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Abstract / Description of output
In this paper we present a simple, easily implemented and effective approach for explicitly-filtered Large Eddy Simulation with a Discontinuous Galerkin (DG) discretision for velocity. DG formulations are often desirable due to their stability and increased accuracy, however this can come at greater computational expense due to the additional degrees of freedom in the velocity field. Additionally, data output can also be an issue, due to the increased storage requirements. Here we present a hybrid approach, based upon the construction of an approximation of the velocity shear tensor using information from a projected Continuous Galerkin (CG) version of the discontinuous velocity field.
The resulting turbulence algorithm is implemented within Fluidity, an open-source computational fluid dynamics solver. The model is then validated with a well known test case, and shown to agree favourably with published results. Comparisons are also made between the CG/DG hybrid LES with DG-only LES, which demonstrate the superior computational performance of the hybrid model.
The resulting turbulence algorithm is implemented within Fluidity, an open-source computational fluid dynamics solver. The model is then validated with a well known test case, and shown to agree favourably with published results. Comparisons are also made between the CG/DG hybrid LES with DG-only LES, which demonstrate the superior computational performance of the hybrid model.
Original language | English |
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Article number | 107730 |
Journal | Computer Physics Communications |
Volume | 260 |
Early online date | 19 Nov 2020 |
DOIs | |
Publication status | Published - 1 Mar 2021 |
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Dive into the research topics of 'Hybrid Large Eddy Simulation for Low-order Discontinuous Galerkin Methods Using an Explicit Filter'. Together they form a unique fingerprint.Projects
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Velocity time-series data from a hybrid finite element Large Eddy Simulation of flow over a backward-facing step with associated geometry and mesh files
Creech, A. (Creator) & Jackson, A. (Creator), Edinburgh DataShare, 5 Mar 2024
DOI: 10.7488/ds/7690
Dataset