(193c) CFD Modeling of Oxy-Natural Gas Furnace Using Detailed Kinetic Modeling with and without the Use of Chemistry Acceleration | AIChE

(193c) CFD Modeling of Oxy-Natural Gas Furnace Using Detailed Kinetic Modeling with and without the Use of Chemistry Acceleration


Krishnamoorthy, N. - Presenter, Siemens PLM Software
Aglave, R., Siemens PLM Software
Tourani, C., Siemens PLM Software
Numerical modelling of the combustion in industrial geometries is often carried out using computationally less intensive methodologies such as global kinetic reaction schemes involving a few steps, or via pre-computed flamelet tables constructed from zero or one dimensional canonical systems. Although reasonable predictions of heat release, temperature, and major species like CO2 and H2O is possible using these combustion models, the slow forming species are often not captured accurately. Detailed kinetic modelling enables the accurate description of all the species (both slow and fast) in a reacting system but is often not used for industrial applications because of its prohibitive computational cost. The objective of this study is to investigate the effect of three chemistry acceleration techniques that can be used with the complex chemistry model in STAR-CCM+ [1] for improving CPU efficiency with potentially minimum loss of accuracy.

The test case considered in this paper is a coaxial high momentum oxy-natural gas flame in a refractory lined furnace at a thermal input of 0.8 MW [2]. Radial profiles of velocity, temperature and species are compared between experiments and simulations at several axial locations downstream of the burner. The detailed chemical kinetic mechanism GRI3.0 was used to represent the chemical kinetics and the effect of chemistry acceleration techniques such as (a) In-situ Adaptive Tabulation (ISAT), (b) Clustering and (c) Dynamic Mechanism Reduction (DMR) on the predicted results was analysed. ISAT [3] enables speed up of complex chemistry simulations by storing and retrieving approximate reaction mapping solutions related to the chemical state space during run time. The clustering method groups cells with similar thermal and chemical states before integrating the averaged state for the group which in turn causes substantial speed-up. The DMR methodology is based on Directed Relation Graph (DRG) algorithm [4] where the mechanism is dynamically reduced in every cell at every iteration by identifying and solving only those species that substantially change over the reaction step thereby reducing overall computational time. The speed up achieved for each of the acceleration techniques and the associated accuracy is reported in this study.


[1] https://mdx.plm.automation.siemens.com/star-ccm-plus

[2] Lallemant, N., Dugue, R., and Weber, R., (1997). Analysis of the Experimental Data Collected During the OXYFLAM-1 and OXYFLAM-2 Experiments. Technical Report F85/y/4, International Flame Research Foundation.

[3] Pope, S.B. 1997. Combust. Theory and Modelling, 1, pp. 41-63.

[4] T. Lu, C. Law, Proc. Combust. Inst. 30 (2005) 1333-1341.