Turbulence modelling remains a key challenge in the field of computational fluid dynamics (CFD) for accurately resolving fluid flows. A new class of data-driven models have recently gained popularity that aim to quantitatively incorporate data from higher fidelity simulations to improve turbulent closure modelling in Reynolds Averaged Navier Stokes (RANS) models. These models have been particularly well researched in modelling the Reynold stress anisotropy tensor (RST).

This thesis re-formulates the data-driven framework in the context of resolving passive scalar fluxes. Corrections to the scalar flux vector are regressed for two different frameworks: framework 1 that uses the simple gradient diffusion hypothesis (SGDH) model and framework 2: that additionally introduces transport equations for the scalar variance and dissipation. These models are compared to each other for the Jets in-crossflow (JICF) case for resolving the scalar field where baseline RANS models struggle. Both data-driven frameworks show an improvement in modelling the scalar field and turbulent scalar flux vector than the baseline models. The study also indicates that inclusion of additional transport equations doesn’t necessarily improve regression of the corrective fields. ... Read more