Learning wake regimes from snapshot data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Fluid wakes are often categorized by visual inspection according to the number and grouping of vortices shed per cycle (e.g., 2S, 2P, P+S). While such categorizations have proven useful for describing and comparing wakes, the criterion excludes features that are essential to a wake's evolution (i.e., the relative positions and strengths of the shed vortices). For example, not all 2P wakes exhibit the same dynamics; thus, the evolution of wake patterns among 2P wakes can be markedly distinct. Here, we explore the notion of labeling wakes according their dynamics based on empirical snapshot data. Snapshots of the velocity field are reduced to a representative feature vector, which is then processed using machine learning techniques tailored to the task of wake regime learning. The wake regime learning framework is evaluated on an idealized 2P wake model, which can be configured to simulate different (known) dynamical regimes. A simple version of the wake regime learning framework successfully discriminates between dynamically distinct 2P wakes. The results presented here suggest that the wake regime learning perspective may facilitate the development of a new dynamics-based wake labeling convention in the future.

Original languageEnglish (US)
Title of host publication46th AIAA Fluid Dynamics Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624104367
DOIs
StatePublished - 2016
Event46th AIAA Fluid Dynamics Conference, 2016 - Washington, United States
Duration: Jun 13 2016Jun 17 2016

Publication series

Name46th AIAA Fluid Dynamics Conference

Other

Other46th AIAA Fluid Dynamics Conference, 2016
Country/TerritoryUnited States
CityWashington
Period6/13/166/17/16

Bibliographical note

Publisher Copyright:
© 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

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