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SUMMARY:MEchanics GAthering -MEGA- Seminar: A cluster-based network analys
 is of post stall load fluctuations
DTSTART:20220407T161500
DTEND:20220407T173000
DTSTAMP:20260413T224226Z
UID:574136d3ca933bf1d934562b35efd3fd63c6d7f605001532e06f1ebc
CATEGORIES:Conferences - Seminars
DESCRIPTION:Fatma Ayancik (UNFoLD\, EPFL)\nAbstract The cycle-to-cycle v
 ariability of the vortex shedding and the associated fluctuations of the a
 erodynamic force coefficients during the dynamic stall are physical phenom
 ena. This phenomenon always drops beneath the researcher’s radar due to 
 phase-averaged presentation of velocity fields and lift histories. A corre
 ct representation of the maximal and minimal bounds of aerodynamic force f
 luctuations is important for the accurate prediction of the aerodynamic ef
 ficiency and the dynamic stall hysteresis of wind turbines or helicopter b
 lades. We used cluster-based analysis to identify the chain of events lead
 ing to flow separation and associated post-stall lift fluctuations and the
 ir bounds. A feature space is spanned by the aerodynamic forces. The featu
 re space of trajectories is then partitioned into discrete clusters with s
 imilar aerodynamic characteristics using unsupervised clustering. Each clu
 ster corresponds to a characteristic phase of the flow in the feature spac
 e\, and it is represented with a cluster centroid. Statistical properties 
 of cycle-to-cycle variations are determined at each cluster centroid. We p
 ropose an inverse model to rebuild the experimental data from the informat
 ion obtained from clustered statistical properties of cycle-to-cycle varia
 tions. This model would be used to introduce post-stall fluctuations to dy
 namic stall models for the accurate prediction of dynamic stall hysteresis
  without the requirement of additional second-degree terms.\n\nBio Fatma 
 Ayancik is a postdoctoral researcher at UNFoLD\, EPFL. She received his B.
 S. and M.Sc. in mechanical engineering from TOBB ETU and Ph.D. in mechanic
 al engineering from Lehigh University in 2019. She is working on modeling 
 of dynamic stall with data driven methods.
LOCATION:MED 2 2423 https://plan.epfl.ch/?room==MED%202%202423 https://epf
 l.zoom.us/j/64863509346?pwd=RGlkWjRGOVVSaXZxdFdrb3duYy9mZz09
STATUS:CONFIRMED
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