Convolutional Neural Networks for Approximation of Blood Flow in Artificial Lungs
- Blood flow in channels of varying diameters <500μm exhibits strong non-linear effects. Multiphase finite volume approaches are feasible, but still computationally costly. Here, the feasibility of applying convolutional neural networks for blood flow prediction in artificial lungs is investigated. Training targets are precomputed using an Eulerian two-phase approach. To match with experimental data, the interphase drag and lift, as well as intraphase shear-thinning are adapted. A recursively branching regression network and convolution/deconvolution networks with plain skip connections and densely connected skips are investigated. A priori knowledge is incorporated in the loss functional to prevent the network from learning non-physical solutions. Inference from neural networks is approximately six orders of magnitude faster than the classical finite volume approach. Even if resulting in comparably coarse flow fields, the neural network predictions can be used as close to convergence initial solutions greatly accelerating classical flow computations.
Author: | Clemens BirkenmaierORCiD, Lars KrenkelORCiDGND |
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DOI: | https://doi.org/10.1007/978-3-030-79561-0_43 |
ISBN: | 978-3-030-79560-3 |
Parent Title (English): | New Results in Numerical and Experimental Fluid Mechanics XIII: Contributions to the 22nd STAB/DGLR Symposium |
Publisher: | Springer International Publishing |
Place of publication: | Cham |
Editor: | Andreas Dillmann, Gerd Heller, Ewald Krämer, Claus Wagner |
Document Type: | conference proceeding (article) |
Language: | English |
Year of first Publication: | 2021 |
Release Date: | 2022/02/17 |
Tag: | Deep learning fluid mechanics; Multiphase blood flow |
Issue: | 1. Auflage |
First Page: | 451 |
Last Page: | 460 |
Andere Schriftenreihe: | Notes on Numerical Fluid Mechanics and Multidisciplinary Design ; 151 |
Institutes: | Fakultät Maschinenbau |
Regensburg Center of Biomedical Engineering - RCBE | |
Regensburg Center of Health Sciences and Technology - RCHST | |
Fakultät Maschinenbau / Labor Biofluidmechanik | |
Begutachtungsstatus: | peer-reviewed |
research focus: | Lebenswissenschaften und Ethik |
Licence (German): | Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG |