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.

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.

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.

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.

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.

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.

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.

Effect of pitched short blades on the flow characteristics in a stirred tank with long-short blades impeller

This work focuses on the design improvement of the long-short blades(LSB)impeller by using pitched short blades(SBs)to regulate the flow field in the stirred vessel.After mesh size evaluation and velocity field validation by the particle image velocimetry,large eddy simulation method coupled with sliding mesh approach was used to study the effect of the pitched SBs on the flow characteristics.We changed the inclined angles of the SBs from 30°to 60°and compared the flow characteristics when the impeller was operated in the down-pumping and up-pumping modes.In the case of down-pumping mode,the power number is relatively smaller and vortexes below the SBs are suppressed,leading to turbulence intensification in the bottom of the vessel.Whereas in the case of up-pumping mode,the axial flow rate in the center increased significantly with bigger power number,resulting in more efficient mass exchange between the axial and radial flows in the whole vessel.The LSB with 45°inclined angle of the SBs in the up-pumping mode has the most uniform distributions of flow field and turbulent kinetic energy compared with other impeller configurations.

Foreign Object Damage to Fan Rotor Blades of Aeroengine Part II: Numerical Simulation of Bird Impact

Bird impact is one of the most dangerous threats to flight safety. The consequences of bird impact can be severe and, therefore, the aircraft components have to be certified for a proven level of bird impact resistance before being put into service. The fan rotor blades of aeroengine are the components being easily impacted by birds. It is necessary to ensure that the fan rotor blades should have adequate resistance against the bird impact, to reduce the flying accidents caused by bird impacts. Using the contacting-impacting algorithm, the numerical simulation is carded out to simulate bird impact. A three-blade computational model is set up for the fan rotor blade having shrouds. The transient response curves of the points corresponding to measured points in experiments, displacements and equivalent stresses on the blades are obtained during the simulation. From the comparison of the transient response curves obtained from numerical simulation with that obtained from experiments, it can be found that the variations in measured points and the corresponding points of simulation are basically the same. The deforming process, the maximum displacements and the maximum equivalent stresses on blades are analyzed. The numerical simulation verifies and complements the experiment results.

Determination of wax pattern die profile for investment casting of turbine blades

In order to conform to dimensional tolerances, an efficient numerical method, displacement iterative compensation method, based on finite element methodology （FEM） was presented for the wax pattern die profile design of turbine blades. Casting shrinkages at different positions of the blade which was considered nonlinear thermo-mechanical casting deformations were calculated. Based on the displacement iterative compensation method proposed, the optimized wax pattern die profile can be established. For a A356 alloy blade, substantial reduction in dimensional and shape tolerances was achieved with the developed die shape optimization system. Numerical simulation result obtained by the proposed method shows a good agreement with the result measured experimentally. After four times iterations, compared with the CAD model of turbine blade, the total form error decreases to 0.001 978 mm from the orevious 0.515 815 mm.

Foreign Object Damage to Fan Rotor Blades of Aeroengine Part I:Experimental Study of Bird Impact

The conditions of experiment for bird impact to blades have been improved. The experiment of bird impact to the fan rotor blades of an aeroengine is carried out. Through analyzing the transient state response of blades impacted by bird and the change of blade profile before and after the impact, the anti-bird impact performance of blades in the first fan rotor is verified. The basis of anti-foreign object damage design for the fan rotor blades of an aeroengine is provided.

Videometric research on deformation measurement of large-scale wind turbine blades

Utilization of wind energy is a promising way to generate power,and wind turbine blades play a key role in collecting the wind energy effectively.This paper attempts to measure the deformation parameter of wind turbine blades in mechanics experiments using a videometric method. In view that the blades experience small buckling deformation and large integral deformation simultaneously, we proposed a parallel network measurement(PNM) method including the key techniques such as camera network construction,camera calibration,distortion correction,the semi-automatic high-precision extraction of targets,coordinate systems unification,and bundle adjustment,etc. The relatively convenient construction method of the measuring system can provide an abundant measuring content,a wide measuring range and post processing.The experimental results show that the accuracy of the integral deformation measurement is higher than 0.5 mm and that of the buckling deformation measurement higher than 0.1mm.

The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

Genetic variation in LBL1 contributes to depth of leaf blades lobes between cotton subspecies, Gossypium barbadense and Gossypium hirsutum

Leaf is a essential part of the plants for photosynthetic activities which mainly economize the resources for boll heath. Significant variations of leaf shapes across the Gossypium sp. considerably influence the infiltration of sunlight for photosynthesis. To understand the genetic variants and molecular processes underlying for cotton leaf shape, we used F2 population derived from upland cotton genotype P30A （shallow-lobed leaf） and sea-island cotton genotype ISR （deep-lobed leaf） to map leaf deep lobed phenotype controlling genes LBL1 and LBL2. Genetic analysis and localization results have unmasked the position and interaction between both loci of LBL1 and LBL2, and revealed the co-dominance impact of the genes in regulating depth of leaf blades lobes in cotton. LBL1 had been described as a main gene and member of transcription factor family leucine zipper （HD-ZIPI） from a class I homologous domain factor Gorai.OO2G244000. The qRT-PCR results elaborated the continuous change in expression level of LBL1 at different growth stages and leaf parts of cotton. Higher expression level was observed in mature large leaves followed by medium and young leaves respectively. For further confirmation, plants were tested from hormonal induction treatments, which explained that LBL 1 expression was influenced by hormonal signaling. Moreover, the highest expression level was detected in brassinolides （BR） treatment as compared to other hormones, and this hormone plays an important role in the process of leaf blade lobed formation.

Study on the solid-liquid suspension behavior in a tank stirred by the long-short blades impeller

We investigated the solid–liquid suspension characteristics in the tank with a liquid height/tank diameter ratio of 1.5 stirred by a novel long-short blades(LSB) impeller by the Euler granular flow model coupled with the standard k–ε turbulence model. After validation of the local solid holdup by experiments,numerical predictions have been successfully used to explain the influences of impeller rotating speed,particle density, particle size, liquid viscosity and initial solid loading on the solid suspension behavior,i.e. smaller particles with lower density are more likely to be suspended evenly in the liquid with higher liquid viscosity. At a low impeller rotating speed(N), increase in N leads to an obvious improvement in the solid distribution homogeneity. Moreover, the proposed LSB impeller has obvious advantages in the uniform distribution of the solid particles compared with single Rushton turbine(RT), dual RT impellers or CBY hydrofoil impeller under the same power consumption.

Enhancing the surface hardness and roughness of engine blades using the shot peening process

The effects of shot peening on the mechanical properties of steel 1070 were studied to enhance the material’s properties and surface characteristics.In this study,pressure and exposure time were the main parameters governing surface hardness and surface roughness.The optimal time duration and pressure were determined after several experimental trials.Changes in hardness and surface roughness were monitored as the pressure of the shot and the exposure time were varied.Furthermore,the microstructure was evaluated by scanning electron microscopy(SEM)and the images were enhanced by image processing techniques to evaluate the surface changes.Pareto charts were constructed to estimate the effects of pressure and time on both surface hardness and surface roughness.The novelty of this study is the concentration on engine blades which are frequently used in aircrafts to determine the optimal time–pressure combination for shot peening to achieve suitable mechanical and surface properties.The results show that shot peening pressure(up to 482.6 kPa for 7 min)has positive effect on enhancing the surface and mechanical properties for steel 1070 blades;however,an increase in either pressure or time above that level adversely affected both surface hardness and surface roughness.