Optimized Design of Bio-Inspired Wind Turbine Blades

To enhance the aerodynamic performance of wind turbine blades,this study proposes the adoption of a bionic airfoil inspired by the aerodynamic shape of an eagle.Based on the blade element theory,a non-uniform extraction method of blade elements is employed for the optimization design of the considered wind turbine blades.Moreover,Computational Fluid Dynamics(CFD)is used to determine the aerodynamic performances of the eagle airfoil and a NACA2412 airfoil,thereby demonstrating the superior aerodynamic performance of the former.Finally,a mathematical model for optimizing the design of wind turbine blades is introduced and a comparative analysis is conducted with respect to the aerodynamic performances of blades designed using a uniform extraction approach.It is found that the blades designed using non-uniform extraction exhibit better aerodynamic performance.

Research on the Icing Diagnosis ofWind Turbine Blades Based on FS–XGBoost–EWMA

In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.

Numerical simulation on directional solidification and heat treatment processes of turbine blades

Study on turbine blades is crucial due to their critical role in ensuring the efficient and reliable operation of aircraft engines.Nickel-based single crystal superalloys are extensively used in the hot manufacturing of turbine blades due to their exceptional high-temperature mechanical properties.The hot manufacturing of single crystal blades involves directional solidification and heat treatment.Experimental manufacturing of these blades is time-consuming,capital-intensive,and often insufficient to meet industrial demands.Numerical simulation techniques have gained widespread acceptance in blade manufacturing research due to their low energy consumption,high efficiency,and rapid turnaround time.This article introduces the modeling and simulation of hot manufacturing in single crystal blades.The discussion outlines the prevalent mathematical models employed in numerical simulations related to blade hot manufacturing.It encapsulates the advancements in research concerning macro to micro-level numerical simulation techniques for directional solidification and heat treatment processes.Furthermore,potential future trajectories for the numerical simulation of single crystal blade hot manufacturing are also discussed.

Nonlinear Flap-Wise Vibration Characteristics ofWind Turbine Blades Based onMulti-Scale AnalysisMethod

This work presents a novel approach to achieve nonlinear vibration response based on the Hamilton principle.We chose the 5-MW reference wind turbine which was established by the National Renewable Energy Laboratory(NREL),to research the effects of the nonlinear flap-wise vibration characteristics.The turbine wheel is simplified by treating the blade of a wind turbine as an Euler-Bernoulli beam,and the nonlinear flap-wise vibration characteristics of the wind turbine blades are discussed based on the simplification first.Then,the blade’s large-deflection flap-wise vibration governing equation is established by considering the nonlinear term involving the centrifugal force.Lastly,it is truncated by the Galerkin method and analyzed semi-analytically using the multi-scale analysis method,and numerical simulations are carried out to compare the simulation results of finite elements with the numerical simulation results using Campbell diagram analysis of blade vibration.The results indicated that the rotational speed of the impeller has a significant impact on blade vibration.When the wheel speed of 12.1 rpm and excitation amplitude of 1.23 the maximum displacement amplitude of the blade has increased from 0.72 to 3.16.From the amplitude-frequency curve,it can be seen that the multi-peak characteristic of blade amplitude frequency is under centrifugal nonlinearity.Closed phase trajectories in blade nonlinear vibration,exhibiting periodic motion characteristics,are found through phase diagrams and Poincare section diagrams.

Surface Defect Detection and Evaluation Method of Large Wind Turbine Blades Based on an Improved Deeplabv3+Deep Learning Model

The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade,so as to ensure the safe and stable operation of wind turbine in natural environment.The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics.The strain signal contains a lot of noise,which makes the analysis error.Therefore,it is very important to denoise and extract features of measured signals before signal analysis.In this paper,the joint algorithm of ensemble empirical mode decomposition(EEMD)and wavelet transform(WT)is used for the first time to achieve sufficient noise reduction and effectively extract the feature signals of non-stationary strain signals.The application process of EEMD-WT is optimized.This optimization can avoid the repeated selection of wavelet basis function and the number of decomposition layers due to different crosswind conditions.EEMD adaptively decomposes the strain signal into intrinsic mode functions,to judge the frequency of IMFs,remove the high-frequency noise components,retain the useful components.The useful components are denoised twice by the wavelet transform,the components and residual terms after the secondary denoising are reconstructed to obtain the characteristic signal.The EEMD-WT was applied to process the simulating signals andmeasured the strain signals.The results were compared with the results of the EEMD.The results showed that the EEMD-WTmethod has better noise reduction performance,and can effectively extract the characteristics of strain signals,which lays a solid foundation for accurate analysis of wind turbine blade strain signals under crosswind conditions.

Mechanism of internal thermal runaway propagation in blade batteries

Blade batteries are extensively used in electric vehicles,but unavoidable thermal runaway is an inherent threat to their safe use.This study experimentally investigated the mechanism underlying thermal runaway propagation within a blade battery by using a nail to trigger thermal runaway and thermocouples to track its propagation inside a cell.The results showed that the internal thermal runaway could propagate for up to 272 s,which is comparable to that of a traditional battery module.The velocity of the thermal runaway propagation fluctuated between 1 and 8 mm s^(-1),depending on both the electrolyte content and high-temperature gas diffusion.In the early stages of thermal runaway,the electrolyte participated in the reaction,which intensified the thermal runaway and accelerated its propagation.As the battery temperature increased,the electrolyte evaporated,which attenuated the acceleration effect.Gas diffusion affected thermal runaway propagation through both heat transfer and mass transfer.The experimental results indicated that gas diffusion accelerated the velocity of thermal runaway propagation by 36.84%.We used a 1D mathematical model and confirmed that convective heat transfer induced by gas diffusion increased the velocity of thermal runaway propagation by 5.46%-17.06%.Finally,the temperature rate curve was analyzed,and a three-stage mechanism for internal thermal runaway propagation was proposed.In Stage I,convective heat transfer from electrolyte evaporation locally increased the temperature to 100℃.In Stage II,solid heat transfer locally increases the temperature to trigger thermal runaway.In StageⅢ,thermal runaway sharply increases the local temperature.The proposed mechanism sheds light on the internal thermal runaway propagation of blade batteries and offers valuable insights into safety considerations for future design.

Numerical Simulation of Water Droplets Deposition on the Last-Stage Stationary Blade of Steam Turbine

Based on the method of discrete phase, the law of droplets’ deposition in the last stage stationary blade of a supercritical 600 MW Steam Turbine is simulated in the first place of this paper by using the Wet-steam model in commercial software FLUENT, where the influence of inlet angle of water droplets of the stationary blades is also considered. Through the calculation, the relationship between the deposition and the diameter of water droplets is revealed. Then, the amount of droplets deposition in the suction and pressure surface is derived. The result is compared with experimental data and it proves that the numerical simulation result obtained in this paper is reasonable. Finally, a formula of the relationship between the diameter of water droplets and the inlet angle is fit, which could be used for approximate calculation in the engineering applications.

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their safety.Accurately identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this end.This paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory Control and Data Acquisition(SCADA)system.Two datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and balanced.The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the blade.Performance indicators for the model,including a reward function,are also designed to assess its predictive accuracy.Finally,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are calculated.The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature.The results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.

Thermal Analysis of Turbine Blades with Thermal Barrier Coatings Using Virtual Wall Thickness Method

Avirtual wall thicknessmethod is developed to simulate the temperature field of turbine bladeswith thermal barrier coatings(TBCs),to simplify the modeling process and improve the calculation efficiency.The results show that the virtualwall thickness method can improve themesh quality by 20%,reduce the number ofmeshes by 76.7%and save the calculation time by 35.5%,compared with the traditional real wall thickness method.The average calculation error of the two methods is between 0.21%and 0.93%.Furthermore,the temperature at the blade leading edge is the highest and the average temperature of the blade pressure surface is higher than that of the suction surface under a certain service condition.The blade surface temperature presents a high temperature at both ends and a low temperature in themiddle height when the temperature of incoming gas is uniformand constant.The thermal insulation effect of TBCs is the worst near the air film hole,and the best at the blade leading edge.According to the calculated temperature field of the substrate-coating system,the highest thermal insulation temperature of the TC layer is 172.01 K,and the thermal insulation proportions of TC,TGO and BC are 93.55%,1.54%and 4.91%,respectively.

Stiffness Degradation Modeling for Composite Wind Turbine Blades Based on Full-Scale Fatigue Testing

In order to provide more insights into the damage propagation composite wind turbine blades(blade)under cyclic fatigue loading,a stiffness degradation model for blade is proposed based on the full-scale fatigue testing of a blade.A novel non-linear fatigue damage accumulation model is proposed using the damage assessment theories of composite laminates for the first time.Then,a stiffness degradation model is established based on the correlation of fatigue damage and residual stiffness of the composite laminates.Finally,a stiffness degradation model for the blade is presented based on the full-scale fatigue testing.The scientific rationale of the proposed stiffness model of blade is verified by using full-scale fatigue test data of blade with a total length of 52.5 m.The results indicate that the proposed stiffness degradation model of the blade agrees well with the fatigue testing results of this blade.This work provides a basis for evaluating the fatigue damage and lifetime of blade under cyclic fatigue loading.

Research on Automatic Test System of Engine Blade Natural Frequency

Blades are one of the important components on aircraft engines.If they break due to vibration failure,the normal operation of the entire engine will be offected.Therefore,it is necessary to measure their natural frequency before installing them on the engine to avoid resonance.At present,most blade vibration testing systems require manual operation by operators,which has high requirements for operators and the testing process is also very cumbersome.Therefore,the testing efficiency is low and cannot meet the needs of efficient testing.To solve the current problems of low testing efficiency and high operational requirements,a high-precision and high-efficiency automatic test system is designed.The testing accuracy of this system can reach ±1%,and the testing efficiency is improved by 37% compared to manual testing.Firstly,the influence of compression force and vibration exciter position on natural frequency test is analyzed by amplitude-frequency curve,so as to calibrate servo cylinder and fourdimensional motion platform.Secondly,the sine wave signal is used as the excitation to sweep the blade linearly,and the natural frequency is determined by the amplitude peak in the frequency domain.Finally,the accuracy experiment and efficiency experiment are carried out on the developed test system,whose results verify its high efficiency and high precision.

Review on Dynamic Modeling and Vibration Characteristics of Rotating Cracked Blades

As one of the most important parts in the engine,the structure and state of the rotating blade directly affect the normal performance of the aeroengine.In order to monitor engine crack failure and ensure flight safety,it is necessary to carry out research on the dynamic modeling of the cracked blade and breathing crack-induced vibration mechanisms.This paper summarizes the current research status on the dynamics of cracked blade,and the related topics mainly include four aspects:crack propagation path,mechanical model of open and breathing cracks,dynamic modeling methods of cracked blades such as lumped mass model,semi-analytical model and finite element model,and dynamic characteristics of cracked blades.The review will provide valuable references for future studies on dynamics and fault diagnosis of cracked blade in aeroengine.

Damage Identification of Wind Turbine Blades–A Brief Review

The increasing size of these blades of wind turbines emphasizes the need for reliable monitoring and maintenance.This brief review explores the detection and analysis of damage in wind turbine blades.The study highlights various techniques,including acoustic emission analysis,strain signal monitoring,and vibration analysis,as effective approaches for damage detection.Vibration analysis,in particular,shows promise for fault identification by analyzing changes in dynamic characteristics.Damage indices based on modal properties,such as natural frequencies,mode shapes,and curvature,are discussed.

Blade Wrap Angle Impact on Centrifugal Pump Performance:Entropy Generation and Fluid-Structure Interaction Analysis

The centrifugal pump is a prevalent power equipment widely used in different engineering patterns,and the impeller blade wrap angle significantly impacts its performance.A numerical investigation was conducted to analyze the influence of the blade wrap angle on flow characteristics and energy distribution of a centrifugal pump evaluated as a low specific speed with a value of 69.This study investigates six impellermodels that possess varying blade wrap angles(95°,105°,115°,125°,135°,and 145°)that were created while maintaining the same volute and other geometrical characteristics.The investigation of energy loss was conducted to evaluate the values of total and entropy generation rates(TEG,EGR).The fluid-structure interaction was considered numerically using the software tools ANSYS Fluent and ANSYSWorkbench.The elastic structural dynamic equation was used to estimate the structural response,while the shear stress transport k–ωturbulence model was utilized for the fluid domain modeling.The findings suggest that the blade wrap angle has a significant influence on the efficiency of the pump.The impeller featuring a blade wrap angle of 145°exhibits higher efficiency,with a notable increase of 3.76%relative to the original model.Variations in the blade wrap angle impact the energy loss,shaft power,and pump head.The model with a 145°angle exhibited a maximum equivalent stress of 14.8MPa and a total deformation of 0.084 mm.The results provide valuable insights into the intricate flow mechanism of the centrifugal pump,particularly when considering various blade wrap angles.

Data-Driven Modeling for Wind Turbine Blade Loads Based on Deep Neural Network

Blades are essential components of wind turbines.Reducing their fatigue loads during operation helps to extend their lifespan,but it is difficult to quickly and accurately calculate the fatigue loads of blades.To solve this problem,this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis,feature selection,and model construction.In the mechanism analysis part,the generation mechanism of blade loads and the load theoretical calculationmethod based on material damage theory are analyzed,and four measurable operating state parameters related to blade loads are screened;in the feature extraction part,15 characteristic indicators of each screened parameter are extracted in the time and frequency domain,and feature selection is completed through correlation analysis with blade loads to determine the input parameters of data-driven modeling;in the model construction part,a deep neural network based on feedforward and feedback propagation is designed to construct the nonlinear coupling relationship between the unit operating parameter characteristics and blade loads.The results show that the proposed method mines the wind turbine operating state characteristics highly correlated with the blade load,such as the standard deviation of wind speed.The model built using these characteristics has reasonable calculation and fitting capabilities for the blade load and shows a better fitting level for untrained out-of-sample data than the traditional scheme.Based on the mean absolute percentage error calculation,the modeling accuracy of the two blade loads can reach more than 90%and 80%,respectively,providing a good foundation for the subsequent optimization control to suppress the blade load.

Effect of Rigid Pitch Motion on Flexible Vibration Characteristics of a Wind Turbine Blade

Adynamic pitch strategy is usually adopted to improve the aerodynamic performance of the blade of awind turbine.The dynamic pitch motion will affect the linear vibration characteristics of the blade.However,these influences have not been studied in previous research.In this paper,the influences of the rigid pitch motion on the linear vibration characteristics of a wind turbine blade are studied.The blade is described as a rotating cantilever beam with an inherent coupled rigid-flexible vibration,where the rigid pitch motion introduces a parametrically excited vibration to the beam.Partial differential equations governing the nonlinear coupled pitch-bend vibration are proposed using the generalized Hamiltonian principle.Natural vibration characteristics of the inherent coupled rigid-flexible system are analyzed based on the combination of the assumed modes method and the multi-scales method.Effects of static pitch angle,rotating speed,and characteristics of harmonic pitch motion on flexible natural frequencies andmode shapes are discussed.It shows that the pitch amplitude has a dramatic influence on the natural frequencies of the blade,while the effects of pitch frequency and pith phase on natural frequencies are little.

PFNA2 versus 95 Degree Condylar Blade Plate in the Management of Unstable Trochanteric Fractures

Purpose: The proximal femoral nail anti-rotation (PFNA) is known to have advantages in enhancing the anchorage ability of internal fixation in elderly unstable osteoporotic intertrochanteric fracture patients. However whether it is superior to condylar blade fixation is not clear. This study aimed to determine which treatment has better clinical outcomes in older patients. Materials and Methods: A total of 86 patients over the age of 60 with unstable trochanteric fractures within the past 3 weeks, were included in this prospective study conducted from June 1, 2018, to May 31, 2021. All the intertrochanteric fractures were classified according to AO/OTA classification. Among them, 44 cases were treated with the Proximal Femoral Nail (PFNA2) with or without an augmentation screw, and 42 cases were treated with the Condylar Blade Plate. In addition, the operative time, intraoperative blood loss, intraoperative and postoperative blood transfusion, postoperative weight-bearing time, hospitalization time, Harris score of hip function, Kyle’s criteria and postoperative complications were compared between the two groups. Results: The mean duration of surgery for the PFN group was 66.8 minutes (on average), whereas for the condylar blade plate group, it was 99.30 minutes (on average). The PFNA2 group experienced less blood loss (average of 80 mL) compared to the condylar blade plate group (average of 120 mL). Union and partial weight-bearing occurred earlier in the PFNA2 group (14.1 weeks and 10.6 weeks, respectively) compared to the Condylar blade plate group (18.7 weeks and 15.8 weeks). In two patients from the PFNA2 group, screw backing out and varus collapse complications were encountered;however, these patients remained asymptomatic and did not require revision surgery. In two other patients, screw cut out and breakage of the nail at the helical screw hole leading to non-union of the proximal femur were observed during the nine-month follow-up, necessitating revision surgery with prosthetic replacement. Among the condylar blade plate group, three patients experienced complications, including blade breakage at the blade and plate junction. In two cases, the fracture united in varus, and in one case, the blade cut through, resulting in non-union of the femoral head, which required revision surgery. According to the Harris hip score and Kyle’s criteria, a good-excellent outcome was observed in 92.85% of cases in the PFNA2 group and 90.90% of cases in the condylar blade plate group. Conclusion: Both the Proximal Femoral Nail A2 and Condylar blade plate are effective implants for the treatment of unstable trochanteric fractures. The intramedullary implant promotes biological healing and allows for early ambulation with minimal complications. Similarly satisfactory restoration of anatomy and favorable radiological and functional results can be achieved with the biological fixation provided by the 95-degree condylar blade plate. However, the use of PFNA2 internal fixation technique has the advantage of less trauma in elderly patients than the 95-degree condylar blade plate.

Assessment of Similitude Behavior in Natural Frequencies of Printed Turbine Blade

Improving structures involves comparing old and new designs on a key parameter.Calculating the percent change in performance is a method to assess.This paper proposes a cost-effective analogy by generating replicas of additive manufactured aluminum alloy(Al Si10Mg)body-centered cubic lattice(BCC)based turbine blade(T106C)with the same in poly-lactic acid(PLA)material and their comparison in the context of percent change for natural frequencies.Initially,a cavity is created inside the turbine blade(hollow blade).Natural frequencies are obtained experimentally and numerically by incorporating BCC at 50%and 80%of the cavity length into the hollow blade for both materials.The cost of manufacturing the metal blades is 90%more than that of the PLA blades.The two material blade designs show a similar percentage variation,as the first-order mode enhancs more than 5%and the second-order mode more than 4%.To observe the behavior in another material,both blades are analyzed numerically with a nickel-based U-500 material,and the same result is achieved,describing that percent change between designs can be verified using the PLA material.

Research on Fatigue Damage Behavior of Main Beam Sub-Structure of Composite Wind Turbine Blade

Given the difficulty in accurately evaluating the fatigue performance of large composite wind turbine blades(referred to as blades),this paper takes the main beam structure of the blade with a rectangular cross-sectionas the simulation object and establishes a composite laminate rectangular beam structure that simultaneouslyincludes the flange,web,and adhesive layer,referred to as the blade main beam sub-structure specimen,throughthe definition of blade sub-structures.This paper examines the progressive damage evolution law of the compositelaminate rectangular beam utilizing an improved 3D Hashin failure criterion,cohesive zone model,B-K failurecriterion,and computer simulation technology.Under static loading,the layup angle of the anti-shear web hasa close relationship with the static load-carrying capacity of the composite laminate rectangular beam;under fatigueloading,the fatigue damage will first occur in the lower flange adhesive area of the whole composite laminaterectangular beam and ultimately result in the fracture failure of the entire structure.These results provide a theoreticalreference and foundation for evaluating and predicting the fatigue performance of the blade main beamstructure and even the full-size blade.