Year: 2024
Type: article
Abstract: We propose a data-driven methodology to learn a low-dimensional manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of actuations. Key enablers for the actuation manifold are isometric mapping as encoder, and a combination of a neural network and a $k$ -nearest-neighbour interpolation as decoder. This methodology is tested for the fluidic pinball, a cluster of three parallel cylinders perpendicular to the oncoming uniform flow. The centres of these cylinders are the vertices of an equilateral triangle pointing upstream. The flow is manipulated by constant rotation of the cylinders, i.e. described by three actuation parameters. The Reynolds number based on a cylinder diameter is chosen to be $30$ . The unforced flow yields statistically symmetric periodic shedding represented by a one-dimensional limit cycle. The proposed methodology yields a five-dimensional manifold describing a wide range of dynamics with small representation error. Interestingly, the manifold coordinates automatically unveil physically meaningful parameters. Two of them describe the downstream periodic vortex shedding. The other three describe the near-field actuation, i.e. the strength of boat-tailing, the Magnus effect and forward stagnation point. The manifold is shown to be a key enabler for control-oriented flow estimation.
Source: Journal of Fluid Mechanics
DOI: 10.1017/jfm.2024.593
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
Funders: Comunidad de Madrid, Ministerio de Ciencia, Innovación y Universidades, University of Cambridge, European Commission, National Natural Science Foundation of China, Harbin Institute of Technology, Universidad Carlos III de Madrid, Horizon 2020 Framework Programme, European Regional Development Fund, Agencia Estatal de Investigación, Basic and Applied Basic Research Foundation of Guangdong Province
Awards: G1105844351, G1175115578, G1240329685, G1603176675, G1944816339, G210041235, G2262748287, G2601397997, G2628364946, G300979063, G34346518, G3480869486, G3681454997, G387506447, G4042783231, G410571670, G4107125871, G425668148, G4277366295, G447568237, G451917667, G4547590489, G4787430317, G488842982, G491712853, G4937468798, G5080475149, G5175525644, G5555131222, G5575567616, G5783360633, G5967599077, G624877701, G683931746, G7084143925, G7805626310, G7863371703, G8497050386, G8684520763, G929308097
OpenAlex ID: W4402993478