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Parametric solutions of turbulent incompressible flows in OpenFOAM via the proper generalised decomposition
Journal of Computational Physics, Volume: 449, Start page: 110802
Swansea University Author: Rubén Sevilla
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An a priori reduced order method based on the proper generalised decomposition (PGD) is proposed to compute parametric solutions involving turbulent incompressible flows of interest in an industrial context, using Open-FOAM. The PGD framework is applied for the first time to the incompressibleNavier...
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An a priori reduced order method based on the proper generalised decomposition (PGD) is proposed to compute parametric solutions involving turbulent incompressible flows of interest in an industrial context, using Open-FOAM. The PGD framework is applied for the first time to the incompressibleNavier-Stokes equations in the turbulent regime, to compute a generalised solution for velocity, pressure and turbulent viscosity, explicitly depending on the design parameters of the problem. In order to simulate flows of industrial interest, a minimally intrusive implementation based on Open-FOAM SIMPLE algorithm applied to the Reynolds-averaged Navier-Stokes equations with the Spalart-Allmaras turbulence model is devised. The resulting PGD strategy is applied to parametric flow control problems and achieves both qualitative and quantitative agreement with the full order OpenFOAM solution for convection-dominated fully-developed turbulent incompressible flows, with Reynolds number up to one million.
Reduced order models, proper generalised decomposition, turbulent incompressible flows, parametrised flows, OpenFOAM
College of Engineering
This work was partially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (Grant agreement No. 675919) that financed the PhD fellowship of V.T. M.G. acknowledges the support of the Serra Húnter Programme of the Generalitat de Catalunya. M.G., R.S. and A.H. were supported by the Spanish Ministry of Economy and Competitiveness (Grant agreement No. DPI2017-85139-C2-2-R). M.G. and A.H. are also grateful
for the financial support provided by the Spanish State Research Agency (Grant agreement No. CEX2018-000797-S) and the Generalitat de Catalunya (Grant agreement No. 2017-SGR-1278). R.S. also acknowledges the support of the Engineering and Physical Sciences Research Council (Grant number: EP/P033997/1).