1,475 research outputs found
Turbulent entrainment origin of protostellar outflows
Protostellar outflow is a prominent process that accompanies the formation of
stars. It is generally agreed that wide-angled protostellar outflows come from
the interaction between the wind from a forming star and the ambient gas.
However, it is still unclear how the interaction takes place. In this work, we
theoretically investigate the possibility that the outflow results from
interaction between the wind and the ambient gas in the form of turbulent
entrainment. In contrast to the previous models, turbulent motion of the
ambient gas around the protostar is taken into account. In our model, the
ram-pressure of the wind balances the turbulent ram-pressure of the ambient
gas, and the outflow consists of the ambient gas entrained by the wind. The
calculated outflow from our modelling exhibits a conical shape. The total mass
of the outflow is determined by the turbulent velocity of the envelope as well
as the outflow age, and the velocity of the outflow is several times higher
than the velocity dispersion of the ambient gas. The outflow opening angle
increases with the strength of the wind and decreases with the increasing
ambient gas turbulence. The outflow exhibits a broad line width at every
position. We propose that the turbulent entrainment process, which happens
ubiquitously in nature, plays a universal role in shaping protostellar
outflows.Comment: 15 pages, accepted for publication in A&
Model Selection Using Gaussian Mixture Models and Parallel Computing
In recent years, model selection methods have seen significant advancement, but improvements have tended to be bench marked on its efficiency. An effective model selection system requires a robust feature extraction module. A model selection system is developed by using Finite Multivariate Generalized Gaussian Mixture Model, which organize data points to clusters. Clustering is basically to assign data set into different groups based on their similarity. In this model, expectation maximization method is used to calculate the distance from each point to their dummy center point, where center point will be changing with the process of simulation to get the best fitting results. Parallel computing is utilized to accelerate simulation process. The performance of the developed model is studied through experimental evaluation with ten thousands data points and identification accuracy. The system still can be improved by a new algorithm to separate the cluster. Performance evaluations will be investigated and compared
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