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
Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication,transportation,and installation.Nonetheless,their conceptual design remains predominantly dependent on enginee...Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication,transportation,and installation.Nonetheless,their conceptual design remains predominantly dependent on engineering experience,and a generally applicable approach is still absent.This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application.A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach,wherein modular constraints and their corresponding sensitivity expressions are explicitly included.The method is applied to a reference wind turbine model to generate modular lattice configurations.The novel structural models are evaluated under three representative design load cases outlined in IEC 61400 by finite element analysis.Compared with the reference structure,the 12-layer self-similar modular design reduces the maximum deformation and von Mises stress by 39.5%and 51.1%,respectively,demonstrating a substantial stiffness improvement while preserving modularity.The suggested approach provides an efficient and practical tool for the conceptual design of modular lattice-type wind turbine towers.展开更多
The coupling effects among the flow field,temperature distribution and structural deformation in a turbine cannot be ignored,particularly during flight cycles when the turbine experiences varied operational states.Rel...The coupling effects among the flow field,temperature distribution and structural deformation in a turbine cannot be ignored,particularly during flight cycles when the turbine experiences varied operational states.Relying solely on steady-state solutions cannot predict the detrimental effects caused by hysteresis.Consequently,this paper employs a quasi-steady-state fluid-thermalstructure multidisciplinary coupling solution method,integrating transient solid heat conduction with steady-state flow field and static structural deformation solutions.After conducting a numerical simulation of a three-dimensional,five-stage,low-pressure turbine air system,the following conclusions are drawn:when boundary conditions attain high-power states through processes that are numerically identical but in opposite directions,slight variations in solid deformation significantly impact the flow field;when boundary conditions attain high-power states through processes that are directionally consistent but have different numerical values,the influence of the boundary condition change rate on the flow field surpasses that of solid deformation.In terms of turbine design parameters,a large difference in stage-reaction between adjacent stages at the lower radius of the turbine can lead to significant changes in the disc cavity flow field during flight cycles.The difference in the stage-reaction of 0.23 at 10%blade height in adjacent stages may induce severe gas ingress in the stator disc cavity.Thus,it is crucial to minimize this difference and to appropriately extend the duration of the deceleration phase to ensure the turbine's safe operation.展开更多
Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the acc...Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the accuracy of emission prediction models,supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts.This paper presents a data-driven approach for the accurate prediction of gas emissions,specifically nitrogen oxides(NOx)and carbon monoxide(CO),in natural gas power plants using an optimized hybrid machine learning framework.The proposed model integrates a Feedforward Neural Network(FFNN)trained using Particle Swarm Optimization to capture the nonlinear emission dynamics under varying gas turbine operating conditions.To further enhance predictive performance,the K-Nearest Neighbor(K-NN)algorithm serves as a post-processing method to enhance IPSO-FFNN predictions through adjustment and refinement,improving overall prediction accuracy,while Neighbor Component Analysis is used to identify and rank the most influential operational variables.The study makes a significant contribution through the combination of NCA feature selection with PSO global optimization,FFNN nonlinear modelling,and K-NN error correction into one unified system,which delivers precise emission predictions.The model was developed and tested using a real-world dataset collected from gas-fired turbine operations,with validated results demonstrating robust accuracy,achieving Root Mean Square Error values of 0.355 for CO and 0.368 for NOx.When benchmarked against conventional models such as standard FFNN,Support Vector Regression,and Long Short-Term Memory networks,the hybrid model achieved substantial improvements,up to 97.8%in Mean Squared Error,95%in Mean Absolute Error(MAE),and 85.19%in RMSE for CO;and 97.16%in MSE,93.4%in MAE,and 83.15%in RMSE for NOx.These results underscore the model’s potential for improving emission prediction,thereby supporting enhanced operational efficiency and adherence to environmental standards.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
Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's per...Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's performance.The multiscale mixing phenomenon in a typical high-pressure turbine rotor flow was studied in this work.The contributions of various scale flows to entropy production and mixing properties were identified.The corresponding physical mechanisms at different scales were explored.It is shown that the large-scale and time-averaged flow contributions to mixing are significant,accounting for approximately 37.1% and 25% of the total.Time-averaged and large-scale flows cause the majority of the fluid deformation of the material surface,while mesoand small-scale flows just generate finer deformations.It raises the area stretch coefficient and the virtual concentration gradient.Thus,mixing is enhanced.Furthermore,time-averaged and large-scale flows account for the majority of the losses in the upstream and downstream regions of the blade tip respectively,accounting for approximately 53.8%and 33.5%of the total.The sheet-like structures—rather than the tip leaking vortex—are the primary source of the loss.High-dissipation regions are produced by the sheet-like structures via the pressure Hessian term and the self-amplification terms.展开更多
The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of ...The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.展开更多
The low-pressure and low-density conditions encountered at high altitudes significantly reduce the operating Reynolds number of micro radial-flow turbines,frequently bringing it below the self-similarity critical thre...The low-pressure and low-density conditions encountered at high altitudes significantly reduce the operating Reynolds number of micro radial-flow turbines,frequently bringing it below the self-similarity critical threshold of 3.5×10^(4).This departure undermines the applicability of conventional similarity-based design approaches.In this study,micro radial-flow turbines with rotor diameters below 50 mm are investigated through a combined approach integrating high-fidelity numerical simulations with experimental validation,aiming to elucidate the mechanisms by which low Reynolds numbers influence aerodynamic and thermodynamic performance.The results demonstrate that decreasing Reynolds number leads to boundary-layer thickening on blade surfaces,enhanced flow separation on the suction side,and increased secondary-flow losses within the blade passages.These effects jointly produce a pronounced and non-linear deterioration of turbine efficiency.Geometric scaling analysis further indicates that efficiency losses intensify with decreasing turbine size,and become particularly severe at low rotational speeds and high expansion ratios.Detailed flow-field analyses reveal a direct link between the degradation of blade loading distribution and the amplification of transverse pressure gradients under low-Reynolds-number conditions,providing physical insight into the observed performance decline.展开更多
Stator vanes especially vane suction sides of transonic turbines are subjected to high frequency excitation forces under many circumstances,and thus are exposed to the risk of high cycle fatigue.Therefore,it is necess...Stator vanes especially vane suction sides of transonic turbines are subjected to high frequency excitation forces under many circumstances,and thus are exposed to the risk of high cycle fatigue.Therefore,it is necessary to reveal the flow mechanism of this kind of excitations for potential prevention measures.In this paper,the traveling shock phenomenon in the transonic turbine stator/rotor gap is observed and the concept of‘Inter-Row Traveling Shock(IRTS)'is proposed through the unsteady Reynolds-Averaged Navier-Stokes(RANS)simulation of a typical highlyloaded transonic turbine stage.The characteristics of an IRTS were described and summarized in aspects of unsteady shock wave system,aerodynamic characteristics and motion.The probable forming mechanism of an IRTS was explained through a theoretical model and it was validated through correct prediction of the flow state parameter change across the IRTS.Since IRTSs would strike onto vane suction sides,the pressure oscillation dynamic modes on vane suction side corresponding to the characteristic frequencies associated with IRTS were extracted through Dynamic Mode Decomposition(DMD),from which the way and extent of the IRTS influences on vane aerodynamic excitation were revealed and evaluated.Over 82%pressure oscillation energy on vane suction side could be brought by the IRTS sweeping along with blade rotation.展开更多
The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one...The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.展开更多
To address challenges in wind turbine blade defect detection models,primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices,this study proposes a...To address challenges in wind turbine blade defect detection models,primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices,this study proposes a wind turbine blade defect detection model,WtCS-YOLO11,that incorporates multiscale feature extraction and an attention mechanism.Firstly,the cross-stage partial with two kernels and a wavelet convolution module(C3k2_WTConv)is proposed by introducing wavelet convolution into the module.The cross-stage partial with two kernels(C3k2)module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field,enable multiscale feature extraction,and reduce computational parameter usage.Second,the convolutional block attention module(CBAM)is proposed and applied to the neck network,integrating channel and spatial attention,allowing the model to focus on essential features and enhance its ability to detect large targets.In addition,the model employs shape-aware intersection over union(Shape-IoU),which focuses on the shape and scale of bounding boxes,and combines the normalized Wasserstein distance(NWD)to calculate bounding box similarity,thereby improving the accuracy of bounding-box regression.In this study,a dataset for wind turbine blade defect detection was constructed covering six defect categories.The experimental results showed that the precision(P),recall(R),and mean average precision at the intersection over union threshold of 0.5(mAP50)for the WtCS-YOLO11 model were 84.4%,86.9%,and 89.7%,respectively.Compared to the baseline You Only Look Once 11(YOLO11)model,P,R,and mAP50 improved by 5.9%,2.5%,and 2.4%,respectively,with virtually no increase in computational complexity or parameter count.WtCS-YOLO11 improved the precision measurement accuracy.Its model size and computational complexity are suitable for deployment on edge devices,and it achieves high inference speed,meeting the application requirements for real-time wind turbine blade defect detection.展开更多
A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.T...A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.展开更多
Inertial response control(IRC)makes variable-speed wind turbine generators(WTGs)provide short-term frequency support during contingencies by releasing the kinetic energy stored in wind turbine rotors.When frequency su...Inertial response control(IRC)makes variable-speed wind turbine generators(WTGs)provide short-term frequency support during contingencies by releasing the kinetic energy stored in wind turbine rotors.When frequency support is terminated,the rotor speed should be restored to optimum for maximum power point tracking(MPPT).Existing IRCs utilize rotor speed recovery(RSR)strategies with a consistent power reference function.However,under real turbulent wind with alternate gusts and lulls,the consistent power reference function may fail to restore rotor speed or cause unexpected secondary frequency drop(SFD).In this regard,this paper proposes a novel adaptive RSR strategy that not only restores rotor speed via the aerodynamic power enhanced by wind gusts,but also stabilizes the turbine at wind lulls by tracking a suboptimal power curve.Experiments on a wind power-integrated power system testbed validate the proposed RSR strategy can successfully restore rotor speed while attenuating SFD under turbulent wind.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
With the continuous development of the offshore wind industry,the design concept of composite foundation has been given attention in the past decade.This paper presents an accurate method for investigating the horizon...With the continuous development of the offshore wind industry,the design concept of composite foundation has been given attention in the past decade.This paper presents an accurate method for investigating the horizontal vibration of monopile-friction wheel composite foundations in layered saturated soil.Firstly,the three-dimensional continuum mechanics theory with the range of linear elasticity is introduced to calculate the frictional resistance distributed on the upper soil surface.Then,the resistances of multilayered soils and inviscid seawater to the pile shaft under horizontal harmonic excitation are obtained using Novak's plane strain model,Biot's porous media theory and radiationwave theory.Thirdly,the expressions for the deformation,bending moment and internal force of the Euler-Bernoulli pile are derived using the boundary conditions with definitephysical meaning and transfer matrix method.By comparing with the results of 1g laboratory test and the idealized formula reported by the literature,the rationality and accuracy of the developed dynamical model can be verified.Finally,this paper conducts a series of worked examples to investigate the influencesof the elastic modulus and thickness of three-layer saturated soil and the location of interlayer soil on the horizontal dynamic vibration of composite foundation.The results show that an increase in elastic modulus of the surface soil is an effective way to improve the dynamic stability of the composite foundation in service conditions.The conclusions drawn from the numerical examples can develop some guidelines for the current foundation design of offshore wind turbines.展开更多
Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that devi...Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.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
To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-posi...To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.展开更多
Thermal barrier coatings(TBCs)are extensively utilized in aero-engines and heavy-duty gas turbines due to their outstanding properties,including low thermal conductivity,corrosion,high-temperature oxidation,and wear r...Thermal barrier coatings(TBCs)are extensively utilized in aero-engines and heavy-duty gas turbines due to their outstanding properties,including low thermal conductivity,corrosion,high-temperature oxidation,and wear resistance.The rising thrust-to-weight ratio and service temperature in engine hot sections have presented a significant challenge in TBC's materials,structure,and preparation process;it is one of the current research hotspots in the aviation field.This paper reviews the recent advancement in turbine blade TBCs.It focuses on the TBC's structure,deposition mechanism and the key performance evaluation indexes for TBCs applied to turbine blades.Finally,the future research field of TBCs for turbine blades is also be prospected.展开更多
Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the random...Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability assessment.First,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random excitations.Then,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic systems.The probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,respectively.Furthermore,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of system.Finally,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other methods.It is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue problems.The rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.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.
基金funded by the National Key Research and Development Program of China(No.2024YFE0208600)the National Natural Science Foundation of China(No.U24B2090).
文摘Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication,transportation,and installation.Nonetheless,their conceptual design remains predominantly dependent on engineering experience,and a generally applicable approach is still absent.This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application.A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach,wherein modular constraints and their corresponding sensitivity expressions are explicitly included.The method is applied to a reference wind turbine model to generate modular lattice configurations.The novel structural models are evaluated under three representative design load cases outlined in IEC 61400 by finite element analysis.Compared with the reference structure,the 12-layer self-similar modular design reduces the maximum deformation and von Mises stress by 39.5%and 51.1%,respectively,demonstrating a substantial stiffness improvement while preserving modularity.The suggested approach provides an efficient and practical tool for the conceptual design of modular lattice-type wind turbine towers.
基金supported by the National Science and Tech-nology Major Project,China(No.J2019-II-0012-0032)。
文摘The coupling effects among the flow field,temperature distribution and structural deformation in a turbine cannot be ignored,particularly during flight cycles when the turbine experiences varied operational states.Relying solely on steady-state solutions cannot predict the detrimental effects caused by hysteresis.Consequently,this paper employs a quasi-steady-state fluid-thermalstructure multidisciplinary coupling solution method,integrating transient solid heat conduction with steady-state flow field and static structural deformation solutions.After conducting a numerical simulation of a three-dimensional,five-stage,low-pressure turbine air system,the following conclusions are drawn:when boundary conditions attain high-power states through processes that are numerically identical but in opposite directions,slight variations in solid deformation significantly impact the flow field;when boundary conditions attain high-power states through processes that are directionally consistent but have different numerical values,the influence of the boundary condition change rate on the flow field surpasses that of solid deformation.In terms of turbine design parameters,a large difference in stage-reaction between adjacent stages at the lower radius of the turbine can lead to significant changes in the disc cavity flow field during flight cycles.The difference in the stage-reaction of 0.23 at 10%blade height in adjacent stages may induce severe gas ingress in the stator disc cavity.Thus,it is crucial to minimize this difference and to appropriately extend the duration of the deceleration phase to ensure the turbine's safe operation.
文摘Thermal power plants are the main contributors to greenhouse gas emissions.The prediction of the emission supports the decision makers and environmental sustainability.The objective of this study is to enhance the accuracy of emission prediction models,supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts.This paper presents a data-driven approach for the accurate prediction of gas emissions,specifically nitrogen oxides(NOx)and carbon monoxide(CO),in natural gas power plants using an optimized hybrid machine learning framework.The proposed model integrates a Feedforward Neural Network(FFNN)trained using Particle Swarm Optimization to capture the nonlinear emission dynamics under varying gas turbine operating conditions.To further enhance predictive performance,the K-Nearest Neighbor(K-NN)algorithm serves as a post-processing method to enhance IPSO-FFNN predictions through adjustment and refinement,improving overall prediction accuracy,while Neighbor Component Analysis is used to identify and rank the most influential operational variables.The study makes a significant contribution through the combination of NCA feature selection with PSO global optimization,FFNN nonlinear modelling,and K-NN error correction into one unified system,which delivers precise emission predictions.The model was developed and tested using a real-world dataset collected from gas-fired turbine operations,with validated results demonstrating robust accuracy,achieving Root Mean Square Error values of 0.355 for CO and 0.368 for NOx.When benchmarked against conventional models such as standard FFNN,Support Vector Regression,and Long Short-Term Memory networks,the hybrid model achieved substantial improvements,up to 97.8%in Mean Squared Error,95%in Mean Absolute Error(MAE),and 85.19%in RMSE for CO;and 97.16%in MSE,93.4%in MAE,and 83.15%in RMSE for NOx.These results underscore the model’s potential for improving emission prediction,thereby supporting enhanced operational efficiency and adherence to environmental standards.
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 National Science and Technology Major Project,China(No.J2019-Ⅱ-0012-0032)。
文摘Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's performance.The multiscale mixing phenomenon in a typical high-pressure turbine rotor flow was studied in this work.The contributions of various scale flows to entropy production and mixing properties were identified.The corresponding physical mechanisms at different scales were explored.It is shown that the large-scale and time-averaged flow contributions to mixing are significant,accounting for approximately 37.1% and 25% of the total.Time-averaged and large-scale flows cause the majority of the fluid deformation of the material surface,while mesoand small-scale flows just generate finer deformations.It raises the area stretch coefficient and the virtual concentration gradient.Thus,mixing is enhanced.Furthermore,time-averaged and large-scale flows account for the majority of the losses in the upstream and downstream regions of the blade tip respectively,accounting for approximately 53.8%and 33.5%of the total.The sheet-like structures—rather than the tip leaking vortex—are the primary source of the loss.High-dissipation regions are produced by the sheet-like structures via the pressure Hessian term and the self-amplification terms.
基金funded by the Science and Technology Vice President Project in Changping District,Beijing(Project Name:Research on multi-scale optimization and intelligent control technology of integrated energy systemProject number:202302007013).
文摘The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation,mechanical wear,and airflow disturbances during prolonged operation.These conditions can lead to a series of issues,including mechanical faults,air path malfunctions,and combustion irregularities.Traditional modelbased approaches face inherent limitations due to their inability to handle nonlinear problems,natural factors,measurement uncertainties,fault coupling,and implementation challenges.The development of artificial intelligence algorithms has provided an effective solution to these issues,sparking extensive research into data-driven fault diagnosis methodologies.The review mechanism involved searching IEEE Xplore,ScienceDirect,and Web of Science for peerreviewed articles published between 2019 and 2025,focusing on multi-fault diagnosis techniques.A total of 220 papers were identified,with 123 meeting the inclusion criteria.This paper provides a comprehensive review of diagnostic methodologies,detailing their operational principles and distinctive features.It analyzes current research hotspots and challenges while forecasting future trends.The study systematically evaluates the strengths and limitations of various fault diagnosis techniques,revealing their practical applicability and constraints through comparative analysis.Furthermore,this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.
基金supported by the Tiangsu Association for Science and Technology(Grant No.JSKX 0225089).
文摘The low-pressure and low-density conditions encountered at high altitudes significantly reduce the operating Reynolds number of micro radial-flow turbines,frequently bringing it below the self-similarity critical threshold of 3.5×10^(4).This departure undermines the applicability of conventional similarity-based design approaches.In this study,micro radial-flow turbines with rotor diameters below 50 mm are investigated through a combined approach integrating high-fidelity numerical simulations with experimental validation,aiming to elucidate the mechanisms by which low Reynolds numbers influence aerodynamic and thermodynamic performance.The results demonstrate that decreasing Reynolds number leads to boundary-layer thickening on blade surfaces,enhanced flow separation on the suction side,and increased secondary-flow losses within the blade passages.These effects jointly produce a pronounced and non-linear deterioration of turbine efficiency.Geometric scaling analysis further indicates that efficiency losses intensify with decreasing turbine size,and become particularly severe at low rotational speeds and high expansion ratios.Detailed flow-field analyses reveal a direct link between the degradation of blade loading distribution and the amplification of transverse pressure gradients under low-Reynolds-number conditions,providing physical insight into the observed performance decline.
文摘Stator vanes especially vane suction sides of transonic turbines are subjected to high frequency excitation forces under many circumstances,and thus are exposed to the risk of high cycle fatigue.Therefore,it is necessary to reveal the flow mechanism of this kind of excitations for potential prevention measures.In this paper,the traveling shock phenomenon in the transonic turbine stator/rotor gap is observed and the concept of‘Inter-Row Traveling Shock(IRTS)'is proposed through the unsteady Reynolds-Averaged Navier-Stokes(RANS)simulation of a typical highlyloaded transonic turbine stage.The characteristics of an IRTS were described and summarized in aspects of unsteady shock wave system,aerodynamic characteristics and motion.The probable forming mechanism of an IRTS was explained through a theoretical model and it was validated through correct prediction of the flow state parameter change across the IRTS.Since IRTSs would strike onto vane suction sides,the pressure oscillation dynamic modes on vane suction side corresponding to the characteristic frequencies associated with IRTS were extracted through Dynamic Mode Decomposition(DMD),from which the way and extent of the IRTS influences on vane aerodynamic excitation were revealed and evaluated.Over 82%pressure oscillation energy on vane suction side could be brought by the IRTS sweeping along with blade rotation.
基金supported by the National Natural Science Foundation of China under Grant 52022016China Postdoctoral Science Foundation under grant 2021M693711Fundamental Research Funds for the Central Universities under grant 2021CDJQY-037.
文摘The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.
基金supported in part by the Science and Technology Research Project of Henan Province under grants 242102240040 and 222102210087in part by the Training Plan for Young Backbone Teachers at Undergraduate Universities in Henan Province under grant 2024GGJS155.
文摘To address challenges in wind turbine blade defect detection models,primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices,this study proposes a wind turbine blade defect detection model,WtCS-YOLO11,that incorporates multiscale feature extraction and an attention mechanism.Firstly,the cross-stage partial with two kernels and a wavelet convolution module(C3k2_WTConv)is proposed by introducing wavelet convolution into the module.The cross-stage partial with two kernels(C3k2)module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field,enable multiscale feature extraction,and reduce computational parameter usage.Second,the convolutional block attention module(CBAM)is proposed and applied to the neck network,integrating channel and spatial attention,allowing the model to focus on essential features and enhance its ability to detect large targets.In addition,the model employs shape-aware intersection over union(Shape-IoU),which focuses on the shape and scale of bounding boxes,and combines the normalized Wasserstein distance(NWD)to calculate bounding box similarity,thereby improving the accuracy of bounding-box regression.In this study,a dataset for wind turbine blade defect detection was constructed covering six defect categories.The experimental results showed that the precision(P),recall(R),and mean average precision at the intersection over union threshold of 0.5(mAP50)for the WtCS-YOLO11 model were 84.4%,86.9%,and 89.7%,respectively.Compared to the baseline You Only Look Once 11(YOLO11)model,P,R,and mAP50 improved by 5.9%,2.5%,and 2.4%,respectively,with virtually no increase in computational complexity or parameter count.WtCS-YOLO11 improved the precision measurement accuracy.Its model size and computational complexity are suitable for deployment on edge devices,and it achieves high inference speed,meeting the application requirements for real-time wind turbine blade defect detection.
基金supported by the National Science and Technology Major Project,China(No.2017-II-0006-0019)the National Natural Science Foundation of China(No.52375266)the Shaanxi Science Foundation for Distinguished Young Scholars,China(No.2022JC-36)。
文摘A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.
基金supported by National Natural Science Foundation of China(51977111)the Six Talent Peaks High-level Talent Project in Jiangsu Province(XNY-025)the Special Fund of Jiangsu Province for Transformation of Scientific and Technological Achievements(BA2019045).
文摘Inertial response control(IRC)makes variable-speed wind turbine generators(WTGs)provide short-term frequency support during contingencies by releasing the kinetic energy stored in wind turbine rotors.When frequency support is terminated,the rotor speed should be restored to optimum for maximum power point tracking(MPPT).Existing IRCs utilize rotor speed recovery(RSR)strategies with a consistent power reference function.However,under real turbulent wind with alternate gusts and lulls,the consistent power reference function may fail to restore rotor speed or cause unexpected secondary frequency drop(SFD).In this regard,this paper proposes a novel adaptive RSR strategy that not only restores rotor speed via the aerodynamic power enhanced by wind gusts,but also stabilizes the turbine at wind lulls by tracking a suboptimal power curve.Experiments on a wind power-integrated power system testbed validate the proposed RSR strategy can successfully restore rotor speed while attenuating SFD under turbulent wind.
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.
基金supported by the National Natural Science Foundation of China(Grant No.52178329),the China Scholarship Council(Grant No.202306130155)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(Grant No.CX20230442).
文摘With the continuous development of the offshore wind industry,the design concept of composite foundation has been given attention in the past decade.This paper presents an accurate method for investigating the horizontal vibration of monopile-friction wheel composite foundations in layered saturated soil.Firstly,the three-dimensional continuum mechanics theory with the range of linear elasticity is introduced to calculate the frictional resistance distributed on the upper soil surface.Then,the resistances of multilayered soils and inviscid seawater to the pile shaft under horizontal harmonic excitation are obtained using Novak's plane strain model,Biot's porous media theory and radiationwave theory.Thirdly,the expressions for the deformation,bending moment and internal force of the Euler-Bernoulli pile are derived using the boundary conditions with definitephysical meaning and transfer matrix method.By comparing with the results of 1g laboratory test and the idealized formula reported by the literature,the rationality and accuracy of the developed dynamical model can be verified.Finally,this paper conducts a series of worked examples to investigate the influencesof the elastic modulus and thickness of three-layer saturated soil and the location of interlayer soil on the horizontal dynamic vibration of composite foundation.The results show that an increase in elastic modulus of the surface soil is an effective way to improve the dynamic stability of the composite foundation in service conditions.The conclusions drawn from the numerical examples can develop some guidelines for the current foundation design of offshore wind turbines.
基金supported in part by the National Natural Science Foundation of China(No.52467008)Gansu Provincial Depatment of Education Youth Doctoral Suppo Project(2024QB-051).
文摘Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.
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.
基金The National Natural Science Foundation of China (No. 51908107)。
文摘To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.
基金supported by the National Natural Science Foundation of China(Grant No.52271087).
文摘Thermal barrier coatings(TBCs)are extensively utilized in aero-engines and heavy-duty gas turbines due to their outstanding properties,including low thermal conductivity,corrosion,high-temperature oxidation,and wear resistance.The rising thrust-to-weight ratio and service temperature in engine hot sections have presented a significant challenge in TBC's materials,structure,and preparation process;it is one of the current research hotspots in the aviation field.This paper reviews the recent advancement in turbine blade TBCs.It focuses on the TBC's structure,deposition mechanism and the key performance evaluation indexes for TBCs applied to turbine blades.Finally,the future research field of TBCs for turbine blades is also be prospected.
基金supports of the National Natural Science Foundation of China(Nos.12032008,12102080)the Fundamental Research Funds for the Central Universities,China(No.DUT23RC(3)038)are much appreciated。
文摘Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability assessment.First,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random excitations.Then,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic systems.The probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,respectively.Furthermore,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of system.Finally,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other methods.It is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue problems.The rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.
