Abstract
A time-resolved particle image velocimetry (TR-PIV) system has been employed to investigate the unsteady propagation of upstream wakes in a low-pressure turbine cascade. Data are obtained in the steady state condition and for two passing wake reduced frequencies. The study is focused on the identification and split of the different dynamics responsible for deterministic and random oscillations, thus loss generation by means of a new procedure based on proper orthogonal decomposition (POD). The paper takes advantage of the properties of POD that reduce the data set to a low number of modes that represent the most energetic dynamics of the system. It is clearly shown that the phase averaged flow field can be represented by a few number of POD modes related to the wake passing event for the unsteady cases. Proper orthogonal decomposition is also able to capture flow features affecting the instantaneous flow field not directly related to the wake passage (i.e., the vortex shedding phenomenon induced by the intermittent separation developing between adjacent wakes), which are smeared out in the phase averaged results. A procedure exploiting the biorthogonality condition of the POD modes, and the related temporal coefficients, has been developed for the quantification of the contribution due to the different POD modes to the overall turbulence kinetic energy production, or, equivalently, the mean flow energy dissipation rate. Results into the paper clearly show that losses due to wake migration, boundary layer and vortex shedding related phenomena can be distinguished and separately quantified for the different tested conditions.
Highlights
The deep understanding of the unsteady flow in turbomachines is a complicated task due to the superposition of different sources of unsteadiness
Time-mean boundary layer separation seems to not be present either for the f + = 0.35 condition, even though lower velocity levels can be observed close to the wall, midway between the two previous conditions, as expected. As it will be discussed in the following, this latter condition is characterized by an unsteady separation, and proper orthogonal decomposition (POD) will be shown to clearly capture the dominant dynamics responsible for flow oscillations and losses in the different conditions
POD has been applied to time-resolved particle image velocimetry (TR-PIV) data in order to characterize the different phenomena responsible for TKE production affecting the unsteady behavior of an low pressure turbine (LPT) blade
Summary
The deep understanding of the unsteady flow in turbomachines is a complicated task due to the superposition of different sources of unsteadiness. The phase-averaged treatment of experimental data allows the detailed analysis of the periodic flow due to the wake, while the random part of flow oscillations can be only statistically described [11] In both cases, all the unsteadiness sources not directly related to the wake passing frequency are not resolved, and their role in the loss generation mechanisms remains substantially obscure. The effects of the reduced frequency variation (from the steady state to the nominal unsteady condition) on the main dynamics inducing flow oscillations (i.e., boundary layer separation and incoming wake effects) will be deeply characterized by means of POD modes and related temporal coefficients. The possibility to separate the loss contribution due to the different dynamics can provide a useful tool to establish or verify optimization criteria, contributing to the development of high efficiency new generation turbine stages
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