Adaptive mesh refinement towards optimized mesh generation for large eddy simulation of turbulent combustion in a typical micro gas turbine combustor

Alessio Pappa, Antoine Verhaeghe, Pierre Bénard, Ward De Paepe and Laurent Bricteux

Energy, 2024, vol. 301, issue C

Abstract: Over the last years, several advanced micro Gas Turbine (mGT) cycle developments have been proposed, aiming at making the mGT more fuel and operational flexible. However, accurate data on real industrial combustors, assessing the performances and emissions of the combustion under unconventional diluted conditions or fuels involved in these novel cycles, are still missing. In this framework, Large Eddy Simulations, who allow to accurately assess the unsteady effects coupled to turbulent-chemistry interaction of reacting flows, offer an opportunity to better assess the combustion behaviour under these specific conditions. However, the computational cost remains much higher compared to RANS simulations. The mesh generation process might be complex, especially when the region of interest is not intuitively known. Therefore, Adaptive Mesh Refinement (AMR) methods allow the mesh to be refined only in the region where finer cells are required to capture important phenomena, i.e. in the flame front. By dynamically refining the mesh all along the simulation, the mesh is optimized in terms of cell quantity and distribution for more accurate results at potentially lower computational costs. In this work, an adaptive mesh refinement method based on a predefined criterion is successfully applied to the LES of a typical industrial mGT combustor, the Turbec T100. The results show that the adaptation strategy allows to automatically generate a dynamic mesh that is able to capture correctly the flame over time for an increased computational cost of 15%. Therefore, the human meshing effort is reduced while the automatic meshing leads to an optimized mesh.

Keywords: Large Eddy Simulation (LES); Turbulent combustion; micro Gas Turbine (mGT); Adaptive Mesh Refinement (AMR); Thickened flame model (TFLES) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:301:y:2024:i:c:s0360544224013239

DOI: 10.1016/j.energy.2024.131550

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