Accelerating self-gravitating hydrodynamics simulations with adaptive force updates

Many astrophysical hydrodynamics simulations must account for gravity, and evaluating the gravitational field at the positions of all resolution elements can incur significant cost. Typical algorithms update the gravitational field at the position of each resolution element every time the element is updated hydrodynamically, but the actual required update frequencies for hydrodynamics and gravity can be different in general. We show that the gravity calculation in hydrodynamics simulations can be optimized by only updating gravity on a time-scale dictated by the already determined maximum time-step for accurate gravity integration Delta t(grav), while staying well within the typical error budget of hydro schemes and gravity solvers. Our implementation in the GIZMO code uses the time-scale derived from the tidal tensor t(tidal) = parallel to T parallel to(-1/2) to determine Delta t(grav) and the force update frequency in turn, and uses the rate of change of acceleration evaluated by the gravity solver to construct a predictor of the acceleration for use between updates. We test the scheme on standard self-gravitating hydrodynamics test problems, finding solutions very close to the standard scheme while evaluating far fewer gravity forces, optimizing the simulations. We also demonstrate a similar to 70 per cent speed-up in an example simulation of a giant molecular cloud. In general, this scheme introduces a new tunable parameter for obtaining an optimal compromise between accuracy and computational cost, in conjunction with, e.g. time-step tolerance, numerical resolution, and gravity solver tolerance.

Automated summary

This study shows that optimizing gravity calculations in hydrodynamic simulations by updating gravity only on a specific time scale can significantly reduce the evaluation frequency of gravity forces while maintaining simulation accuracy and efficiency. Testing on standard problems and a simulation of a giant molecular cloud demonstrates close solutions to the traditional approach and around a 70% speed-up.