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.

Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

Solid solution strengthening(SSS)is one of the main contributions to the desired tensile properties of nickel-based superalloys for turbine blades and disks.The value of SSS can be calculated by using Fleischer’s and Labusch’s theories,while the model parameters are incorporated without fitting to experimental data of complex alloys.In thiswork,four diffusionmultiples consisting of multicomponent alloys and pure Niare prepared and characterized.The composition and microhardness of singleγphase regions in samples are used to quantify the SSS.Then,Fleischer’s and Labusch’s theories are examined based on high-throughput experiments,respectively.The fitted solid solution coefficients are obtained based on Labusch’s theory and experimental data,indicating higher accuracy.Furthermore,six machine learning algorithms are established,providing a more accurate prediction compared with traditional physical models and fitted physical models.The results show that the coupling of highthroughput experiments and machine learning has great potential in the field of performance prediction and alloy design.

Comparative study on microstructure evolution and failure mechanisms of ordinary and refurbished EB-PVD TBC under cyclic oxidation

Refurbishment of thermal barrier coating(TBC)has become a valuable technique to prolong the service life of high-temperature components.This study investigates the effect of the refurbishment process(coating removal and recoating)on the microstructure evolution and physical properties of TBC,including oxidation characteristics,element diffusion behavior,and crack failure mechanisms.The results showed that a certain amount of interdiffusion zone(IDZ)with Cr-rich would be retained in DD6 superalloy substrate after coating removal.The microstructure of the refurbished specimens showed equiaxedβ-NiAl phases,while the ordinary specimens have elongated grain shapes with a high aspect ratio.Moreover,mixed oxides in the refurbished TBC specimens were earlier observed during cyclic oxidation,with a greater thickness compared to ordinary TBC,due to the influence of BC layer phase sizes.The growth mechanism of thermally grown oxide(TGO-Al_(2)O_(3)layer)in the refurbished TBC specimens was also different,resulting from the different mechanisms of mixed oxides growth.Furthermore,under cyclic oxidation with water quenching at 1100℃,the cracks in the refurbished specimen tend to occur in the mixed oxides layer,while the cracks in the ordinary specimen occur in the top coat(TC)layer,attributing to the earlier and thicker mixed oxides layer formed in refurbished specimens.

Experimental and numerical investigation on the thermal and mechanical behaviours of thermal barrier coatings exposed to CMAS corrosion

Calcium-magnesium-alumino-silicate(CMAS)corrosion is a critical factor which causes the failure of thermal barrier coating(TBC).CMAS attack significantly alters the temperature and stress fields in TBC,resulting in their delamination or spallation.In this work,the evolution process of TBC prepared by suspension plasma spraying(SPS)under CMAS attack is investigated.The CMAS corrosion leads to the formation of the reaction layer and subsequent bending of TBC.Based on the observations,a corrosion model is proposed to describe the generation and evolution of the reaction layer and bending of TBC.Then,numerical simulations are performed to investigate the corrosion process of free-standing TBC and the complete TBC system under CMAS attack.The corrosion model constructs a bridge for connecting two numerical models.The results show that the CMAS corrosion has a significant influence on the stress field,such as the peak stress,whereas it has little influence on the steady-state temperature field.The peak of stress increases with holding time,which increases the risk of the rupture of TBC.The Mises stress increases nonlinearly along the thick direction of the reaction layer.Furthermore,in the traditional failure zone,such as the interface of the top coat and bond coat,the stress obviously changes during CMAS corrosion.