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Physically Organized Latent Spaces in Unsupervised Autoencoders: Evidence from Aerodynamic Databases
Renato Tognaccini, Ettore Saetta, Gianluca Iaccarino
Using aerodynamic databases of progressively increasing complexity - from linear analytical theory to nonlinear separated RANS flows - we demonstrate that an autoencoder, trained s…
Effect of span size in Scale Resolving Simulations of airfoil stall and post-stall
Francesco Mario D'Afiero, Renato Tognaccini, Gianluca Iaccarino
In aerodynamic design, the use of Computational Fluid Dynamics (CFD) tools has long relied on Reynolds-Averaged Navier-Stokes (RANS) and Unsteady RANS (URANS) turbulence models. Ho…
Neural network-based closure models for large-eddy simulations with explicit filtering
Mark Benjamin, Gianluca Iaccarino
Data from direct numerical simulations of turbulent flows are commonly used to train neural network-based models as subgrid closures for large-eddy simulations; however, models wit…
Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
Jin-Long Wu, Heng Xiao, Eric Paterson
Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncert…
A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
Jian-Xun Wang, Jinlong Wu, Julia Ling +2
Although an increased availability of computational resources has enabled high-fidelity simulations of turbulent flows, the RANS models are still the dominant tools for industrial…