Effective thermal conductivity of lithium multipurpose grease filled with metal particles

Abstract

Two-phase samples were prepared by mixing Fe, Cu, and Al particles (<50 µm) in lithium multipurpose grease with different weight fractions of Fe, Cu, and Al powders. Effective thermal conductivity of these samples has been measured by a laboratory-made thermal conductivity probe as a function of weight fraction of filled metal particles. Grease-Fe, Grease-Cu, and Grease-Al systems showed maximum thermal conductivity enhancement of 35.28%, 72.28%, and 97.40% at weight fraction of 0.3, 0.4, and 0.4 of Fe, Cu, and Al particles, respectively. An artificial neural network approach is used to model the effective thermal conductivity of these samples with three input parameters, viz. thermal conductivity of grease, thermal conductivity of metal particles, and weight fraction of metal particles, respectively. A theoretical prediction was also done using a model developed by Verma et al. Results obtained were compared based on coefficient of determination and mean absolute error between experimental and predicted values of effective thermal conductivity by artificial neural network and theoretical formulation. It is found that artificial neural network approach showed better agreement with the experimental results.

Keywords

Effective thermal conductivity two-phase materials thermal conductivity probe artificial neural network coefficient of determination mean absolute error

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