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Register a new userAn Extension of the Athena++ Framework for General Equations of State
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Matthew S. B. Coleman
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We present modifications to the Athena++ framework to enable use of general equations of state (EOS). Part of our motivation for doing so is to model transient astrophysics phenomena, as these types of events are often not well approximated by an ideal gas. This necessitated changes to the Riemann solvers implemented in Athena++. We discuss the adjustments made to the HLLC, and HLLD solvers and EOS calls required for arbitrary EOS. We demonstrate the reliability of our code in a number of tests which utilize a relatively simple, but non-trivial EOS based on hydrogen ionization, appropriate for the transition from atomic to ionized hydrogen. Additionally, we perform tests using an electron-positron Helmholtz EOS, appropriate for regimes where nuclear statistical equilibrium is a good approximation. These new complex EOS tests overall show that our modifications to Athena++ accurately solve the Riemann problem with linear convergence and linear-wave tests with quadratic convergence. We provide our test solutions as a means to check the accuracy of other hydrodynamic codes. Our tests and additions to Athena++ will enable further research into (magneto)hydrodynamic problems where realistic treatments of the EOS are required.
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The design and implementation of a new framework for adaptive mesh refinement (AMR) calculations is described. It is intended primarily for applications in astrophysical fluid dynamics, but its flexible and modular design enables its use for a wide variety of physics. The framework works with both uniform and nonuniform grids in Cartesian and curvilinear coordinate systems. It adopts a dynamic execution model based on a simple design called a task list that improves parallel performance by overlapping communication and computation, simplifies the inclusion of a diverse range of physics, and even enables multiphysics models involving different physics in different regions of the calculation. We describe physics modules implemented in this framework for both non-relativistic and relativistic magnetohydrodynamics (MHD). These modules adopt mature and robust algorithms originally developed for the Athena MHD code and incorporate new extensions: support for curvilinear coordinates, higher-order time integrators, more realistic physics such as a general equation of state, and diffusion terms that can be integrated with super-time-stepping algorithms. The modules show excellent performance and scaling, with well over 80% parallel efficiency on over half a million threads. The source code has been made publicly available.
We present a co-scaling grid formalism and its implementation in the magnetohydrodynamics code Athena++. The formalism relies on flow symmetries in astrophysical problems involving expansion, contraction, and center-of-mass motion. The grid is evolved at the same time order as the fluid variables. The user specifies grid evolution laws, which can be independent of the fluid motion. Applying our implementation to standard hydrodynamic test cases leads to improved results and higher efficiency, compared to the fixed-grid solutions.
We make a brief historical review to the moment model reduction to the kinetic equations, particularly the Grads moment method for Boltzmann equation. The focus is on the hyperbolicity of the reduced model, which is essential to the existence of its classical solution as a Cauchy problem. The theory of the framework we developed in last years is then introduced, which may preserve the hyperbolic nature of the kinetic equations with high universality. Some lastest progress on the comparison between models with/without hyperbolicity is presented to validate the hyperbolic moment models for rarefied gases.
The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems show that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.
Performing a stable, long duration simulation of driven MHD turbulence with a high thermal Mach number and a strong initial magnetic field is a challenge to high-order Godunov ideal MHD schemes because of the difficulty in guaranteeing positivity of the density and pressure. We have implemented a robust combination of reconstruction schemes, Riemann solvers, limiters, and Constrained Transport EMF averaging schemes that can meet this challenge, and using this strategy, we have developed a new Adaptive Mesh Refinement (AMR) MHD module of the ORION2 code. We investigate the effects of AMR on several statistical properties of a turbulent ideal MHD system with a thermal Mach number of 10 and a plasma $beta_0$ of 0.1 as initial conditions; our code is shown to be stable for simulations with higher Mach numbers ($M_rms = 17.3$) and smaller plasma beta ($beta_0 = 0.0067$) as well. Our results show that the quality of the turbulence simulation is generally related to the volume-averaged refinement. Our AMR simulations show that the turbulent dissipation coefficient for supersonic MHD turbulence is about 0.5, in agreement with unigrid simulations.
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