Reduction of RANS/LES combustion sub-models for quasi-dimensional spark ignition engine simulations and evaluation of the modelling assumptions with DNS

• Published in: Combustion and flame Vol. 220; pp. 189 - 202
• Main Authors: Bardis, Konstantinos, Kyrtatos, Panagiotis, Frouzakis, Christos E., Wright, Yuri M., Giannakopoulos, George K., Boulouchos, Konstantinos
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.10.2020; Elsevier BV
• Subjects: Calibration; Combustion; Direct numerical simulation; Early flame development; Flame wall interaction; Large eddy simulation; Mathematical models; Model accuracy; Optimization; Quasi dimensional model; Reduction; Simulation; Spark ignition; Spark ignition engines; Three dimensional models; Flame wall interaction; Direct numerical simulation; Spark ignition engines; Early flame development; Quasi dimensional model
• DOI: 10.1016/j.combustflame.2020.06.034
• ISSN: 0010-2180

Despite the significant improvement of computational resources, large eddy simulations (LES) are too expensive to be applied to wide ranges of operating conditions and multiple engine architectures. Efficient models employed in quasi/zero-dimensional approaches may offer an attractive alternative for engine calibration or optimisation. Despite their fair predictive capability, validity is typically limited to around their calibration region and is subject to uncertainties that stem from heuristic simplifications. The validity of the individual sub-models is rarely assessed using detailed three-dimensional (3-D) simulations over a wide range of combustion regimes. The objectives of this work are the following: (i) to present a formal reduction of widely applied 3-D LES combustion sub-models found in the literature with particular emphasis on the early flame development and flame-wall interaction, (ii) to assess the assumptions/approximations introduced in quasi-dimensional (Q-D) modelling using Direct Numerical Simulation (DNS), and (iii) to refine the Q-D models used currently, using formally-derived sub-models. It is found that many of the refinements introduced noticeably improve the accuracy of the Q-D model. The functional form of the models obtained through formal reduction of various LES combustion sub-models presents similarities and agrees well with the heuristic functions that can be found in existing Q-D models. Ultimately, this study enhances the transferability of insights from fundamental investigations to real engine applications, which can be useful for future Q-D model development and validation.