Prediction of separated flow transition using les and transitional RANS model

Publikation: KonferenzbeitragPaper


The goal of this work is to predict the boundary layer transition
induced by a separation bubble on the suction side of a turbine
blade of a linear turbine cascade using Large Eddy Simulation
(LES). The numerical simulations refer to the linear turbine
cascade T106D-EIZ tested at the Institute for Jet Propulsion of
the Bundeswehr University Munich (Germany). The blade pitch
was increased compared to the design point in order to have a
higher load and enhance the formation of a separation bubble at
the suction side of the blade. Different flow configurations were
tested and the transition of the boundary layer was evaluated.
For the numerical case, the operating condition with an inlet turbulence
below 1% was used. In the first part of this work, the
LES setup is discussed. A modified Smagorinsky subgrid-scale
model is used to reduce the turbulent viscosity in the region closest
to the wall. The computational grid is designed according
to the information coming from the Taylor and the Kolmogorov
length scales. These parameters are found from RANS k-omega
SST simulations. The fifth-order accurate WENO scheme was
used for the computation of the cell fluxes. In the second part of
the work, a comparison between the results of the LES simulations
and of the RANS k-omega SST simulations with the gamma-Re theta
transition model is done. Integral and statistical parameters of
the boundary layer from the simulations with the two models are
evaluated and compared. The ability of the LES and the RANS
models to predict the boundary layer evolution along the blade
profile and the point of separation will be discussed.
PublikationsstatusVeröffentlicht - 2019
VeranstaltungASME Turbo Expo 2019: Turbomachinery Technical Conference & Exhibition - Phoenix, USA / Vereinigte Staaten
Dauer: 17 Jun 201921 Jun 2019


KonferenzASME Turbo Expo 2019
LandUSA / Vereinigte Staaten

Fields of Expertise

  • Mobility & Production

Fingerprint Untersuchen Sie die Forschungsthemen von „Prediction of separated flow transition using les and transitional RANS model“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren