most citedData-driven nonlinear turbulent flow scaling with Buckingham Pi variables

35 citations · 49 across the 5 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2024

An Invitation to Resolvent Analysis

Laura Victoria Rolandi, Jean Hélder Marques Ribeiro, Chi-An Yeh +1

Resolvent analysis is a powerful tool that can reveal the linear amplification mechanisms between the forcing inputs and the response outputs about a base flow. These mechanisms ca…

physics.flu-dyn202435 cited

Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables

Kai Fukami, Susumu Goto, Kunihiko Taira

Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate…

nlin.AO20241 cited

Phase autoencoder for limit-cycle oscillators

Koichiro Yawata, Kai Fukami, Kunihiko Taira +1

We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder i…

physics.flu-dyn20232 cited

Similarities in Massive Separation Across Reynolds Numbers for Swept and Tapered Finite Span Wings

Jacob Neal, Anton Burtsev, Jean Helder Marques Ribeiro +3

Experimental investigations were performed to elucidate the features of flow fields occurring over cantilevered finite-aspect ratio NACA 0015 wings at high angles of attack with va…

physics.flu-dyn202311 cited

Image and video compression of fluid flow data

Vishal Anatharaman, Jason Feldkamp, Kai Fukami +1

We study the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The efficacy of spatial compression techniques, including JPEG…