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Assessment of Advanced SGS Models for LES Analysis of ICE Wall-Bounded Flows - Part I: Basic Test Case
ISSN: 1946-3936, e-ISSN: 1946-3944
Published March 14, 2016 by SAE International in United States
Citation: Catellani, C., Bianchi, G., Falfari, S., Cazzoli, G. et al., "Assessment of Advanced SGS Models for LES Analysis of ICE Wall-Bounded Flows - Part I: Basic Test Case," SAE Int. J. Engines 9(1):657-673, 2016, https://doi.org/10.4271/2016-01-9041.
Large Eddy Simulation (LES) represents nowadays one of the most promising techniques for the evaluation of the dynamics and evolution of turbulent structures characterizing internal combustion engines (ICE). In the present paper, subdivided into two parts, the capabilities of the open-source CFD code OpenFOAM® v2.3.0 are assessed in order to evaluate its suitability for engine cold flow LES analyses. Firstly, the code dissipative attitude is evaluated through an inviscid vortex convection test to ensure that the levels of numerical dissipation are compatible with LES needs. Quality and completeness estimators for LES simulations are then proposed. In particular the Pope M parameter is used as a LES completeness indicator while the LSR parameter provides useful insights far calibrating the grid density. Other parameters such as the two-grid LESIQk index are also discussed. Then advanced SGS models such as the dynamic WALE and the Sigma models are compared with models traditionally used for LES simulations of ICE wall-bounded flows, i.e. the dynamic Smagorinsky and the WALE models. The proposed SGS models have been implemented in the open-source CFD code OpenFOAM®, extending its standard capabilities. Validation of the implemented models has been performed using two different test cases: the Dellenback abrupt expansion, which is presented in part I, and the Thobois stationary flow bench, presented in part II. The results have been evaluated through comparison with experimental data and the completeness of the LES simulations has been assessed using the aforementioned quality estimators.