Towards Predictive Reacting Flow LES

Cocks, Peter A.; Sankaran, Vaidyanathan; Soteriou, Marios

doi:10.2514/6.2014-0826

Towards Predictive Reacting Flow LES

Conference · Thu Jan 09 23:00:00 EST 2014

DOI:https://doi.org/10.2514/6.2014-0826· OSTI ID:1567673

Cocks, Peter A. ^[1]; Sankaran, Vaidyanathan ^[1]; Soteriou, Marios ^[1]

United Technologies Corporation, East Hartford, CT (United States)

Grid dependency and the interaction of numerical errors with those from physical models can negatively impact the predictive capabilities of the traditional large eddy simulation (LES) approach. This paper attempts to address these issues for the simulation of turbulent reacting flows, where the canonical problem of a temporally evolving reacting jet is used as a test case. The impact of implicit and explicit filtering of the governing system of equations, and the impact of removing grid dependency from physical models through application of a constant filter width, is assessed. Four different grid resolutions are employed in the study. Differences are experienced in mean velocity and temperature profiles with grid resolution refinement when using the traditional implicit filtering approach, demonstrating the grid dependency of the obtained solutions. These differences are substantially mitigated by fixing the filter width used in physical models to a constant value, as the grid resolution is refined. Explicit filtering utilizing discrete filters reduces these differences even further, but to a smaller degree. This latter improvement comes at a very substantial computational cost. Higher Reynolds number cases need to be studied, where the LES resolution is far from that of a DNS, in order for a more conclusive assessment of the benefit of the explicit filtering approach to be made. However, even if explicit filtering is found to have a large positive impact on the predictive capabilities of reacting flow LES, its computational cost is likely to prevent it from being routinely applied to large, complex geometries, at least for the case where discrete filters are used.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge Leadership Computing Facility

Sponsoring Organization:: USDOE Office of Science (SC)

OSTI ID:: 1567673

Country of Publication:: United States

Language:: English

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