Affiliation:
1. School of Materials Science and Engineering, Baise University, Baise 533000, China
2. School of Physics and Electronics, Nanning Normal University, Nanning 530100, China
Abstract
Composite materials and structural optimization are important research topics in heat transfer enhancement. The current evaluation parameter for the conductive heat transfer capability of composites is effective thermal conductivity (ETC); however, this parameter has not been studied or analyzed for its applicability to different heat transfer models and composite structures. In addition, the optimized composite structures of a specific object will vary when different optimization methods and criteria are employed. Therefore, it is necessary to investigate a suitable method and parameter for evaluating the heat transfer capability of optimized composites under different heat transfer models. Therefore, this study analyzes and summarizes three typical conductive heat transfer models: surface-to-surface (S-to-S), volume-to-surface (V-to-S), and volume-to-volume (V-to-V) models. The equivalent thermal conductivity (keq) is proposed to evaluate the conductive heat transfer capability of topology-optimized composite structures under the three models. A validated simulation method is used to obtain the key parameters for calculating keq. The influences of the interfacial thermal resistance and size effect on keq are considered. The results show that the composite structure optimized for the V-to-S and V-to-V models has a keq value of only 79.4 W m−1 K−1 under the S-to-S model. However, the keq values are 233.4 W m−1 K−1 and 240.3 W m−1 K−1 under the V-to-S and V-to-V models, respectively, which are approximately 41% greater than those of the in-parallel structure. It can be demonstrated that keq is more suitable than the ETC for evaluating the V-to-S and V-to-V heat transfer capabilities of composite structures. The proposed keq can serve as a characteristic parameter that is beneficial for heat transfer analysis and composite structural optimization.
Funder
National Natural Science Foundation of China
Science and Technology Program of Guangxi
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