Data-Driven Turbulence Modelling for Magnetohydrodynamic Flows in Annular Pipes
A. Montoya Santamaria (TU Delft - Aerodynamics)
T.S.B. Buchanan (TU Delft - Aerodynamics)
Francesco Fico (Loughborough University)
I. Langella (TU Delft - Flight Performance and Propulsion)
R.P. Dwight (TU Delft - Aerodynamics)
Nguyen Anh Khoa Doan (TU Delft - Aerodynamics)
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Abstract
We present a data-driven approach to Reynolds-averaged Navier-Stokes (RANS) turbulence closure modelling in magnetohydrodynamic (MHD) flows. In these flows the magnetic field interacting with the conductive fluid induces unconventional turbulence states such as quasi two-dimensional (2D) turbulence, and turbulence suppression, which are poorly represented by standard Boussinesq models. Our data-driven approach uses time-averaged Large Eddy Simulation (LES) data of annular pipe flows, at different Hartmann numbers, to derive corrections for the - SST model. Correction fields are obtained by injecting time averaged LES fields into the MHD RANS equations, and examining the remaining residuals. The correction to the Reynolds-stress anisotropy is approximated with a modified Tensor Basis Neural Network (TBNN). We extend the generalised eddy hypothesis with a traceless antisymmetric tensor representation of the Lorentz force to obtain MHD flow features, thus keeping Galilean and frame invariance while including MHD effects in the turbulence model. The resulting data-driven models are shown to reduce errors in the mean flow, and to generalise to annular flow cases with different Hartmann numbers from those of the training cases.
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