Evaluation of flamelet-based partially premixed combustion models for simulating the gas phase combustion of a grate firing biomass furnace. (1st February 2023) - Sakib, A H M Nazmush; Farokhi, Mohammadreza; Birouk, Madjid
Highlights: The gas-phase combustion of a lab-scale biomass furnace is numerically studied. Flamelet-based partially premixed combustion models are evaluated. C-equation/UFM based partially premixed model is found to be the most optimum. Abstract: Depending on the mass flow rates of fuel, the primary air (injected usually from underneath of the fuel bed) as well as the composition of volatile gases extracted from the bed, it is believed that the volatile gases taking part in the gas-phase combustion undergo a partially premixed process close to the bed section. Therefore, the aim of this study is to evaluate the prediction capability of flamelet-based partially premixed combustion models in simulating the gas-phase combustion process of a grate firing biomass furnace. Additionally, the effects of the adopted premixed models (i.e., C Equation based model and extended coherent flame model) and non-premixed models (i.e., steady diffusion flamelet (SFM) and unsteady diffusion flamelet (UFM)) on the overall prediction of partially premixed model are assessed. The predicted temperature field and species concentrations are compared with published experimental measurements and also with numerical simulations which use other combustion models (i.e., EDC/Flamelet hybrid model, SFM and UFM). The results of this study revealed that except for slow forming and chemically dominated species, partially premixed combustion models (both extended coherent flame model/SFM and C-equation/SFM based partially premixed model) are capable of reproducing the experimental temperature and major species with reasonable accuracy. For instance, the predicted temperature deviates from the experimental counterpart by a maximum of 9.01%, and the major species by 15.44% (CO2 ) and 21.60% (O2 ). In addition, C-equation/UFM-based partially premixed model is found to be the most optimum combination of the examined partially premixed models for overcoming the deficiency faced while predicting the slow-forming species.