Akademik Veri Yönetim Sistemi
Intelligent and Fuzzy Systems 2022, İstanbul, Türkiye, 19 Temmuz 2022, ss.1-8
Wakes
and vortices are commonly observed in fluid flows around bluff bodies, a phenomenon which is called vortex shedding. Such
vortices are named as von Kármán vortices since their first investigation is
performed by the leading fluid dynamicist Theodore von Kármán. Although
initially observed in the studies of fluid flows, the same phenomenon
can also be observed in different branches of mediums such as
condensates. It is possible to model these vortices using
numerical techniques that solve the Navier-Stokes equations, however, some
dynamic equations such as the complex Ginzburg-Landau (GL) equation is another
frequently used model for these purposes. In this paper, we solve the GL
equation using a spectral scheme and Runge-Kutta time integrator to
simulate the dynamics of von Kármán vortices around a cylinder.
The prediction of temporal dynamics is of crucial importance to avoid excessive
shedding, resonance, and structural damage of the engineering structures. With
this motivation, here we examine the predictability of the von Kármán vortices using the adaptive neuro-fuzzy
inference system (ANFIS) which relies on a rule-based relationship between
input values and output values that are learned adaptively by being trained
with the data set analyzed. We show that the temporal dynamics of the
von Kármán vortices can be adequately performed by ANFIS and we report the
prediction success of the ANFIS in the solution of this complex prediction
problem measured by the coefficient of determination and the root mean square error values. Our results can be used for
predicting, interpolating, and extrapolating vortex data to analyze fluid
dynamics problems and to develop control strategies for avoiding structural
failures.