Consistent submodel coupling in hybrid particle/finite volume algorithms for zone-adaptive modelling of turbulent reactive flows

Abstract

A hybrid particle/finite volume algorithm has been formulated for zone-adaptive modelling of turbulent reactive flows to achieve both high fidelity in predictions and high computational efficiency. Specifically, a computationally economical species transport model via finite volume algorithm is employed as the base model for the whole computational domain, while the advanced transported probability density function (TPDF) method via Lagrangian particle tracking is employed only for regions with intense turbulence-chemistry interaction. The ‘PDF regions’ can be updated dynamically based on local flow and flame characteristics, and are compatible with complex geometric structures such as separated multi blocks, non-convex, and multi-connected regions. A two-way particle/finite volume submodel coupling is formulated to ensure the composition consistency in submodels in the PDF regions and to impose the correct interface conditions for composition and mass flow rate on the boundary of the PDF regions. The spatial partition and particle algorithms for time-varying PDF regions are demonstrated and the convergence characteristics of the adaptive modelling are investigated specifically for the variation of statistical error and bias with the number of particles per cell. The proposed zone-adaptive hybrid particle/finite volume algorithm has been numerically validated in a turbulent hydrogen/air non-premixed jet flame. It is shown that the predictions from zone-adaptive modelling are almost identical to those of stand-alone TPDF, illustrating the preservation of prediction accuracy but with significantly less computational cost.

Keywords:

• hybrid particle/finite volume algorithm
• transported PDF method
• zone-adaptive modelling
• submodel coupling
• convergence analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [grant numbers 91841302, 52025062] and National Science and Technology Major Project [grant number 2017-I-0004-0005].