OpenFOAM is one of the most widely used open-source computational fluid dynamics tools and often employed for chemical engineering applications. However, there is no systematic assessment of OpenFOAM’s numerical accuracy and parallel performance for chemically reacting flows. For the first time, this work provides a direct comparison between OpenFOAM’s built-in flow solvers as well as its reacting flow extension EBIdnsFoam with four other, well established high-fidelity combustion codes. Quantification of OpenFOAM’s numerical accuracy is achieved with a benchmark suite that has recently been established by Abdelsamie et al. (Comput Fluids 223:104935, 2021. https://doi.org/10.1016/j.compfluid.2021.104935) for combustion codes. Fourth-order convergence can be achieved with OpenFOAM’s own cubic interpolation scheme and excellent agreement with other high-fidelity codes is presented for incompressible flows as well as more complex cases including heat conduction and molecular diffusion in multi-component mixtures. In terms of computational performance, the simulation of incompressible non-reacting flows with OpenFOAM is slower than the other codes, but similar performance is achieved for reacting flows with excellent parallel scalability. For the benchmark case of hydrogen flames interacting with a Taylor–Green vortex, differences between low-Mach and compressible solvers are identified which highlight the need for more investigations into reliable benchmarks for reacting flow solvers. The results from this work provide the first contribution of a fully implicit compressible combustion solver to the benchmark suite and are thus valuable to the combustion community. The OpenFOAM cases are publicly available and serve as guide for achieving the highest numerical accuracy as well as a basis for future developments.

LES models for turbulent non-premixed combustion usually require knowledge of the filtered density function of the conserved scalar, and we propose to use a simple top-hat function. Such top-hat distributions were developed as probability density functions for RANS applications in the 1970s but were soon surpassed by the β function. We find that in the context of LES, the top-hat distribution provides an excellent alternative to the now much more common β function. The top-hat function is assessed through a phenomenological analysis of Direct Numerical Simulation (DNS) data from a planar jet and of experimental data from a turbulent opposed jet. The approach is then tested a posteriori for a piloted diffusion flame (Sandia Flame D). Advantages of the top-hat function are the ease of implementation and the reduced dimensionality of look-up tables. The present paper also discusses inconsistencies of sub-grid β-FDFs, the FDFs sensitivity on implicit filtering, and the regime in which a β assumption can be a valid filtered density function for LES.