CFD-MRI: A coupled measurement and simulation approach for accurate fluid flow characterisation and domain identification. (30th April 2018)

• Record Type: Journal Article
• Authors: Klemens, Fabian; Schuhmann, Sebastian; Guthausen, Gisela; Thäter, Gudrun; Krause, Mathias J.
• Is Part Of: Computers & fluids. Volume 166(2018)
• Page Start: 218
• Page End: 224
• Publication Date: 2018-04-30
• Subjects: Lattice Boltzmann method -- Simulation -- MRI -- Optimisation -- Fluid characterisation -- Domain identification; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2018.02.022
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: The CFD-MRI method for accurate fluid flow characterisation and domain identification is proposed. Simulation and measurement data are combined to an optimisation problem. The method is able to locate an object and accurately characterise the fluid flow using only 2D spatially resolved MRI data. As a result, the measurement noise was significantly reduced. Abstract: This article presents the coupling of magnetic resonance imaging (MRI) measurements and computational fluid dynamics (CFD) for accurate characterisation of fluid flow and identification of flow domains. Currently, MRI measurements are averaged over time and space, assuming a certain smoothness of the velocity and pressure space. However, a possible solution of a fluid problem must fulfil the Navier–Stokes equations, which sets up a condition that is much more restrictive than the usual smoothness assumptions in e.g. curve fitting. The novel CFD-MRI method uses this insight to reduce the statistical noise and to identify finer structures of the underlying domain. The problem is formulated as a distributed control problem which minimises the distance between measured and simulated flow field. Thereby, the simulated flow field is the solution of a parametrised porous media BGK-Boltzmann equation which approaches a homogenised Navier–Stokes equation in the hydrodynamic limit. The parameters represent the porosity distributed in the domain which yields a domain and a fluid flow that fits best to the measured data. This enables the method they locate an obstacle and the flow field from limited 2 D spatially resolved MRI data with one velocity component. The problem is solved with an adjoint lattice Boltzmann method (ALBM) using the open source software OpenLB 1 .