Bayesian inference of mean velocity fields and turbulence models from flow MRI
This work addresses the challenge of accurate turbulence modeling for fluid dynamics applications, such as medical devices, but is incremental as it builds on existing Bayesian inverse RANS methods.
The authors tackled the problem of reconstructing mean flow fields and learning turbulence model parameters from flow MRI data, achieving successful reconstruction and parameter learning without overfitting in a confined turbulent jet experiment.
We solve a Bayesian inverse Reynolds-averaged Navier-Stokes (RANS) problem that assimilates mean flow data by jointly reconstructing the mean flow field and learning its unknown RANS parameters. We devise an algorithm that learns the most likely parameters of an algebraic effective viscosity model, and estimates their uncertainties, from mean flow data of a turbulent flow. We conduct a flow MRI experiment to obtain mean flow data of a confined turbulent jet in an idealized medical device known as the FDA (Food and Drug Administration) nozzle. The algorithm successfully reconstructs the mean flow field and learns the most likely turbulence model parameters without overfitting. The methodology accepts any turbulence model, be it algebraic (explicit) or multi-equation (implicit), as long as the model is differentiable, and naturally extends to unsteady turbulent flows.