Such composites are likely to have anisotropic microstructures which bring new challenges to thermal conductivity simulation but also significant potential for improvement in the thermal performance.
The thermal conductivity simulation framework is validated against experimental measurements on two highly different porous materials: a low-porosity granular sintered glass filter and a highly-porous cellular-granular acoustic absorber.
Note that there are two competing groups that show significantly different results for the conductivity measurement of hydrous olivine, and therefore, selection of the laboratory model leads different impacts of water on the conductivity simulation.
Our work shows that a successful model is strongly controlled by choice of hydraulic conductivity (for simulations in this study, 70 m day−1 was optimal), the ratio of kariz hydraulic conductivity to aquifer conductivity (107 was optimal in this study), as well as the length of the kariz in contact with the water table.
The simplified thermal conductivity model of the pulsating heat pipe is introduced in numerical simulation.
Recent advances relevant to such developments include: quantum chemistry including continuum solvation and force field embedding, de novo force fields to describe phase transitions, molecular dynamics (MD) including continuum solvent, non equilibrium MD for rheology and thermal conductivity and mesoscale simulations.
The FEA results demonstrate that (i) the overall effective thermal conductivity from direct simulations is comparable to the results estimated by experimental measurement, and are in the order of 10−1 W m−1 K−1 and (ii) thermal transport through fluid-saturated β-SiC foams depends on the solid-to-fluid conductivity ratio.
To check the determined thermal conductivity value, last simulations are performed for each pattern; results are given in Figure 9.
In order to understand the effects of particle heat transport and coagulation of particles on thermal conductivity of nanofluids, simulations were performed for Cu(100 nm)/DIW nanofluids by neglecting two-way temperature coupling (q2w) and van der Waals interaction force (FVi) one at a time.
The results for each pattern are given in Figure 8. Figure 8 Alpha coefficient for different thermal conductivities (dot) versus the porous silicon thermal conductivity set in simulation.
