Bound-preserving discontinuous Galerkin methods with modified Patankar time integrations for chemical reacting flows

TL;DR

This paper introduces bound-preserving discontinuous Galerkin methods combined with modified Patankar time integrations to accurately simulate chemical reactive flows, ensuring physical constraints like positivity and mass conservation are maintained.

Contribution

The paper develops a novel combination of bound-preserving DG methods with modified Patankar time integrators for stiff reactive flow problems, addressing positivity and conservation simultaneously.

Findings

Successfully preserves positivity of density, energy, and species fractions.

Handles stiff source terms with larger time steps using explicit-implicit methods.

Ensures mass fractions sum to one and remain within physical bounds.

Abstract

In this paper, we develop bound-preserving discontinuous Galerkin (DG) methods for chemical reactive flows. There are several difficulties in constructing suitable numerical schemes. First of all, the density and internal energy are positive, and the mass fraction of each species is between 0 and 1. Secondly, due to the rapid reaction rate, the system may contain stiff sources, and the strong-stability-preserving explicit Runge-Kutta method may result in limited time step sizes. To obtain physically relevant numerical approximations, we apply the bound-preserving technique to the DG methods. For time discretization, we apply the modified Runge-Kutta/multi-step Patankar methods, which are explicit for the flux while implicit for the source. Such methods can handle stiff sources with relatively large time steps, preserve the positivity of the target variables, and keep the summation of the mass fractions to be 1. Finally, it is not straightforward to combine the bound-preserving DG methods and the Patankar time integrations. The positivity-preserving technique for DG method requires positive numerical approximations at the cell interfaces, while Patankar methods can keep the positivity of the pre-selected point-values of the target variables. To match the degree of freedom, we use $Q^k$ polynomials on rectangular meshes for problems in two space dimensions. To evolve in time, we first read the polynomials at the Gaussian points. Then suitable slope limiters can be applied to enforce the positivity of the solutions at those points, which can be preserved by the Patankar methods, leading to positive updated numerical cell averages. In addition, we use another slope limiter to get positive solutions used for the bound-preserving technique for the flux.