Prediction of Acoustic Fields Using a Lattice-Boltzmann Method and Deep Learning

Lecture notes in computer science, pp. 81–101

Abstract

Abstract Using traditional computational fluid dynamics and aeroacoustics methods, the accurate simulation of aeroacoustic sources requires high compute resources to resolve all necessary physical phenomena. In contrast, once trained, artificial neural networks such as deep encoder-decoder convolutional networks allow to predict aeroacoustics at lower cost and, depending on the quality of the employed network, also at high accuracy. The architecture for such a neural network is developed to predict the sound pressure level in a 2D square domain. It is trained by numerical results from up to 20,000 GPU-based lattice-Boltzmann simulations that include randomly distributed rectangular and circular objects, and monopole sources. Types of boundary conditions, the monopole locations, and cell distances for objects and monopoles serve as input to the network. Parameters are studied to tune the predictions and to increase their accuracy. The complexity of the setup is successively increased along three cases and the impact of the number of feature maps, the type of loss function, and the number of training data on the prediction accuracy is investigated. An optimal choice of the parameters leads to network-predicted results that are in good agreement with the simulated findings. This is corroborated by negligible differences of the sound pressure level between the simulated and the network-predicted results along characteristic lines and by small mean errors.

Authors 5

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

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

   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 Science, Aachen, Germany

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, Germany

2. Seong Ryong Koh

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, Germany

3. Jenia Jitsev

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, Germany

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

   RWTH Aachen University · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics , RWTH Aachen University , Wüllnerstraße 5a , 52062 Aachen , Germany

   Jülich Aachen Research Alliance Center for Simulation and Data Science , Seffenter Weg 23 , 52074 Aachen , Germany

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

   Jülich Aachen Research Alliance Center for Simulation and Data Science, Aachen, Germany

5. Andreas Lintermann

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Aachen Research Alliance Center for Simulation and Data Science, Aachen, Germany

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, Germany