Hybrid datasets: Incorporating experimental data into Lattice‐Boltzmann simulations. Issue 6 (28th May 2020)

• Record Type: Journal Article
• Authors: Morrison, Helen E.; Lintermann, Andreas; Grundmann, Sven
• Is Part Of: Engineering reports. Volume 2:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-28
• Subjects: Lattice‐Boltzmann method -- hybrid simulation -- magnetic resonance velocimetry -- Reynolds‐averaged Navier‐Stokes equations; Engineering -- Periodicals; Computer science -- Periodicals; 620.005
• DOI: 10.1002/eng2.12177
• Languages: English
• ISSNs: 2577-8196

Abstract

Summary: A novel method, which combines both fluid‐mechanical experimental and numerical data from magnetic resonance velocimetry and Lattice‐Boltzmann (LB) simulations is presented. The LB method offers a unique and simple way of integrating the experimental data into the simulation by means of its equilibrium term. The simulation is guided by the experimental data, while at the same time potential outliers or noisy data are physically smoothed. In addition, the simulation allows to increase the resolution and to obtain further physical quantities, which are not measurable in the experiment. For a benchmark case, temporally averaged velocity data is included into the simulation. The proposed model creates a hybrid dataset, which satisfies the Reynolds‐averaged Navier‐Stokes equations, including the correctly deduced contribution from the Reynolds stress tensor. Abstract : A novel method which combines both fluid‐mechanical experimental and numerical data from magnetic resonance velocimetry and lattice‐Boltzmann simulations is presented. For a benchmark case, temporally averaged velocity data is included into the simulation. The proposed model creates a hybrid dataset, which satisfies the Reynolds‐averaged Navier‐Stokes equations, including the correctly deduced contribution from the Reynolds stress tensor.