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Research Papers

Accurate Estimation of Profile Losses and Analysis of Loss Generation Mechanisms in a Turbine Cascade

[+] Author and Article Information
D. Lengani

DIME—Universitá di Genova,
Genova I-16145, Italy
e-mail: davide.lengani@edu.unige.it

D. Simoni

DIME—Universitá di Genova,
Genova I-16145, Italy
e-mail: daniele.simoni@unige.it

M. Ubaldi

DIME—Universitá di Genova,
Genova I-16145, Italy
e-mail: marina.ubaldi@unige.it

P. Zunino

DIME—Universitá di Genova,
Genova I-16145, Italy
e-mail: pietro.zunino@unige.it

F. Bertini

GE AvioAero S.r.l.,
Torino I-10040, Italy
e-mail: francesco.bertini@avioaero.it

V. Michelassi

GE Oil & Gas S.r.l.,
Firenze I-50127, Italy
e-mail: vittorio.michelassi@ge.com

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received August 6, 2017; final manuscript received August 30, 2017; published online October 3, 2017. Editor: Kenneth Hall.

J. Turbomach 139(12), 121007 (Oct 03, 2017) (9 pages) Paper No: TURBO-17-1106; doi: 10.1115/1.4037858 History: Received August 06, 2017; Revised August 30, 2017

The paper analyzes losses and the loss generation mechanisms in a low-pressure turbine (LPT) cascade by proper orthogonal decomposition (POD) applied to measurements. Total pressure probes and time-resolved particle image velocimetry (TR-PIV) are used to determine the flow field and performance of the blade with steady and unsteady inflow conditions varying the flow incidence. The total pressure loss coefficient is computed by traversing two Kiel probes upstream and downstream of the cascade simultaneously. This procedure allows a very accurate estimation of the total pressure loss coefficient also in the potential flow region affected by incoming wake migration. The TR-PIV investigation concentrates on the aft portion of the suction side boundary layer downstream of peak suction. In this adverse pressure gradient region, the interaction between the wake and the boundary layer is the strongest, and it leads to the largest deviation from a steady loss mechanism. POD applied to this portion of the domain provides a statistical representation of the flow oscillations by splitting the effects induced by the different dynamics. The paper also describes how POD can dissect the loss generation mechanisms by separating the contributions to the Reynolds stress tensor from the different modes. The steady condition loss generation, driven by boundary layer streaks and separation, is augmented in the presence of incoming wakes by the wake–boundary layer interaction and by the wake dilation mechanism. Wake migration losses have been found to be almost insensitive to incidence variation between nominal and negative (up to −9 deg) while at positive incidence, the losses have a steep increase due to the alteration of the wake path induced by the different loading distribution.

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References

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Figures

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Fig. 1

Sketch of the test section and PIV investigated area

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Fig. 2

Blade loading distributions

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Fig. 3

Time-mean streamwise velocity component (left), RMS of streamwise velocity fluctuation (right): (a) steady-state case, (b) nominal incidence case, (c) negative incidence case, and (d) positive incidence case

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Fig. 4

Vectorial representation of POD modes and contour plot of the production rate of TKE per mode (per unit length), first two modes: (a) nominal incidence case, (b) negative incidence case, and (c) positive incidence case

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Fig. 5

Vectorial representation of POD modes and contour plot of the production rate of TKE per mode (per unit length), zoom in the boundary layer region for the steady case, on the left. Fourier spectra of POD eigenvectors, on the right.

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Fig. 6

Vectorial representation of POD modes and contour plot of the production rate of TKE per mode (per unit length), zoom in the boundary layer region for the unsteady cases, on the left. Fourier spectra of POD eigenvectors, on the right.

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Fig. 7

Pressure loss coefficient, comparison between steady and unsteady cases at nominal incidence

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Fig. 8

Distribution of loss sources at different incidence angles

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