Hybrid Radiation Hydrodynamics scheme with gravity tree-based adaptive optimization algorithm

TL;DR

This paper introduces a hybrid Radiation-Hydrodynamics scheme coupling SPH and grid-based Monte Carlo Radiative Transfer, utilizing a tree-based algorithm for adaptive optimization that improves computational efficiency while maintaining accuracy.

Contribution

The authors develop a novel tree-based algorithm that adaptively reduces fluid resolution in less affected regions, enhancing the efficiency of coupled RHD simulations without sacrificing accuracy.

Findings

Achieves accurate results consistent with benchmarks.

Provides a significant speed-up proportional to reduced particle-cell mappings.

Enables efficient large-scale RHD modeling with adaptive resolution.

Abstract

Modelling the interaction between ionizing photons emitted from massive stars and their environment is essential to further our understanding of galactic ecosystems. We present a hybrid Radiation-Hydrodynamics (RHD) scheme that couples an SPH code to a grid-based Monte Carlo Radiative Transfer code. The coupling is achieved by using the particle positions as generating sites for a Voronoi grid, and applying a precise mapping of particle-interpolated densities onto the grid cells that ensures mass conservation. The mapping, however, can be computationally infeasible for large numbers of particles. We introduce our tree-based algorithm for optimizing coupled RHD codes. Astrophysical SPH codes typically utilize tree-building procedures to sort particles into hierarchical groups (referred to as nodes) for evaluating self-gravity. Our algorithm adaptively walks the gravity tree and transforms the extracted nodes into pseudo-SPH particles, which we use for the grid construction and mapping. This method allows for the temporary reduction of fluid resolution in regions that are less affected by the radiation. A neighbour-finding scheme is implemented to aid our smoothing length solver for nodes. We show that the use of pseudo-particles produces equally accurate results that agree with benchmarks, and achieves a speed-up that scales with the reduction in the final number of particle-cell pairs being mapped.