Roohollah Askari

Thermal Conduction in Deforming Isotropic and Anisotropic Granular Porous Media with Rough Grain Surface

AbstractResistance to the heat flow in solid–solid contact areas plays a fundamental role in heat transfer in unconsolidated porous materials. In the present work, we study thermal conduction in granular porous media that undergo deformation due to an external compressing pressure. The media’s grains have rough surface, with the roughness profile following the statistics of self-affine fractals that have been shown to be abundant in natural porous media. We utilize a fractal contact model of rough surfaces in order to estimate the deformation of the contact areas, which is a function of roughness fractal parameters, the grains’ Young modulus, and the compressing pressure. For porous media saturated by a single fluid, the effects of various factors, such as the porosity, the grains’ overlap (consolidation), and shapes (circular vs. elliptical), are all studied. Increasing the compressing pressure enhances heat transfer due to deformation of the rough surface of the gains. The thermal conductivity of the medium is strongly affected by the porosity, when the grains’ conductivity is much larger than that of the fluid that saturates the pore space. Furthermore, we show that thermal anisotropy is a decreasing function of roughness deformation. In other words, granular media with rougher grains exhibit larger anisotropy as measured by the ratio of the directional thermal conductivities. Whereas in one type of granular media the anisotropy eventually vanishes at very high compressing pressure, it persists in a second model of anisotropic media that we study.

Accurate Estimation of the Group Velocity of Surface Waves Using Sparse S-transform and Sparse Slant-stacking

Surface wave group velocity is an important property by which we can obtain an S-wave velocity model of subsurface through an inverse procedure. The S- wave velocity modeling using the group velocity has some advantages over the phase velocity because it does not require an estimation of the initial phase or to have a dense array of geophones. In addition, the group velocity estimation is not distorted when the geophone interval is significant. However, uncertainties associated with the transformation by which the group velocity is calculated might introduce some errors to the estimated group velocity. In this study, we introduce a new approach for the estimation the group velocity of the surface waves using the sparse S-transform and sparse slant-stacking that is based on the proximal forward-backward splitting algorithm. Compare to the conventional methods for the estimation of the group velocity using the generalized S-transform, it yields a more accurate estimation of the group velocity. We demonstrate the robustness of our method by synthetic and field data examples.