RT Journal Article
T1 From snapshots to manifolds - a tale of shear flows
A1 Farzamnik, E.
A1 Ianiro, Andrea
A1 Discetti, Stefano
A1 Deng, N.
A1 Oberleithner, K.
A1 Noack, B.R.
A1 Guerrero Lozano, Vanesa
AB We propose a novel nonlinear manifold learning from snapshot data and demonstrate its superiority over proper orthogonal decomposition (POD) for shedding-dominated shear flows. Key enablers are isometric feature mapping, Isomap, as encoder and, K-nearest neighbours (KNN) algorithm as decoder. The proposed technique is applied to numerical and experimental datasets including the fluidic pinball, a swirling jet and the wake behind a couple of tandem cylinders. Analysing the fluidic pinball, the manifold is able to describe the pitchfork bifurcation and the chaotic regime with only three feature coordinates. These coordinates are linked to the vortex-shedding phases and the force coefficients. The manifold coordinates of the swirling jet are comparable to the POD mode amplitudes, yet allow for a more distinct and less noise-sensitive manifold identification. A similar observation is made for the wake of two tandem cylinders. The tandem cylinders are aligned and located at a streamwise distance which corresponds to the transition between the single bluff body and the reattachment regimes of vortex shedding. Isomap unveils these two shedding regimes while the Lissajous plot of the first two POD mode amplitudes features a single circle. The reconstruction error of the manifold model is small compared with the fluctuation level, indicating that the low embedding dimensions contain the coherent structure dynamics. The proposed Isomap-KNN manifold learner is expected to be of great importance in estimation, dynamic modelling and control for a large range of configurations with dominant coherent structures.
PB Cambridge University Press
SN 0022-1120
YR 2023
FD 2023-01-25
LK https://hdl.handle.net/10016/37623
UL https://hdl.handle.net/10016/37623
LA eng
NO This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with Universidad Carlos III de Madrid in the line of 'Fostering Young Doctors Research' (PITUFLOW-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). This work has also been partially supported by the project ARTURO, ref. PID2019-109717RBI00/AEI/10.13039/501100011033, funded by the Spanish State Research Agency. B.R.N. and N.D. acknowledge funding by the National Science Foundation of China (NSFC) through grants 12172109 and 12172111 and 12202121, by the Guandgong province, China, via the Natural Science and Engineering grant 2022A1515011492, by the Shenzhen Research Foundation for Basic Research, China, via grant JCYJ20220531095605012, and HangHua company (Dalian, China) for their scientific support. The authors warmly thank Dr F. Lückoff and Dr M. Raiola for providing the swirling jet and the tandem cylinder data sets. Funding for APC: Universidad Carlos III de Madrid (Read and Publish Carlos III University of Madrid).
DS e-Archivo
RD 30 jun. 2024