A Data Augmentation-Based Technique for Deep Learning Applied to CFD Simulations

Alvaro Abucide-Armas, Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta and Adrian Teso-Fz-Betoño
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Alvaro Abucide-Armas: Automatic Control and System Engineering Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Koldo Portal-Porras: Nuclear Engineering and Fluid Mechanics Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Unai Fernandez-Gamiz: Nuclear Engineering and Fluid Mechanics Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Ekaitz Zulueta: Automatic Control and System Engineering Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Adrian Teso-Fz-Betoño: Automatic Control and System Engineering Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain

Mathematics, 2021, vol. 9, issue 16, 1-14

Abstract: The computational cost and memory demand required by computational fluid dynamics (CFD) codes simulations can become very high. Therefore, the application of convolutional neural networks (CNN) in this field has been studied owing to its capacity to learn patterns from sets of input data, which can considerably approximate the results of the CFD simulations with relative low errors. DeepCFD code has been taken as a basis and with some slight variations in the parameters of the CNN, while the net is able to solve the Navier–Stokes equations for steady turbulent flows with variable input velocities to the domain. In order to acquire extensive input data to the CNN, a data augmentation technique, which considers the similarity principle for fluid dynamics, is implemented. As a consequence, DeepCFD is able to learn the velocities and pressure fields quite accurately, speeding up the time-consuming CFD simulations.

Keywords: data augmentation; computational fluids dynamics; convolutional neural networks; DeepCFD (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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