A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

Applied Intelligence, vol. 52, pp. 9080–9100

Abstract

Abstract Many simulation workflows require to prepare the data for the simulation manually. This is time consuming and leads to a massive bottleneck when a large number of numerical simulations is requested. This bottleneck can be overcome by an automated data processing pipeline. Such a novel pipeline is developed for a medical use case from rhinology, where computer tomography recordings are used as input and flow simulation data define the results. Convolutional neural networks are applied to segment the upper airways and to detect and prepare the in- and outflow regions for accurate boundary condition prescription in the simulation. The automated process is tested on three cases which have not been used to train the networks. The accuracy of the pipeline is evaluated by comparing the network-generated output surfaces to those obtained from a semi-automated procedure performed by a medical professional. Except for minor deviations at interfaces between ethmoidal sinuses, the network-generated surface is sufficiently accurate. To further analyze the accuracy of the automated pipeline, flow simulations are conducted with a thermal lattice-Boltzmann method for both cases on a high-performace computing system. The comparison of the results of the respiratory flow simulations yield averaged errors of less than 1% for the pressure loss between the in- and outlets, and for the outlet temperature. Thus, the pipeline is shown to work accurately and the geometrical deviations at the ethmoidal sinuses to be negligible.

Authors 4

1. Mario Rüttgers corresponding Aachen Institute of Aerodynamics Chair of Fluid Mechanics

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Aachen, Germany

   Jülich Aachen Research Alliance - Center for Simulation and Data Sciences (JARA-CSD), RWTH Aachen University and Forschungszentrum Jülich GmbH, Germany

   Jülich Supercomputing CentreForschungszentrum Jülich GmbH, Jülich, Germany

2. Moritz Waldmann Aachen Institute of Aerodynamics Chair of Fluid Mechanics

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Aachen, Germany

   Jülich Aachen Research Alliance - Center for Simulation and Data Sciences (JARA-CSD), RWTH Aachen University and Forschungszentrum Jülich GmbH, Germany

3. Wolfgang Schröder Aachen Institute of Aerodynamics Chair of Fluid Mechanics

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Aachen, Germany

   Jülich Aachen Research Alliance - Center for Simulation and Data Sciences (JARA-CSD), RWTH Aachen University and Forschungszentrum Jülich GmbH, Germany

4. Andreas Lintermann Aachen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Jülich Aachen Research Alliance - Center for Simulation and Data Sciences (JARA-CSD), RWTH Aachen University and Forschungszentrum Jülich GmbH, Germany

   Jülich Supercomputing CentreForschungszentrum Jülich GmbH, Jülich, Germany