000014963 001__ 14963
000014963 005__ 20161115100207.0
000014963 04107 $$aeng
000014963 046__ $$k2016-08-21
000014963 100__ $$aHack, Philipp
000014963 24500 $$aCharacterization and prediction of streak breakdown using machine learning

000014963 24630 $$n24.$$p24th International Congress of Theoretical and Applied Mechanics - Book of Papers
000014963 260__ $$bInternational Union of Theoretical and Applied Mechanics, 2016
000014963 506__ $$arestricted
000014963 520__ $$2eng$$aCharacteristics of streaks in DNS flow fields of pre-transitional boundary layers are extracted and compared between streaks that induce the formation of turbulent spots via secondary instability and the remainder of the population. The analysis shows that the two classes of streaks differ in a variety of attributes, including their peak amplitude, distance to the wall and size. The data are used in a machinelearning approach to predict transition to turbulence. An artificial neural network identifies the streaks that will induce the formation of turbulent spots. The method is shown to achieve high prediction accuracy at low computational cost which makes the approach suitable for real-time applications.

000014963 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000014963 653__ $$a

000014963 7112_ $$a24th International Congress of Theoretical and Applied Mechanics$$cMontreal (CA)$$d2016-08-21 / 2016-08-26$$gICTAM2016
000014963 720__ $$aHack, Philipp
000014963 8560_ $$ffischerc@itam.cas.cz
000014963 8564_ $$s130662$$uhttps://invenio.itam.cas.cz/record/14963/files/TS.MS01-3.05.pdf$$yOriginal version of the author's contribution as presented on CD,  page 69, code TS.MS01-3.05
.
000014963 962__ $$r13812
000014963 980__ $$aPAPER