Effect of pouring temperature on cooling slope casting of semi-solid Al-Si-Mg alloy

Present trend of semi-solid processing is directed towards rheocasting route which allows manufacturing of near-net-shape cast components directly from the prepared semi-solid slurry. Generation of globular equi-axed grains during solidification of rheocast components, compared to the columnar dendritic structure of conventional casting routes, facilitates the manufacturing of components with improved mechanical properties and structural integrity. In the present investigation, a cooling slope has been designed and indigenously fabricated to produce semi solid slurry of Al-Si-Mg (A356) alloy and successively cast in a metallic mould. The scope of the present work discusses about development of a numerical model to simulate the liquid metal flow through cooling slope using Eulerian two-phase flow approach and to investigate the effect of pouring temperature on cooling slope semi-solid slurry generation process. The two phases considered in the present model are liquid metal and air. Solid fraction evolution of the solidifying melt is tracked at different locations of the cooling slope, following Schiel's equation. The continuity equation, momentum equation and energy equation are solved considering thin wall boundary condition approach. During solidification of the liquid metal, a modified temperature recovery scheme has been employed taking care of the latent heat release and change of fraction of liquid. The results obtained from simulations are compared with experimental findings and good agreement has been found.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.