Accelerating reactive-flow simulations using vectorized chemistry integration

TL;DR

This paper introduces a vectorized chemistry integration method for reactive-flow simulations that significantly accelerates computation, achieving up to 35 times faster performance while maintaining high accuracy.

Contribution

The authors developed and integrated a vectorized chemistry solver into OpenFOAM, demonstrating substantial speedups over traditional methods in reactive-flow simulations.

Findings

Up to 35x faster chemistry integration with high accuracy.

Effective vectorized algorithms outperform standard solvers.

Validated across various chemical models and turbulent flow simulations.

Abstract

The high cost of chemistry integration is a significant computational bottleneck for realistic reactive-flow simulations using operator splitting. Here we present a methodology to accelerate the solution of the chemical kinetic ordinary differential equations using single-instruction, multiple-data vector processing on CPUs using the OpenCL framework. First, we compared several vectorized integration algorithms using chemical kinetic source terms and analytical Jacobians from the pyJac software against a widely used integration code, CVODEs. Next, we extended the OpenFOAM computational fluid dynamics library to incorporate the vectorized solvers, and we compared the accuracy of a fourth-order linearly implicit integrator -- both in vectorized form and a corresponding method native to OpenFOAM -- with the community standard chemical kinetics library Cantera. We then applied our methodology to a variety of chemical kinetic models, turbulent intensities, and simulation scales to examine a range of engineering and scientific scale problems, including (pseudo) steady-state as well as time-dependent Reynolds-averaged Navier--Stokes simulations of the Sandia flame D and the Volvo Flygmotor bluff-body stabilized, premixed flame. Subsequently, we compared the performance of the vectorized and native OpenFOAM integrators over the studied models and simulations and found that our vectorized approach performs up to 33--35x faster than the native OpenFOAM solver with high accuracy.