Reheat effect on the improvement in efficiency of the turbine driven by pulse detonation

Due to the strong unsteadiness of pulse detonation,large flow losses are generated when the detonation wave interacts with the turbine blades,resulting in low turbine efficiency.Considering that the flow losses are dissipated into the gas as heat energy,some of them can be recycled during the expansion process in subsequent stages by the reheat effect,which should be helpful to improve the detonationdriven turbine efficiency.Taking this into account,this paper developed a numerical model of the detonation chamber coupled with a two-stage axial turbine,and a stoichiometric hydrogen-air mixture was used.The improvement in turbine efficiency attributable to the reheat effect was calculated by comparing the average efficiency of the stages with the efficiency of the two-stage turbine.The research indicated that the first stage was critical in suppressing the flow unsteadiness caused by pulse detonation,which stabilized the intake condition of the second stage and consequently allowed much of the flow losses from the first stage to be recycled,so that the efficiency of the two-stage turbine was improved.At a 95%confidence level,the efficiency improvement was stable at 4.5%—5.3%,demonstrating that the reheat effect is significant in improving the efficiency of the detonation-driven turbine.

Retraction:Optimized Design of Bio-inspired Wind Turbine Blades

The published article titled“Optimized Design of Bio-inspired Wind Turbine Blades”has been retracted from Fluid Dynamics&Materials Processing.

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.

Structure Optimization of a Tesla Turbine Using an Organic Rankine Cycle Technology

The so-called ORC(Organic Rankine Cycle)heat recovery technology has attracted much attention with regard to medium and low temperature waste heat recovery.In the present study,it is applied to a Tesla turbine.At the same time,the effects of the disc speed,diameter and inter-disc gap on the internal flow field and output power of the turbine are also investigated by means of CFD(Computational Fluid Dynamics)numerical simulation,by which the pressure,velocity,and output efficiency of the internal flow field are obtained under different internal and external conditions.The highest efficiency(66.4%)is obtained for a number of nozzles equal to 4,a disk thickness of 1 mm,and a gap of 1 mm between the disks.The results of the study serve as a theoretical basis for the structural design and optimization of Tesla turbines.

Optimization of a Pipeline-Type Savonius Hydraulic Turbine

This study focuses on a DN50 pipeline-type Savonius hydraulic turbine.The torque variation of the turbine in a rotation cycle is analyzed theoretically in the framework of the plane potential flow theory.Related numerical simulations show that the change in turbine torque is consistent with the theoretical analysis,with the main power zone and the secondary power zone exhibiting a positive torque.In contrast,the primary resistance zone and the secondary resistance zone are characterized by a negative torque.Analytical relationships between the turbine’s internal flow angleθ,the deflector’s inclination angleα0,and the coverage angleαof the power zone are introduced,and a method for calculating the optimal number of blades is proposed to maximize the power zone.Results are presented about performance tests conducted on five groups of hydraulic turbines with the blade number ranging from 3 to 7.Such results indicate that both the turbine’s recovery power and efficiency attain the highest values when the blade number is 4,which is in agreement with the number of blades calculated by the proposed method.Additionally,the study examines the effects of the flow rate on turbine parameters and the projected energy generation and cost savings for a specific pipeline configuration.

Semi-supervised surface defect detection of wind turbine blades with YOLOv4

Timely inspection of defects on the surfaces of wind turbine blades can effectively prevent unpredictable accidents.To this end,this study proposes a semi-supervised object-detection network based on You Only Looking Once version 4(YOLOv4).A semi-supervised structure comprising a generative adversarial network(GAN)was designed to overcome the difficulty in obtaining sufficient samples and sample labeling.In a GAN,the generator is realized by an encoder-decoder network,where the backbone of the encoder is YOLOv4 and the decoder comprises inverse convolutional layers.Partial features from the generator are passed to the defect detection network.Deploying several unlabeled images can significantly improve the generalization and recognition capabilities of defect-detection models.The small-scale object detection capacity of the network can be improved by enhancing essential features in the feature map by adding the concurrent spatial and channel squeeze and excitation(scSE)attention module to the three parts of the YOLOv4 network.A balancing improvement was made to the loss function of YOLOv4 to overcome the imbalance problem of the defective species.The results for both the single-and multi-category defect datasets show that the improved model can make good use of the features of the unlabeled images.The accuracy of wind turbine blade defect detection also has a significant advantage over classical object detection algorithms,including faster R-CNN and DETR.

Influence of Surface Ice Roughness on the Aerodynamic Performance of Wind Turbines

The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines.In particular,two icing processes(frost ice and clear ice)were examined by combining the FENSAP-ICE and FLUENT analysis tools.The ice type on the blade surfaces was predicted by using a multi-time step method.Accordingly,the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated.The results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice.Clear ice has a greater impact on aerodynamics compared to frost ice,while frost ice is significantly influenced by the roughness of its icy surface.

Ice-Induced Vibrational Response of Single-Pile Offshore Wind-Turbine Foundations

Important challenges must be addressed to make wind turbines sustainable renewable energy sources.A typical problem concerns the design of the foundation.If the pile diameter is larger than that of the jacket platform,traditional mechanical models cannot be used.In this study,relying on the seabed soil data of an offshore wind farm,the m-method and the equivalent embedded method are used to address the single-pile wind turbine foundation problem for different pile diameters.An approach to determine the equivalent pile length is also proposed accordingly.The results provide evidence for the effectiveness and reliability of the model based on the equivalent embedded method.

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.

Influence of Trailing-Edge Wear on the Vibrational Behavior of Wind Turbine Blades

To study the impact of the trailing-edge wear on the vibrational behavior of wind-turbine blades,unworn blades and trailing-edge worn blades have been assessed through relevant modal tests.According to these experiments,the natural frequencies of trailing-edge worn blades-1,-2,and-3 increase the most in the second to fourth order,thefifth order increases in the middle,and thefirst order increases the least.The damping ratio data indi-cate that,in general,thefirstfive-order damping ratios of trailing-edge worn blades-1 and trailing-edge worn blades-2 are reduced,and thefirstfive-order damping ratios of trailing-edge worn blades-3 are slightly improved.The mode shape diagram shows that the trailing-edge worn blades-1 and-2 have a large swing in the tip and the blade,whereas the second-and third-order vibration shapes of the trailing edge-worn blade-3 tend to be improved.Overall,all these results reveal that the blade’s mass and the wear area are the main fac-tors affecting the vibration characteristics of wind turbine blades.

Vibration attenuation performance of wind turbine tower using a prestressed tuned mass damper under seismic excitation

With the rapid development of large megawatt wind turbines,the operation environment of wind turbine towers(WTTs)has become increasingly complex.In particular,seismic excitation can create a resonance response and cause excessive vibration of the WTT.To investigate the vibration attenuation performance of the WTT under seismic excitations,a novel passive vibration control device,called a prestressed tuned mass damper(PS-TMD),is presented in this study.First,a mathematical model is established based on structural dynamics under seismic excitation.Then,the mathematical analytical expression of the dynamic coefficient is deduced,and the parameter design method is obtained by system tuning optimization.Next,based on a theoretical analysis and parameter design,the numerical results showed that the PS-TMD was able to effectively mitigate the resonance under the harmonic basal acceleration.Finally,the time-history analysis method is used to verify the effectiveness of the traditional pendulum tuned mass damper(PTMD)and the novel PS-TMD device,and the results indicate that the vibration attenuation performance of the PS-TMD is better than the PTMD.In addition,the PS-TMD avoids the nonlinear effect due to the large oscillation angle,and has the potential to dissipate hysteretic energy under seismic excitation.

Experimental Study on the Sinking and Leveling of A Large-Capacity Offshore Wind Turbine Five-Bucket Foundation in Sand

As offshore wind farms expand into deeper and farther ocean regions and the unit capacity of offshore wind turbines(OWTs)increases,there is a pressing need for a new foundation structure that can accommodate deep-sea conditions and support large capacities while maintaining economical and safe.To meet this goal of integrated transportation and one-step installation,a novel five-bucket jacket foundation(FBJF),with its suction installation and leveling methods in sand,has been proposed,analyzed and experimentally studied.First,seepage failure experiments of the FBJF at various depths were conducted,and a formula for calculating the critical suction of seepage failure suitable for the FBJF in sand was chosen and recommended for use with a range of values for the permeability coefficient ratio.Second,through leveling experiments of the FBJF at different depths,the maximum adjustable leveling angle during the sinking process was defined using seepage failure and the adjustable leveling angle of the foundation as control criteria.Various leveling control strategies were proposed and verified.Finally,an automatic sinking and leveling control system for the FBJF was developed and experimentally verified for feasibility.

Study on the Relationship between Structural Aspects and Aerodynamic Characteristics of Archimedes Spiral Wind Turbines

A combined experimental and numerical research study is conducted to investigate the complex relationship between the structure and the aerodynamic performances of an Archimedes spiral wind turbine(ASWT).Two ASWTs are considered,a prototypical version and an improved version.It is shown that the latter achieves the best aerodynamic performance when the spread angles at the three sets of blades areα_(1)=30°,α_(2)=55°,α3=60°,respectively and the blade thickness is 4 mm.For a velocity V=10 m/s,a tip speed ratio(TSR)=1.58 and 2,the maximum CP values are 0.223 and 0.263 for the prototypical ASWT and improved ASWT,respectively,and the maximum C_(P) enhancement is 17.93%.For V=10 m/s and TSR=2,the CP values of the prototypical ASWT and improved ASWT are 0.225 and 0.263,respectively,with an aerodynamic performance enhancement of 16.88%.Through mutual verification of the test outcomes and numerical results,it is concluded that the proposed approach can effectively lead to aerodynamic performance improvement.

Dynamic Analysis of a 10 MW Floating Offshore Wind Turbine Considering the Tower and Platform Flexibility

Recently,semisubmersible floating offshore wind turbine technologies have received considerable attention.For the coupled simulation of semisubmersible floating offshore wind energy,the platform is usually considered a rigid model,which could affect the calculation accuracy of the dynamic responses.The dynamic responses of a TripleSpar floating offshore wind turbine equipped with a 10 MW offshore wind turbine are discussed herein.The simulation of a floating offshore wind turbine under regular waves,white noise waves,and combined wind-wave conditions is conducted.The effects of the tower and platform flexibility on the motion and force responses of the TripleSpar semisubmersible floating offshore wind turbine are investigated.The results show that the flexibility of the tower and platform can influence the dynamic responses of a TripleSpar semisubmersible floating offshore wind turbine.Considering the flexibility of the tower and platform,the tower and platform pitch motions markedly increased compared with the fully rigid model.Moreover,the force responses,particularly for tower base loads,are considerably influenced by the flexibility of the tower and platform.Thus,the flexibility of the tower and platform for the coupled simulation of floating offshore wind turbines must be appropriately examined.

Turbine Passage Secondary Flow Dynamics and Endwall Heat Transfer Under Different Inflow Turbulence

This study presents endwall hydrodynamics and heat transfer in a linear turbine cascade at Re 5×105 at low and high intensities of turbulence.Results are numerically predicted using the standard SST model and Reθ-γtransition model as well as using the high-resolution LES separately.The major secondary flow components,comprising the horseshoe,corner,and passage vortices are recognized and the impact on heat or mass transfer is investigated.The complicated behavior of turbine passage secondary flow generation and establishment are impacted by the perspective of boundary layer attributes and inflow turbulence.The passage vortex concerning the latest big leading-edge vane is generated by the enlargement of the circulation developed at the first instance adjacent to the pressure side becomes powerful and mixes with other vortex systems during its migration towards the suction side.The study conclusions reveal that substantial enhancements are attained on the endwall surface,for the entire spanwise blade extension on the pressure surface,and in the highly 3-D region close to the endwall on the suction surface.The forecasted suction surface thermal exchange depicts great conformity with the measurement values and precisely reproduces the enhanced thermal exchange owing to the development and lateral distribution of the secondary flows along the midspan of the blade passage downstream.The impacts of the different secondary flow structures on the endwall thermal exchange are described in depth.

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.

Preparation and property of seal coating for gas turbine compressor

Double-layer structure of seal coating which consisted of a Ni5Al bond coating and a Ni25 graphite top coating were prepared on steel substrate of gas turbine compressor cylinder block.Bond coating was prepared by atmospheric plasma spraying and top coating was prepared by flame spraying.The microstructure,mechanical properties and abradability of the coating were characterized by scanning elec-tron microscope(SEM),hardness tester,universal testing machine,thermal shock testing machine and abradability testing machine.The res-ults show that the overall spraying structure of the seal coating is uniform,the nickel metal phase is the skeleton supporting the entire coat-ing,and the coating is well bonded without separation.The seal coating has a bonding strength of not less than 7.7 MPa,excellent thermal stability,and thermal shock resistance cycle numbers at 500℃more than 50;the scratch length,deepest invasion depth and wear amount of the coating increase with rise of test temperature,with almost no coating adhesion,indicating that the seal coating has excellent abradability.

Research on Leading Edge Erosion and Aerodynamic Characteristics of Wind Turbine Blade Airfoil

The effects of the erosion present on the leading edge of a wind turbine airfoil(DU 96-W-180)on its aerodynamic performances have been investigated numerically in the framework of a SST k–ωturbulence model based on the Reynolds Averaged Navier-Stokes equations(RANS).The results indicate that when sand-induced holes and small pits are involved as leading edge wear features,they have a minimal influence on the lift and drag coefficients of the airfoil.However,if delamination occurs in the same airfoil region,it significantly impacts the lift and resistance characteristics of the airfoil.Specifically,as the angle of attack grows,there is a significant decrease in the lift coefficient accompanied by a sharp increase in the drag coefficient.As wear intensifies,these effects gradually increase.Moreover,the leading edge wear can exacerbate flow separation near the trailing edge suction surface of the airfoil and cause forward displacement of the separation point.

Zonal Coupling Analysis Method of Seismic Response of Offshore Monopile Wind Turbine

The seismic safety of offshore wind turbines is an important issue that needs to be solved urgently.Based on a unified computing framework,this paper develops a set of seawater-seabed-wind turbine zoning coupling analysis methods.A 5 MW wind turbine and a site analysis model are established,and a seismic wave is selected to analyze the changes in the seismic response of offshore monopile wind turbines under the change of seawater depth,seabed wave velocity and seismic wave incidence angle.The analysis results show that when the seawater increases to a certain depth,the seismic response of the wind turbine increases.The shear wave velocity of the seabed affects the bending moment and displacement at the bottom of the tower.When the angle of incidence increases,the vertical displacement and the acceleration of the top of the tower increase in varying degrees.

Review of Artificial Neural Networks for Wind Turbine Fatigue Prediction

Wind turbines have emerged as a prominent renewable energy source globally.Efficient monitoring and detection methods are crucial to enhance their operational effectiveness,particularly in identifying fatigue-related issues.This review focuses on leveraging artificial neural networks(ANNs)for wind turbine monitoring and fatigue detection,aiming to provide a valuable reference for researchers in this domain and related areas.Employing various ANN techniques,including General Regression Neural Network(GRNN),Support Vector Machine(SVM),Cuckoo Search Neural Network(CSNN),Backpropagation Neural Network(BPNN),Particle Swarm Optimization Artificial Neural Network(PSO-ANN),Convolutional Neural Network(CNN),and nonlinear autoregressive networks with exogenous inputs(NARX),we investigate the impact of average wind speed on stress transfer function and fatigue damage in wind turbine structures.Our findings indicate significant precision levels exhibited by GRNN and SVM,making them suitable for practical implementation.CSNN demonstrates superiority over BPNN and PSO-ANN in predicting blade fatigue life,showcasing enhanced accuracy,computational speed,precision,and convergence rate towards the global minimum.Furthermore,CNN and NARX models display exceptional accuracy in classification tasks.These results underscore the potential of ANNs in addressing challenges in wind turbine monitoring and fatigue detection.However,it’s important to acknowledge limitations such as data availability and model complexity.Future research should explore integrating real-time data and advanced optimization techniques to improve prediction accuracy and applicability in real-world scenarios.In summary,this review contributes to advancing the understanding of ANNs’efficacy in wind turbine monitoring and fatigue detection,offering insights and methodologies that can inform future research and practical applications in renewable energy systems.