Computational Astrophysics PhD Projects

Research projects on offer in our Simulations group:

A new cosmological residual distribution hydrodynamical solver

Sadegh Khochfar

This PhD project proposal aims at developing a new beyond the-state-of-the-art hydrodynamical simulation code. The student will be expected to develop code to implement numerical schemes based on the residual distribution method for highly parallel compute architectures. Emphasis will be put on scalability to a large number of compute nodes to allow for the next generation of cosmological simulations.

In current astrophysical simulations, two prevailing numerical methods are the Lagrangian-based Smoothed Particle Hydrodynamics (SPH) and the Eulerian-based structured-mesh hydrodynamics, often with adaptive mesh capabilities. SPH codes have exceptional adaptive spatial resolution capabilities, as the particles naturally sample the denser regions of the fluid flow. This comes at the cost, however, of poor shock resolution and the suppression of instabilities at contact discontinuities when compared to mesh-based codes. Cartesian mesh-based codes capture shocks and entropy mixing better, however, they also suffer drawbacks in resolution (even if using adaptive mesh capabilities) and in poorly resolved bulk flows due to the lack of a Galilean-invariant formulation. As has been noted, many of the drawbacks of both SPH and cartesian mesh-based codes can be eliminated if the mesh is allowed to move with the fluid in an arbitrary Lagrange-Eulerian approach.

Much research has been done to identify a true multi-dimensional upwind scheme over the past few decades. One promising scheme is the residual distribution (RD) method. This method combines many of the advantages of a finite volume method with a genuinely multi-dimensional solution to the hydrodynamical equations. During the project, the student will implement such a hydrodynamical solver and investigate its impact on physical processes in the early Universe.

Under the Cosmology projects see also:

• Lensing and Clustering with the Rubin Observatory - Joe Zuntz

Under the Galaxy Formation and Evolution projects see also:

• The High-Redshift Galaxy Population in Galaxy Formation Simulations - Romeel Dave
• Growing Pains: How do the first galaxies grow? - Sadegh Khochfar
• Machine Learning Galaxy Formation - Sadegh Khochfar