arXiv subjects

A. Marsden

Publications and source records attributed to A. Marsden.

2 recordsLinked to original sources

arXiv2026

Data assimilation of flow MRI data into RANS models with algebraic closures

We adopt the Bayesian inference framework to solve an inverse Reynolds-Averaged Navier-Stokes (RANS) problem for the approximate posterior probability distribution of the turbulence model parameters and inlet boundary conditions of a confined turbulent jet. The data are noisy 3D flow MRI measurements of a Newtonian fluid flowing through the Food and Drug Administration (FDA) nozzle geometry. We assimilate this into RANS using two algebraic turbulence models based on the mean shear rate magnitude and the turbulence kinetic energy. We demonstrate that the inferred models are able to reconstruct the measured mean flow velocities without overfitting and we provide uncertainty estimates for the model parameters. The methodology can readily be extended to more complex RANS models, provided that they remain differentiable.

physics.flu-dyn↗

arXiv2024

Bayesian inference of mean velocity fields and turbulence models from flow MRI

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.

physics.flu-dyn↗