations of aerosol size distribution(0.06-1??m)and cloud condensation nuclei(CCN)spectra over the Southern Ocean(SO)using aircraft observations from the SOCRATES campaign.Results reveal a bimodal aerosol size distribution within the marine boundary layer(MBL),with peaks at diameters of~0.06??m and~0.65??m,dominated by sea-salt particles.Accumulation-mode aerosol concentrations decrease with altitude within the MBL,while Aitken-mode aerosol concentrations peak above the MBL(~2-3 km).Wind speed strongly correlates with coarse-mode aerosol concentration(R~2=0.77),implicating sea spray as a major CCN source at low supersaturations(SS=0.1%).The altitudes of CCN concentration peaks shift from the MBL(<1 km,SS<0.4%)to the free troposphere(~2.5 km,SS>0.4%),suggesting new particle formation aloft,distinct from sea surface sources.These findings highlight the unique aerosol-CCN dynamics in the pristine SO,offering critical constraints for models simulating cloud-aerosol interactions in preindustrial-like environments.展开更多
Predicting the precise impacts of climate change on extreme winds remains challenging,yet strong storms are widely expected to occur more frequently in a warming climate.Wind barriers are commonly used on bridges to r...Predicting the precise impacts of climate change on extreme winds remains challenging,yet strong storms are widely expected to occur more frequently in a warming climate.Wind barriers are commonly used on bridges to reduce aerodynamic loads on trains through blocking effects.This study develops a novel wind barrier based on Tesla valves,which not only blocks incoming flow but also dissipates mechanical energy through fluid collision.To demonstrate this energy-dissipation capability,a Tesla plate is placed in a circular duct to examine its influence on pressure drop.Experimental tests and numerical simulations comparing a Tesla channel and a straight channel of equal porosity show that the Tesla configuration produces a substantially higher pressure drop.Validated simulations are then used to conduct a parametric study to optimize the design.By varying the channel ratio,diversion angle,number of dissipation units,and porosity,velocity–pressure-drop relationships for different Tesla plates are obtained.The results show that larger channel ratios,larger diversion angles,and more dissipation units,combined with lower porosity,all increase pressure drop and thus enhance energy dissipation.Finally,the aerodynamic coefficients of a high-speed train on a bridge deck equipped with a Tesla-type barrier are evaluated and compared with those for a traditional straight-channel barrier.The Tesla-type barrier reduces the train’s lateral force coefficient to only 15%–25%of that produced by the traditional barrier,and it generates an additional stabilizing force that further improves running safety.展开更多
Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly deve...Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly develops and deployment increases,these impacts are becoming increasingly evident.A comprehensive understanding of these impacts is crucial for sustainable development.Based on the harmonization of previous detailed life cycle assessment(LCA)studies,this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power.Using this simplified LCA model,we assess the global warming potential(GWP),acidification potential(AP),and cumulative energy demand(CED)of wind power at the regional scale for 2022 and under three future scenarios(high-power wind turbine promotion,reduced wind curtailment,and a comprehensive development scenario).The results indicate that in 2022,the life cycle GWP,AP,and CED of wind power in western China were 10.76 g CO_(2) eq/kWh,0.177 g SO_(2) eq/kWh,and 17.6 kJ/kWh,respectively.Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia,Gansu,Qinghai,Ningxia,and Xinjiang,producing average decreases of 8.64%in GWP,8.39%in AP,and 9.26%in CED.In contrast,for Guangxi,Chongqing,Sichuan,Guizhou,Yunnan,Xizang,and Shaanxi,the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment,producing average decreases of 3.45%,3.09%,and 4.29%in GWP,AP,and CED,respectively.These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.展开更多
Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously o...Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.展开更多
This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shea...This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.展开更多
Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware los...Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
Unmanned combat aerial vehicles require lightweight,stealth-capable exhaust systems.However,traditional metallic nozzles increase radar detectability and reduce range,while advanced composites offer high performance b...Unmanned combat aerial vehicles require lightweight,stealth-capable exhaust systems.However,traditional metallic nozzles increase radar detectability and reduce range,while advanced composites offer high performance but are expensive.Therefore,to improve the operational range and survivability of unmanned combat aerial vehicles,a lightweight,high-temperature-resistant,oxidation-resistant,and low-observable composite exhaust nozzle is developed to replace conventional metallic straight-type nozzles.The nozzle features a double serpentine shape to reduce radar and infrared signatures and is manufactured as a monolithic structure using the filament winding process,accommodating the complex geometry and large size(length:1.8 m,width:0.8 m).The exhaust nozzle consists of a ceramic matrix composite made of silicon carbide fibers and a silicon oxycarbide matrix,which absorbs and scatters radio frequency signals while withstanding prolonged exposure to high-temperature(700℃)oxidizing environments typical of engine exhaust gases.The polysiloxane resin used to produce the silicon oxycarbide matrix poses significant challenges owing to its low tackiness and high viscosity variations depending on the presence of nanoparticles,making filament winding difficult.These challenges are addressed by optimizing resin viscosity and winding pattern design.As a result,the tensile strength of the composite specimens fabricated with the optimized viscosity increases by 228.03% before pyrolysis and 97.68%after pyrolysis,compared with that of the non-optimized specimens.In addition,the density and tensile strength of the composite processed via three cycles of polymer infiltration and pyrolysis increased by 13.08% and 80.37%,respectively,compared to those of the non-densified composite.High-temperature oxidation and flame tests demonstrate exceptional thermal and oxidative stability.Furthermore,when compared with carbon fiber-reinforced ceramic matrix composites,the developed composite exhibits a permittivity at least two levels lower and a reflection loss below7 dB within the frequency range of 9.3-10.9 GHz,underscoring its superior electromagnetic stealth performance.展开更多
Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operati...Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.展开更多
Water-cooled system have significantly enhanced the power generation efficiency of offshore wind turbines.However,these innovative systems are susceptible to substantial biological fouling,maintenance challenges,and h...Water-cooled system have significantly enhanced the power generation efficiency of offshore wind turbines.However,these innovative systems are susceptible to substantial biological fouling,maintenance challenges,and high upkeep costs.Therefore,the development of a specialized front-end filter tailored for direct current water-cooled system is importance.This involves the integration of dimensionally stable anode(DSA)and nickel alloy cathode,valued for their corrosion resistance in seawater,into a novel front-end filter system for Water-cooled applications.This system has the dual capability of generating hydrogen and chlorine for self-cleaning purposes.Implementing a flushing pulse electrolysis mode,it effectively mitigates electrode failure induced by cathodic calcium and magnesium deposition,thereby significantly prolonging electrode lifespan.Laboratory tests comprising system assembly and performance evaluations were conducted,with the system programmed to operate for 5 minutes every 24 hours under continuous flushing by natural seawater to simulate real-world conditions.After more than 11 months of continuous flushing,observations reveal that the DSA mesh and nickel alloy mesh maintain intact structural integrity and normal functioning.Subsequent 1꞉1 physical prototype Sea trial further validated the soundness of the system design and electrolytic control parameters.展开更多
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce r...Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce random multi-dimensional vibration with different characteristics,which makes it impossible to obtain accurate and efficient aerodynamic data.Therefore,in order to ensure the reliable and efficient conduction of wind tunnel test,a wind-tunnel-modeladaptive vibration control method is proposed in this paper.First,the split type adaptive vibration suppression structure is designed.Second,the multi-dimensional vibration characteristic characterization method is derived and the vibration characteristic identification method of the system is designed.Then,a vibration state estimation model is established according to the identification results of vibration characteristics,and a multi-actuator cooperative control method based on vibration state estimation is constructed.Finally,a model-adaptive vibration control system is built,and vibration characteristics identification and hammer experiments are carried out for two types of typical models.The results show that the proposed model-adaptive vibration control method increases the equivalent damping ratio of pitch and yaw dimensions of the high-aspect-ratio class model by 8.19 times and 48.81 times,respectively.The equivalent damping ratio of pitch and yaw dimensions of the highslenderness-ratio class model is increased by 16.44 and 5.43 times,respectively.It provides a strong guarantee for the reliable and efficient development of multi-type wind tunnel test tasks.展开更多
基金Supported by the National Natural Science Foundation of China(22468035,22468036,22368038,22308048)the Natural Science Foundation of Inner Mongolia(2024QN02018,2025MS02030)+2 种基金First-class Discipline Research Special Project of Inner Mongolia(YLXKZX-NGD-045)Inner Mongolia Autonomous Region Postgraduate Research Innovation Project(KC2024047B)Research Foundation for Introducing High-level Talents in Inner Mongolia Autonomous Region。
文摘The global energy landscape is undergoing a profound transformation,with wind energy,especially wind power,gaining increasing prominence due to its clean,renewable nature.However,as the installed capacity of wind power continues to expand,the disposal of waste wind turbine blades(WWTB)has emerged as a significant challenge.These blades are predominantly composed of epoxy resin(EP)polymers,carbon fibers(CFs),and glass fibers(GFs).Improper disposal not only exacerbates environmental concerns but also leads to the loss of valuable resources,particularly carbon-based materials.Pyrolysis technology,a versatile and environmentally sustainable method for resource recovery,has garnered considerable attention in the context of WWTB disposal.This work presents a comprehensive review of the pyrolytic recycling of WWTB,focusing on the principles and classifications of pyrolysis technology,key factors influencing the pyrolysis process,as well as the pyrolysis methods,equipment,products,and their applications.Through an in-depth analysis of the current research on the pyrolytic recycling of WWTB,this review identifies critical unresolved issues in the field and provides a forward-looking perspective on emerging research trends.
文摘The world’s most powerful offshore wind turbine has begun feeding electricity into the grid off the coast of southeast China,marking a major technological leap in the country’s wind power industry.The colossal turbine,developed and installed by China Three Gorges Corp.(CTG),is located in the Phase II Liuao offshore wind farm,more than 30 km off the coast of Fujian in waters deeper than 40 metres.The 20-mw unit successfully completed commissioning and started operation on 5 February,CTG announced.
文摘Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.
文摘Paying an additional RMB 2 could have your next milk tea delivered by drone to your balcony in just five minutes.This small fee represents the vast potential of the trillion-yuan lowaltitude economy.
基金supported by the Xinjiang Key Research and Development Programme Project(2022B02040-2)the Tianshan Yingcai Program of Xinjiang Uygur Autonomous Region(2024TSYCLJ0028).
文摘Cyperus esculentus(C.esculentus),a desert-adapted plant species with both ecological and economic value,has been widely cultivated in northern China's sandy regions.However,limited studies have investigated the performance of composite shelterbelts that integrate C.esculentus.This study systematically evaluated five shelterbelt models—Populus euphratica(P.euphratica),P.euphratica–C.esculentus composite,P.euphratica–nylon net–C.esculentus composite,Tamarix chinensis(T.chinensis),and T.chinensis–C.esculentus composite—using wind tunnel experiments and field observations.Sediment flux was measured at a normalized downwind distance(x/h)of 5,where x refers to the distance from the front edge(upwind side)of the shelterbelt for upwind measurements,and the distance from the rear edge(downwind side)for downwind measurements,and h represents the canopy height.Wind velocity was measured at x/h of–2,–1,1,2,3,5,and 7,and sand flux was measured at x/h=5,under initial wind velocities of 8.0 and 12.0 m/s.The results indicated that the P.euphratica–nylon net–C.esculentus composite was the most effective in reducing wind velocity,followed by the P.euphratica–C.esculentus composite.In contrast,the P.euphratica and T.chinensis exhibited relatively weaker wind reduction capabilities.Regarding sand flux,under moderate wind velocity(8.0 m/s),both the P.euphratica–C.esculentus composite and P.euphratica–nylon net–C.esculentus composite demonstrated the lowest sand flux values.However,under high wind velocity(12.0 m/s),the P.euphratica–nylon net–C.esculentus composite significantly outperformed the other shelterbelt models in sand retention,highlighting its superior windbreak and sand fixation efficacy.Field observations further validated the windbreak and sand fixation effects of C.esculentus.Comparisons between the bare sand plot and C.esculentus plot within protective forests demonstrated that planting C.esculentus can provide substantial ecological benefits in windbreak and sand-fixation.These findings,reinforced by field observations,strengthen the wind tunnel experiment results and highlight the critical role of C.esculentus in enhancing the performance of composite shelterbelts for desert ecological restoration.
基金supported by an International Cooperation Key Plan of Shaanxi Province(Grant No.2022KWZ-12)an Agricultural Science Innovation and Transformation Project of Shaanxi Province[Grant No.NYKJ-2022-YL(XN)12]a High-End Foreign Expert Recruitment Program(Grant No.G2022172006L).
文摘Wind disturbance has emerged as a potential eco-friendly method for seedling cultivation.In this study,an electromechanical device was designed and built to investigate the effects of airflow on the micro-environment and physiological activities of tomato seedlings in seedbeds by controlled experiments.The results indicated that airflow could enhance CO_(2) concentration near the seedling canopy,accelerate water evaporation from the seedling substrate,and reduce fluctuations in the temperature and humidity in microclimate.The photosynthetic rates of leaves at the 4th,7th,and 10th positions in seedlings subjected to airflow increased by 25.04%,8.23%,and 8.47%,respectively,whereas the transpiration rates increased by 15.59%,22.28%,and 13.26%,respectively when compared to the control group.Additionally,the strong seedling index of seedlings treated with airflow and exogenous iron element increased by 26.02%and 31.5%,respectively.Compared to seedlings treated with exogenous iron element,the geometric mean diameter of the pith tissue cells in the stems of seedlings subjected to airflow disturbance was reduced by approximately 18.66%,while the elastic modulus and bending strength of the stems increased by 10.01%and 5.89%,respectively.Similarly,the volume of root tissue cells decreased by 19.22%,but the elastic modulus of the roots increased by 6.46%.This study confirms that airflow significantly enhances seedling resilience to abiotic stress,yielding similar or better outcomes than exogenous iron application.It provides both theoretical and practical support for using airflow disturbance as a green technology for cultivating robust seedlings.
文摘Ice crystal icing is an important cause of accidents in aircraft engines.Ice formation in aircraft engines can cause internal blades to freeze,affecting the quality of the air flow field and blocking the flow path.On the other hand,the entry of ice crystal particles into the combustion chamber can cause a decrease in temperature or even flameout,leading to engine surge or shutdown.Therefore,it is necessary to conduct multiphase flow tests on ice crystals for aircraft components such as aircraft engines.Conducting ice crystal multiphase flow tests on aircraft is an effective research method,but it requires the construction of an ice crystal multiphase flow test platform that meets relevant technical requirements.The paper focuses on the relevant experimental requirements and combines wind tunnel test structures to conduct multiphase flow numerical simulations on various forms of jet pipelines,obtaining particle motion distribution results.After comparison,the optimal form of jet structure is obtained,providing the best selection scheme for the design of relevant wind tunnel structures.
文摘In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.42430601,42175087)the Science and Technology Project of Gansu Province(Outstanding Youth Fund,Grant No.24JRRA386)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2024-jdzx04)。
文摘This study investigates the vertical variations of aerosol size distribution(0.06-1??m)and cloud condensation nuclei(CCN)spectra over the Southern Ocean(SO)using aircraft observations from the SOCRATES campaign.Results reveal a bimodal aerosol size distribution within the marine boundary layer(MBL),with peaks at diameters of~0.06??m and~0.65??m,dominated by sea-salt particles.Accumulation-mode aerosol concentrations decrease with altitude within the MBL,while Aitken-mode aerosol concentrations peak above the MBL(~2-3 km).Wind speed strongly correlates with coarse-mode aerosol concentration(R~2=0.77),implicating sea spray as a major CCN source at low supersaturations(SS=0.1%).The altitudes of CCN concentration peaks shift from the MBL(<1 km,SS<0.4%)to the free troposphere(~2.5 km,SS>0.4%),suggesting new particle formation aloft,distinct from sea surface sources.These findings highlight the unique aerosol-CCN dynamics in the pristine SO,offering critical constraints for models simulating cloud-aerosol interactions in preindustrial-like environments.
基金supported by the National Natural Science Foundation of China(52475105)Special Fund for Science and Technology Innovation of Jiangsu Province(BE2022610)National Natural Science Foundation of China(U23A20661).
文摘Predicting the precise impacts of climate change on extreme winds remains challenging,yet strong storms are widely expected to occur more frequently in a warming climate.Wind barriers are commonly used on bridges to reduce aerodynamic loads on trains through blocking effects.This study develops a novel wind barrier based on Tesla valves,which not only blocks incoming flow but also dissipates mechanical energy through fluid collision.To demonstrate this energy-dissipation capability,a Tesla plate is placed in a circular duct to examine its influence on pressure drop.Experimental tests and numerical simulations comparing a Tesla channel and a straight channel of equal porosity show that the Tesla configuration produces a substantially higher pressure drop.Validated simulations are then used to conduct a parametric study to optimize the design.By varying the channel ratio,diversion angle,number of dissipation units,and porosity,velocity–pressure-drop relationships for different Tesla plates are obtained.The results show that larger channel ratios,larger diversion angles,and more dissipation units,combined with lower porosity,all increase pressure drop and thus enhance energy dissipation.Finally,the aerodynamic coefficients of a high-speed train on a bridge deck equipped with a Tesla-type barrier are evaluated and compared with those for a traditional straight-channel barrier.The Tesla-type barrier reduces the train’s lateral force coefficient to only 15%–25%of that produced by the traditional barrier,and it generates an additional stabilizing force that further improves running safety.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFF1303405).
文摘Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly develops and deployment increases,these impacts are becoming increasingly evident.A comprehensive understanding of these impacts is crucial for sustainable development.Based on the harmonization of previous detailed life cycle assessment(LCA)studies,this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power.Using this simplified LCA model,we assess the global warming potential(GWP),acidification potential(AP),and cumulative energy demand(CED)of wind power at the regional scale for 2022 and under three future scenarios(high-power wind turbine promotion,reduced wind curtailment,and a comprehensive development scenario).The results indicate that in 2022,the life cycle GWP,AP,and CED of wind power in western China were 10.76 g CO_(2) eq/kWh,0.177 g SO_(2) eq/kWh,and 17.6 kJ/kWh,respectively.Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia,Gansu,Qinghai,Ningxia,and Xinjiang,producing average decreases of 8.64%in GWP,8.39%in AP,and 9.26%in CED.In contrast,for Guangxi,Chongqing,Sichuan,Guizhou,Yunnan,Xizang,and Shaanxi,the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment,producing average decreases of 3.45%,3.09%,and 4.29%in GWP,AP,and CED,respectively.These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.
基金supported by the Smart Grid-National Science and Technology Major Project of China(2024ZD0802000).
文摘Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.
基金jointly supported by the National Natural Science Foundation of China[grant numbers U2342202,42175005,and 42175016]the Qing Lan Project[grant number R2023Q06]。
文摘This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.
基金supported by the National Natural Science Foundation of China(No.52171284)。
文摘Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金supported by the Agency for Defense Development Grant Funded by the Korean Government(Grant No.912822501).
文摘Unmanned combat aerial vehicles require lightweight,stealth-capable exhaust systems.However,traditional metallic nozzles increase radar detectability and reduce range,while advanced composites offer high performance but are expensive.Therefore,to improve the operational range and survivability of unmanned combat aerial vehicles,a lightweight,high-temperature-resistant,oxidation-resistant,and low-observable composite exhaust nozzle is developed to replace conventional metallic straight-type nozzles.The nozzle features a double serpentine shape to reduce radar and infrared signatures and is manufactured as a monolithic structure using the filament winding process,accommodating the complex geometry and large size(length:1.8 m,width:0.8 m).The exhaust nozzle consists of a ceramic matrix composite made of silicon carbide fibers and a silicon oxycarbide matrix,which absorbs and scatters radio frequency signals while withstanding prolonged exposure to high-temperature(700℃)oxidizing environments typical of engine exhaust gases.The polysiloxane resin used to produce the silicon oxycarbide matrix poses significant challenges owing to its low tackiness and high viscosity variations depending on the presence of nanoparticles,making filament winding difficult.These challenges are addressed by optimizing resin viscosity and winding pattern design.As a result,the tensile strength of the composite specimens fabricated with the optimized viscosity increases by 228.03% before pyrolysis and 97.68%after pyrolysis,compared with that of the non-optimized specimens.In addition,the density and tensile strength of the composite processed via three cycles of polymer infiltration and pyrolysis increased by 13.08% and 80.37%,respectively,compared to those of the non-densified composite.High-temperature oxidation and flame tests demonstrate exceptional thermal and oxidative stability.Furthermore,when compared with carbon fiber-reinforced ceramic matrix composites,the developed composite exhibits a permittivity at least two levels lower and a reflection loss below7 dB within the frequency range of 9.3-10.9 GHz,underscoring its superior electromagnetic stealth performance.
基金supported by the China Three Gorges Corporation(No.NBZZ202300860)the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction.However,several challenges remain.First,signal noise collected during blade operation masks fault features,severely impairing the fault diagnosis performance of deep learning models.Second,current blade fault diagnosis often relies on single-sensor data,resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states.To address these issues,a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed.Specifically,a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to learn complex features under noisy conditions,while a Weight-Aligned Data Fusion Module is designed to comprehensively and effectively utilize multi-sensor fault information.Experimental results of wind turbine blade fault diagnosis under different noise interferences show that higher accuracy is achieved by the proposed method compared to models with single-source data input,enabling comprehensive and effective fault diagnosis.
基金Supported by the Project of Design of Anti-corrosion and Anti-fouling Solutions for Offshore Wind Power Water-Cooled Systems(No.E428161)the National Natural Science Foundation of China(No.42176047)。
文摘Water-cooled system have significantly enhanced the power generation efficiency of offshore wind turbines.However,these innovative systems are susceptible to substantial biological fouling,maintenance challenges,and high upkeep costs.Therefore,the development of a specialized front-end filter tailored for direct current water-cooled system is importance.This involves the integration of dimensionally stable anode(DSA)and nickel alloy cathode,valued for their corrosion resistance in seawater,into a novel front-end filter system for Water-cooled applications.This system has the dual capability of generating hydrogen and chlorine for self-cleaning purposes.Implementing a flushing pulse electrolysis mode,it effectively mitigates electrode failure induced by cathodic calcium and magnesium deposition,thereby significantly prolonging electrode lifespan.Laboratory tests comprising system assembly and performance evaluations were conducted,with the system programmed to operate for 5 minutes every 24 hours under continuous flushing by natural seawater to simulate real-world conditions.After more than 11 months of continuous flushing,observations reveal that the DSA mesh and nickel alloy mesh maintain intact structural integrity and normal functioning.Subsequent 1꞉1 physical prototype Sea trial further validated the soundness of the system design and electrolytic control parameters.
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
基金supported in part by the National Natural Science Foundation of China(Nos.52475550,52305095)in part by the Key R&D Project of Liaoning Province,China(No.2023JH2/101800026)。
文摘Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce random multi-dimensional vibration with different characteristics,which makes it impossible to obtain accurate and efficient aerodynamic data.Therefore,in order to ensure the reliable and efficient conduction of wind tunnel test,a wind-tunnel-modeladaptive vibration control method is proposed in this paper.First,the split type adaptive vibration suppression structure is designed.Second,the multi-dimensional vibration characteristic characterization method is derived and the vibration characteristic identification method of the system is designed.Then,a vibration state estimation model is established according to the identification results of vibration characteristics,and a multi-actuator cooperative control method based on vibration state estimation is constructed.Finally,a model-adaptive vibration control system is built,and vibration characteristics identification and hammer experiments are carried out for two types of typical models.The results show that the proposed model-adaptive vibration control method increases the equivalent damping ratio of pitch and yaw dimensions of the high-aspect-ratio class model by 8.19 times and 48.81 times,respectively.The equivalent damping ratio of pitch and yaw dimensions of the highslenderness-ratio class model is increased by 16.44 and 5.43 times,respectively.It provides a strong guarantee for the reliable and efficient development of multi-type wind tunnel test tasks.
