TY - CONF A1 - Birkenmaier, Clemens A1 - Krenkel, Lars A2 - Dillmann, Andreas A2 - Heller, Gerd A2 - Krämer, Ewald A2 - Wagner, Claus T1 - Convolutional Neural Networks for Approximation of Blood Flow in Artificial Lungs T2 - New Results in Numerical and Experimental Fluid Mechanics XIII: Contributions to the 22nd STAB/DGLR Symposium N2 - 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. KW - Deep learning fluid mechanics KW - Multiphase blood flow Y1 - 2021 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/2750 SN - 978-3-030-79560-3 IS - 1. Auflage SP - 451 EP - 460 PB - Springer International Publishing CY - Cham ER -