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Register a new userSelf-gravitational Magnetohydrodynamics with Adaptive Mesh Refinement for Protostellar Collapse
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Authors
Tomoaki Matsumoto
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A new numerical code, called SFUMATO, for solving self-gravitational magnetohydrodynamics (MHD) problems using adaptive mesh refinement (AMR) is presented. A block-structured grid is adopted as the grid of the AMR hierarchy. The total variation diminishing (TVD) cell-centered scheme is adopted as the MHD solver, with hyperbolic cleaning of divergence error of the magnetic field also implemented. The self-gravity is solved by a multigrid method composed of (1) full multigrid (FMG)-cycle on the AMR hierarchical grids, (2) V-cycle on these grids, and (3) FMG-cycle on the base grid. The multigrid method exhibits spatial second-order accuracy, fast convergence, and scalability. The numerical fluxes are conserved by using a refluxing procedure in both the MHD solver and the multigrid method. The several tests are performed indicating that the solutions are consistent with previously published results.
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Large-scale finite element simulations of complex physical systems governed by partial differential equations crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required. Existing scalable AMR methods make heuristic refinement decisions based on instantaneous error estimation and thus do not aim for long-term optimality over an entire simulation. We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning (RL) to train refinement policies directly from simulation. AMR poses a new problem for RL in that both the state dimension and available action set changes at every step, which we solve by proposing new policy architectures with differing generality and inductive bias. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations. We demonstrate in comprehensive experiments on static function estimation and the advection of different fields that RL policies can be competitive with a widely-used error estimator and generalize to larger, more complex, and unseen test problems.
A computer code is described for the simulation of gravitational lensing data. The code incorporates adaptive mesh refinement in choosing which rays to shoot based on the requirements of the source size, location and surface brightness distribution or to find critical curves/caustics. A variety of source surface brightness models are implemented to represent galaxies and quasar emission regions. The lensing mass can be represented by point masses (stars), smoothed simulation particles, analytic halo models, pixelized mass maps or any combination of these. The deflection and beam distortions (convergence and shear) are calculated by modified tree algorithm when halos, point masses or particles are used and by FFT when mass maps are used. The combination of these methods allow for a very large dynamical range to be represented in a single simulation. Individual images of galaxies can be represented in a simulation that covers many square degrees. For an individual strongly lensed quasar, source sizes from the size of the quasars host galaxy (~ 100 kpc) down to microlensing scales (~ 10^-4 pc) can be probed in a self consistent simulation. Descriptions of various tests of the codes accuracy are given.
In this work, we introduce GRChombo: a new numerical relativity code which incorporates full adaptive mesh refinement (AMR) using block structured Berger-Rigoutsos grid generation. The code supports non-trivial many-boxes-in-many-boxes mesh hierarchies and massive parallelism through the Message Passing Interface (MPI). GRChombo evolves the Einstein equation using the standard BSSN formalism, with an option to turn on CCZ4 constraint damping if required. The AMR capability permits the study of a range of new physics which has previously been computationally infeasible in a full 3+1 setting, whilst also significantly simplifying the process of setting up the mesh for these problems. We show that GRChombo can stably and accurately evolve standard spacetimes such as binary black hole mergers and scalar collapses into black holes, demonstrate the performance characteristics of our code, and discuss various physics problems which stand to benefit from the AMR technique.
We have developed a simulation code with the techniques which enhance both spatial and time resolution of the PM method for which the spatial resolution is restricted by the spacing of structured mesh. The adaptive mesh refinement (AMR) technique subdivides the cells which satisfy the refinement criterion recursively. The hierarchical meshes are maintained by the special data structure and are modified in accordance with the change of particle distribution. In general, as the resolution of the simulation increases, its time step must be shortened and more computational time is required to complete the simulation. Since the AMR enhances the spatial resolution locally, we reduce the time step locally also, instead of shortening it globally. For this purpose we used a technique of hierarchical time steps (HTS) which changes the time step, from particle to particle, depending on the size of the cell in which particles reside. Some test calculations show that our implementation of AMR and HTS is successful. We have performed cosmological simulation runs based on our code and found that many of halo objects have density profiles which are well fitted to the universal profile proposed by Navarro, Frenk, & White (1996) over the entire range of their radius.
Radiative transfer has a strong impact on the collapse and the fragmentation of prestellar dense cores. We present the radiation-hydrodynamics solver we designed for the RAMSES code. The method is designed for astrophysical purposes, and in particular for protostellar collapse. We present the solver, using the co-moving frame to evaluate the radiative quantities. We use the popular flux limited diffusion approximation, under the grey approximation (one group of photon). The solver is based on the second-order Godunov scheme of RAMSES for its hyperbolic part, and on an implicit scheme for the radiation diffusion and the coupling between radiation and matter. We report in details our methodology to integrate the RHD solver into RAMSES. We test successfully the method against several conventional tests. For validation in 3D, we perform calculations of the collapse of an isolated 1 M_sun prestellar dense core, without rotation. We compare successfully the results with previous studies using different models for radiation and hydrodynamics. We have developed a full radiation hydrodynamics solver in the RAMSES code, that handles adaptive mesh refinement grids. The method is a combination of an explicit scheme and an implicit scheme, accurate to the second-order in space. Our method is well suited for star formation purposes. Results of multidimensional dense core collapse calculations with rotation are presented in a companion paper.
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