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

• Published in: Mathematics (Basel) Vol. 9; no. 16; p. 1843
• Main Authors: Abucide-Armas, Alvaro, Portal-Porras, Koldo, Fernandez-Gamiz, Unai, Zulueta, Ekaitz, Teso-Fz-Betoño, Adrian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: Artificial neural networks; Computational fluid dynamics; computational fluids dynamics; Computing costs; convolutional neural networks; Data augmentation; Deep learning; DeepCFD; Design optimization; Fluid flow; Geometry; Neural networks; Product development; Reynolds number; Simulation; Turbulence models; Turbulent flow; Velocity; Viscosity
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9161843

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.