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Efficiently thermal management in electronic devices calls for creating polymer composites that have superior thermal transport abilities. Compared with single-filler composites, hybrid-filler composites have gained significant attention as they not only utilize the advantages of each filler individually but also allow for the potential cooperation in constructing filler networks between them. Differing from the previous research on hybrid-filler size, geometry, composition, and content and their impact on thermal conductivity (TC) in composites, this study aims to examine the spatial distribution effects of hybrid-fillers. A binary-filler strategy comprising of anisotropic 2D h- BN and 0D isotropic S–Al 2 O 3 has been proposed, with the primary h -BN filler aligned to filly utilize its anisotropy and intrinsic TC properties. Furthermore, positioning a small quantity of S–Al 2 O 3 between adjacent parallel h -BN considerably improves TC in the non-oriented direction of silicon rubber (SiR) composites. A special focus is given to the influence of the interstitial S–Al 2 O 3 filler on the individual through-plane TC (λ ⊥) and in-plane TC (λ ⫽) variation of the binary-filler hybrid SiR composites, which are perpendicular and parallel to the oriented h -BN. When incorporating 40 vol% h -BN and 5 vol% S–Al 2 O 3 , the binary SiR composites exhibit the highest concurrent λ ⫽ and λ ⊥ values of 14.581 and 2.132 W/m·K, respectively. The corresponding effects on the ductility, hardness, and dielectric properties of the SiR composites were explained. Moreover, the use of commercially available TC fillers, along with the effortless preparation method, enables the potential to produce h -BN based TIMs materials on a large scale for thermal management usage. [Display omitted] • A binary filler system consisting in-plane oriented 2D h -BN and 0D isotropic S–Al 2 O 3 is designed. • The addition of S–Al 2 O 3 filler between adjacent parallel h -BN flakes facilitates vertical heat transfer. • The binary structure of h -BN/S–Al 2 O 3 exhibits an anisotropic feature in the filler contact thermal resistances. • The binary filler strategy mitigates the trade-off between in-plane and through-plane thermal conductivity. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) nanosheets, such as graphene and hexagonal boron nitride, are considered as the most promising fillers for enhancing thermal conductivity of polymers and phase-change materials. Nevertheless, the effect of various 2D nanosheets on the effective thermal conductivity of composites is not fully understood, and the corresponding prediction model is still lacking, since numerous influence factors and complex thermal transfer networks are involved. This paper aims to study the macroscopically effective thermal conductivity of the nanosheets-reinforced composites in a systematical way, and develop a robust machine learning based prediction model. To this end, a series of representative volume elements are reconstructed based on the SEM observations of experimental samples, and high-throughput simulations are performed via the updated lattice Boltzmann scheme proposed in our recent work. The effects of shape, size, orientation, intrinsic thermal conductivity, interface thermal resistance, surface coating, and hybrid filling of the 2D nanosheets are clarified. This work could provide a deep insight into the effective thermal conductivity of the nanosheets-reinforced composites, and may offer important guidelines for the custom-design of polymer and phase-change composites with targeted thermal performances. [Display omitted] [ABSTRACT FROM AUTHOR]

The prediction of the prevailing self-heat transfer parameters of a glass/epoxy composite coupon during fatigue testing in general and the distinction between viscoelastic- and frictional crack growth-related energy dissipation in particular, are not trivial problems. This work investigates the feasibility of predicting the convective film coefficient, the total work loss as well as the ratio between viscoelastic and fracture-induced damping from thermal images using Bayesian optimization in conjunction with 3D FE thermal analysis. To this end, glass fiber/epoxy biax coupons are pre-damaged by means of a drop weight impact machine and subsequently tested under uniaxial tension-tension high cycle fatigue conditions. IR images are taken of the self-heating thermal profile at steady-state conditions. Synthetic surface thermal images are generated by numerical thermal analysis of the damage distribution obtained by μ -CT scanning prior to testing. Bayesian optimization of the aforementioned parameters is conducted by minimizing the loss function between the as-measured and the synthetic IR image. The predicted work-loss is consequently validated against the measured hysteretic response, from which a very good agreement is found. [Display omitted] • Thermal heat transfer parameters are predicted via Bayesian optimization (BO). • FEM and BO are combined to find physically meaningful parameters. • The method efficiently handles 3D parameters compared to other approaches. [ABSTRACT FROM AUTHOR]