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Hybrid datasets: Incorporating experimental data into Lattice‐Boltzmann simulations

Authors: Helen E. Morrison; Andreas Lintermann; Sven Grundmann;

Hybrid datasets: Incorporating experimental data into Lattice‐Boltzmann simulations

Abstract

SummaryA 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.

Country
Germany
Keywords

Lattice‐Boltzmann method, Electronic computers. Computer science, QA75.5-76.95, TA1-2040, Engineering (General). Civil engineering (General), hybrid simulation, info:eu-repo/classification/ddc/620, magnetic resonance velocimetry, Reynolds‐averaged Navier‐Stokes equations

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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