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.

Convergence Analysis of the Numerical Solution for Cathode Design of Aero-engine Blades in Electrochemical Machining

As a main difficult problem encountered in electrochemical machining （ECM）, the cathode design is tackled, at present, with various numerical analysis methods such as finite difference, finite element and boundary element methods. Among them, the finite element method presents more flexibility to deal with the irregularly shaped workpieces. However, it is very difficult to ensure the convergence of finite element numerical approach. This paper proposes an accurate model and a finite element numerical approach of cathode design based on the potential distribution in inter-electrode gap. In order to ensure the convergence of finite element numerical approach and increase the accuracy in cathode design, the cathode shape should be iterated to eliminate the design errors in computational process. Several experiments are conducted to verify the machining accuracy of the designed cathode. The experimental results have proven perfect convergence and good computing accuracy of the proposed finite element numerical approach by the high surface quality and dimensional accuracy of the machined blades.

Aero-engine Blade Fatigue Analysis Based on Nonlinear Continuum Damage Model Using Neural Networks

Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇s sum method is not suitable for aero-engine because of its low accuracy.A back propagation neutral network（BPNN） based on the combination of Levenberg-Marquardt（LM） and finite element method（FEM） is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade.Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve.A nonlinear continuum damage model（CDM）,based on the BPNN with one hidden layer and ten neurons,is built to investigate the nonlinear damage accumulation behavior,in which the results from the tests are used as training set.Comparing with linear models and previous nonlinear models,BPNN has the lowest calculation error in full load range.It has significant accuracy when the load is below 500 MPa.Especially,when the load is 350 MPa,the calculation error of the BPNN is only 0.4%.The accurate model of the blade is built by using 3D coordinate measurement technology.The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min,and the load history is obtained from finite element analysis（FEA）.Then the fatigue life of the compressor blade is predicted by using the BPNN model.The final fatigue life of the aero-engine blade is 6.55 104 cycles（10 916 h） based on the BPNN model,which is effective for the virtual design of aero-engine blade.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

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.

Composite Adaptive Control of Belt Polishing Force for Aero-engine Blade

The existing methods for blade polishing mainly focus on robot polishing and manual grinding.Due to the difficulty in high-precision control of the polishing force,the blade surface precision is very low in robot polishing,in particular,quality of the inlet and exhaust edges can not satisfy the processing requirements.Manual grinding has low efficiency,high labor intensity and unstable processing quality,moreover,the polished surface is vulnerable to burn,and the surface precision and integrity are difficult to ensure.In order to further improve the profile accuracy and surface quality,a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed,which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together.By the mode decision-making mechanism,Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value,and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision.Based on the mathematical model of the force-exerting mechanism,simulation analysis is implemented on DSCAC.Simulation results show that the output polishing force can better track the given signal.Finally,the blade polishing experiments are carried out on the designed polishing equipment.Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility,valve dead-time effect,valve nonlinear flow,cylinder friction,measurement noise and other interference on the control precision of polishing force,which has high control precision,strong robustness,strong anti-interference ability and other advantages compared with MRACFNN.The proposed research achieves high-precision control of the polishing force,effectively improves the blade machining precision and surface consistency,and significantly reduces the surface roughness.

An Inter-Shaft Bearing Fault Diagnosis Dataset from an Aero-Engine System

In this paper,the aero-engine test with inter-shaft bearing fault is carried out,and a dataset is proposed for the first time based on the vibration signal of rotors and casings.First,a test rig based on a real aero-engine is established,driven by motors and equipped with a lubricating system.Then,the aero-engine is disassembled and assembled following the specification process,and the inter-shaft bearing with artificial fault is replaced.Next,the aero-engine test is conducted at 28 groups of high-and low-pressure speeds.Six measuring points are arranged,including two displacement sensors to test the displacement vibration signals of the low-pressure rotor and four acceleration sensors to test the acceleration vibration signals of the casing.The test results are integrated into an inter-shaft bearing fault dataset.Finally,based on the dataset in this paper,frequency spectrum,envelope spectrum,CNN,LSTM,and TST are used for fault diagnosis,and the results are compared with those of CWRU and XJTU datasets.The results show that the characteristic fault frequency cannot be found directly in the spectrum and envelope spectrum corresponding to this paper’s dataset but in CWRU and XJTU datasets.Using CNN,LSTM,and TST for fault diagnosis of the dataset in this paper,the accuracy is 83.13%,85.41%,and 71.07%,respectively,much lower than the diagnosis results of CWRU and XJTU datasets.It can be seen that the dataset in this paper is closer to the actual fault diagnosis situation and is a more challenging dataset.This dataset provides a new benchmark for the validation of fault diagnosis methods.Mendeley data:https://github.com/HouLeiHIT/HIT-dataset.

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.

An Error Area Determination Approach in Machining of Aero-engine Blade

As a result of the recently increasing demands on high-performance aero-engine,the machining accuracy of blade profile is becoming more stringent. However,in the current profile,precision milling,grinding or near-netshape technology has to undergo a tedious iterative error compensation. Thus,if the profile error area and boundary can be determined automatically and quickly,it will help to improve the efficiency of subsequent re-machining correction process. To this end,an error boundary intersection approach is presented aiming at the error area determination of complex profile,including the phaseⅠof cross sectional non-rigid registration based on the minimum error area and the phaseⅡof boundary identification based on triangular meshes intersection. Some practical cases are given to demonstrate the effectiveness and superiority of the proposed approach.

Failure Assessment of Aero-engine Support Structure due to Blade-off

Aero-engine blade-off event could cause serious malfunction and endanger flight safety,which is an important issue widely concerned for a long period.This paper presents a comprehensive review on the regulation requirements,the major research methods and status at home and abroad.Firstly,the relevant certification regulations and standards about aero-engine structure safety due to blade-off event were overviewed and the research gaps between the abroad and the domestic were compared.Then,the simulation and experimental methodologies on aero-engine supporting structures undertake abnormal load due to blade-off event were discussed as major issue.Finally,the safety certification verification technology system for aero-engine support structures during blade-off event was proposed.

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.