most citedConstruction of Reduced Order Models for Fluid Flows Using Deep Feedforward Neural Networks

165 citations · 168 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CE2020

Strong Scaling of Numerical Solver for Supersonic Jet Flow Configuration

Carlos Junqueira-Junior, João Luiz F. Azevedo, Jairo Panetta +2

Acoustics loads are rocket design constraints which push researches and engineers to invest efforts in the aeroacoustics phenomena which is present on launch vehicles. Therefore, a…

cs.DC2020

On the scalability of CFD tool for supersonic jet flow configurations

Carlos Junqueira-Junior, João Luiz F. Azevedo, Jairo Panetta +2

New regulations are imposing noise emissions limitations for the aviation industry which are pushing researchers and engineers to invest efforts in studying the aeroacoustics pheno…

physics.flu-dyn2019

On Secondary Tones Arising in Trailing-Edge Noise at Moderate Reynolds Numbers

Tulio R. Ricciardi, Walter Arias-Ramirez, William R. Wolf

Direct numerical simulations are carried out to investigate the flow features responsible for secondary tones arising in trailing-edge noise at moderate Reynolds numbers. Simulatio…

physics.flu-dyn20193 cited

Active Flow Control for Drag Reduction of a Plunging Airfoil under Deep Dynamic Stall

Brener D'Lélis Oliveira Ramos, William Roberto Wolf, Chi-An Yeh +1

High-fidelity simulations are performed to study active flow control techniques for alleviating deep dynamic stall of a SD7003 airfoil in plunging motion. The flow Reynolds number…

physics.flu-dyn2019165 cited

Construction of Reduced Order Models for Fluid Flows Using Deep Feedforward Neural Networks

Hugo F. S. Lui, William R. Wolf

We present a numerical methodology for construction of reduced order models, ROMs, of fluid flows through the combination of flow modal decomposition and regression analysis. Spect…