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The rate of melting of a solid and the rate of deformation of the resulting melt due to capillary forces are comparable in additive manufacturing applications. This dynamic structural change of a melting solid is extremely challenging to study experimentally. Using meshless numerical simulations we show the influence of the flow of the melt on the heat transfer and resulting phase change. We introduce an accurate and robust Incompressible Smoothed Particle Hydrodynamics method to simulate melting of solids and the ensuing fluid-solid interaction. We present validation for the heat transfer across free surface and the melting interface evolution, separately. We then present two applications for this coupled multiphysics simulation method---the study of rounding of an arbitrarily shaped particle during melting and the non-linear structural evolution of three spheres undergoing agglomeration. In both the studies we use realistic transport and thermal properties for the materials so as to demonstrate readiness of the method for solving engineering problems in additive manufacturing.
Fast thermalization and a strong buildup of elliptic flow of QCD matter as found at RHIC are understood as the consequence of perturbative QCD (pQCD) interactions within the 3+1 dimensional parton cascade BAMPS. The main contributions stem from pQCD
We study the melting dynamics of large ice balls in a turbulent von Karman flow at very high Reynolds number. Using an optical shadowgraphy setup, we record the time evolution of particle sizes. We study the heat transfer as a function of the particl
Grid based fluid simulation methods are not able to monolithically capture complex non-linear dynamics like the rupture of a dynamic liquid bridge between freely colliding solids, an exemplary scenario of capillary forces competing with inertial forc
The k-space polarization structure and its strain response in SrTiO3 with rotational instability are studied using a combination of first-principles density functional calculations, modern theory of polarization, and analytic Wannier-function formula
We develop efficient, accurate, transferable, and interpretable machine learning force fields for Au nanoparticles, based on data gathered from Density Functional Theory calculations. We then use them to investigate the thermodynamic stability of Au