Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning

• URL: http://arxiv.org/abs/2406.18464v2
• Date: Sat, 16 Nov 2024 16:57:04 GMT
• Title: Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning
• Authors: Alexandros Kontogiannis, Scott V. Elgersma, Andrew J. Sederman, Matthew P. Juniper,
• Abstract summary: We formulate and solve a Bayesian inverse Navier-Stokes (N-S) problem that assimilates velocimetry data. We learn the unknown N-S parameters, including the boundary position. We then use this method to reconstruct magnetic resonance velocimetry data of a 3D steady laminar flow.
• Score: 44.62264781248436
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• Abstract: We formulate and solve a Bayesian inverse Navier-Stokes (N-S) problem that assimilates velocimetry data in order to jointly reconstruct a 3D flow field and learn the unknown N-S parameters, including the boundary position. By hardwiring a generalised N-S problem, and regularising its unknown parameters using Gaussian prior distributions, we learn the most likely parameters in a collapsed search space. The most likely flow field reconstruction is then the N-S solution that corresponds to the learned parameters. We develop the method in the variational setting and use a stabilised Nitsche weak form of the N-S problem that permits the control of all N-S parameters. To regularise the inferred the geometry, we use a viscous signed distance field (vSDF) as an auxiliary variable, which is given as the solution of a viscous Eikonal boundary value problem. We devise an algorithm that solves this inverse problem, and numerically implement it using an adjoint-consistent stabilised cut-cell finite element method. We then use this method to reconstruct magnetic resonance velocimetry (flow-MRI) data of a 3D steady laminar flow through a physical model of an aortic arch for two different Reynolds numbers and signal-to-noise ratio (SNR) levels (low/high). We find that the method can accurately i) reconstruct the low SNR data by filtering out the noise/artefacts and recovering flow features that are obscured by noise, and ii) reproduce the high SNR data without overfitting. Although the framework that we develop applies to 3D steady laminar flows in complex geometries, it readily extends to time-dependent laminar and Reynolds-averaged turbulent flows, as well as non-Newtonian (e.g. viscoelastic) fluids.

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