journal article·2015-12

Numerical investigation on the effective thermal conductivity of plasma sprayed zirconia coatings

X.Q. Jin, C.Y. Zhao

Ceramics International· Volume 41· Issue 10
Publisher page

Abstract

Abstract Due to the complex microstructure of the plasma sprayed yttria-stabilized zirconia (YSZ) coatings, there is still no good numerical method to establish the relationship between the effective thermal conductivity and the microstructure. In this paper, two different approaches have been developed to investigate the effective thermal conductivity of the thermal barrier coatings (TBCs), which are both based on the Lattice Boltzmann method (LBM). One approach employs the quartet structure generation set (QSGS) method to construct the isotropic or anisotropic microstructure without experimental techniques. In this method, through changing the parameters of QSGS algorithm, it can estimate the effective thermal conductivity of TBCs fabricated by different plasma spray technologies. The other method is related to the real microstructure of TBCs, which employs the image analysis software to convert the real experimental micrograph obtained using a scanning electron microscope (SEM) into the binary image. Although the first method is more available and practical without analyzing the microstructural image, the second method can estimate the effective thermal conductivity for the plasma sprayed coatings more precisely. However, comparing the predicted results and the experimental data, there are some limitations in both methods. Additionally, the effective thermal conductivity of gradient TBCs is also analyzed in this paper. The results will provide a useful guide for predicting the effective thermal conductivity of plasma sprayed coatings and a strong foundation for designing the gas turbine engines.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Lattice Boltzmann Simulation Studies · Nanofluid Flow and Heat Transfer · Heat and Mass Transfer in Porous Media

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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