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Precipitation hardening of Al alloys is an essential aging process for industrial applications demanding lightweight and high-mechanical strength materials. However, this process drastically reduces the thermal conductivity of the Al alloy owing to the formation of intermetallic precipitates. Because the growth of the intermetallic precipitates is a complicated process involving the diffusion of alloying elements as well as phase transformation, the prediction of the thermal conductivity during the precipitation process has not been accomplished. Herein, an accurate prediction method for the effective thermal conductivity of Al alloys during the precipitation process is presented. Interestingly, the study finds that the inhomogeneous distribution of the composition in the Al solid solution generates a considerably inhomogeneous distribution of the thermal conductivity around the precipitates; consequently, the accuracy of our prediction is 30% higher than that in previous studies.

The practical application of thermally enhanced nanofluids in automotive industries for higher energy efficiency requires that nanoparticles be buoyant, leading to Brownian motion in the base fluid without aggregation. Numerous studies have reported the long-term stability of dispersions resulting from steric hindrance and an affinity for the surrounding base fluid via modification of nanoparticles with functional groups. In this study, we performed molecular dynamics simulations of nanofluids containing a single graphene sheet with various functional groups to investigate the influence of affinity, as well as particle-size, on the behavior of the graphene sheet. Using the concept of the speed of nanoparticle, we quantitatively evaluated the dependence of behavior on affinity to investigate whether having more functional groups with more attractive interactions has an impact on the stable dispersion in nanofluid. In addition, the simulation results for the particle-size effect revealed that the larger nanoparticles produced more stable dispersion in nanofluid. We concluded that the behavior of the graphene sheet depends on a combination of two factors: different charge assigned to atoms due to a nitrogen atom, and the difference in mass, which influences the speed of the nanoparticles.

Nanofluids are candidate materials for thermal management of heat transfer equipment. Practical applications of thermally enhanced nanofluids contribute to the reduction of weight of systems, leading to improved energy efficiency. Microsize particles sink into the systems because of gravity, therefore rendering the addition meaningless in terms of improving thermal properties. However, nanoparticles can be buoyant, leading to Brownian motion in the fluid, when they do not aggregate with each other. The most important factor in nanofluids is long-term stability of the dispersion in the fluid. Numerous studies have reported the dispersion stability; functional groups attached to nanoparticles play a role in causing steric hindrance and have an affinity for the surrounding fluid, resulting in preserving the dispersion. We investigate the structural effects on dispersion by molecular dynamics simulations of nanofluid containing graphene sheets with functional groups of varying lengths at the surface. ...

Nanofluids with enhanced thermal properties are candidates for thermal management in automotive systems, with scope for improving energy efficiency. In particular, many studies have reported on dispersions of nanoparticles with long-term stability in the base fluid, with qualitative evaluations of the dispersion stability via either the naked eye or optical instruments. Additives such as surfactants can be used to enhance the dispersion of nanoparticles; however, this may diminish their intrinsic thermal properties. Here, we describe molecular dynamics simulations of nanofluids containing graphene sheets dispersed in ethylene glycol and water. We go on to suggest a quantitative evaluation method for the degree of dispersion, based on the ratio of the total number of nanoparticles to the number of clustered nanoparticles. Moreover, we investigate the effects of functional groups on the surface of graphene, which are expected to improve the dispersion without requiring additives such as surfactants due to steric hindrance and chemical affinity for the surrounding fluid. We find that, for pure graphene, the degree of dispersion decreased as the quantity of graphene sheets increased, which is attributed to an increased probability of aggregation at higher loadings; however, the presence of functional groups inhibited the graphene sheets from forming aggregates.