Simulation of the FDA nozzle benchmark: A lattice Boltzmann study - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Computer Methods and Programs in Biomedicine Year : 2022

Simulation of the FDA nozzle benchmark: A lattice Boltzmann study

Abstract

Background and objective: Contrary to flows in small intracranial vessels, many blood flow configurations such as thosefound in aortic vessels and aneurysms involve larger Reynolds numbers and, therefore, transitional or turbulent conditions. Dealing with such systems require both robust and efficient numerical methods.Methods: We assess here the performance of a lattice Boltzmann solver with full Hermite expansion of the equilibriumand central Hermite moments collision operator at higher Reynolds numbers, especially for under-resolved simulations.To that end the food and drug administration’s benchmark nozzle is considered at three different Reynolds numberscovering all regimes: 1) laminar at a Reynolds number of 500, 2) transitional at a Reynolds number of 3500, and 3)low-level turbulence at a Reynolds number of 6500.Results: The lattice Boltzmann results are compared with previously published inter-laboratory experimental data obtained by particle image velocimetry. Our results show good agreement with the experimental measurements throughout the nozzle, demonstrating the good performance of the solver even in under-resolved simulations.Conclusion: In this manner, fast but sufficiently accurate numerical predictions can be achieved for flow configurationsof practical interest regarding medical applications.
Fichier principal
Vignette du fichier
2204.10566.pdf (6.73 Mo) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

hal-03881693 , version 1 (06-02-2024)

Licence

Identifiers

Cite

Feng Huang, Romain Noël, Philipp Berg, Seyed Ali Hosseini. Simulation of the FDA nozzle benchmark: A lattice Boltzmann study. Computer Methods and Programs in Biomedicine, 2022, 221, pp.106863. ⟨10.1016/j.cmpb.2022.106863⟩. ⟨hal-03881693⟩
47 View
4 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More