One of the most luminous and mysterious binary star systems, classified as a Luminous Blue Variable (LBV), is known to astronomers as Eta Carinae. Located 7,500 light years away, this system holds a combined mass upwards of 120 solar masses and a total luminosity of over 5 million times greater than the sun, making Eta Car a unique astrophysical laboratory. The incredible mass loss of this system is due to powerful radiation-driven stellar winds propelled by Eta Car's enormous luminosity. In this thesis, the time-dependent, three-dimensional (3D) grid-based adaptive mesh refinement (AMR) hydrodynamics code RAMSES is used to simulate Eta Car binary and its colliding stellar winds around periastron (closest approach). The results of this simulation are presented as slices of density, temperature, and wind velocity centered in the three major coordinate planes xy, xz, and yz. The results show that grid-based AMR codes will ultimately improve upon past simulations performed with SPH methods. Furthermore, by comparing the simulation results to observational data, the various orbital, stellar, and wind parameters of Eta Car can be refined. The RAMSES simulation plots of density, temperature, and wind velocity are of a significantly higher resolution than those of past SPH simulations and, to this benefit, the higher resolution results show in detail the various instabilities (e.g., Kelvin-Helmholtz, thin shell) that arise at the colliding winds region and are important for understanding X-ray observational studies. This simulation presents the idea that grid-based AMR simulations can remain open for future refinement of Eta Car's parameters, including an analysis at apastron, or furthest approach.
|Commitee:||Hamill, Patrick, Romanowsky, Aaron|
|School:||San Jose State University|
|School Location:||United States -- California|
|Source:||MAI 82/1(E), Masters Abstracts International|
|Subjects:||Astrophysics, Astronomy, Physics|
|Keywords:||Adaptive mesh refinement, Carinae, Eta carinae, Massive binary, Massive star, Ramses|
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