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Appl. Sci. 2016, 6(12), 420; doi:10.3390/app6120420

Axial Turbine Cascade Correlation

Petrobras S.A., Av. Henrique Valadares, 28, Torre A, 9th floor, Centro, Rio de Janeiro/RJ 20231-030, Brazil
Academic Editors: Pericles Pilidis and Theoklis Nikolaidis
Received: 10 November 2016 / Revised: 25 November 2016 / Accepted: 6 December 2016 / Published: 10 December 2016
(This article belongs to the Special Issue Gas Turbines Propulsion and Power)
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Abstract

The performance simulation of an axial turbine is achieved in a simple way from the calculation of velocity diagrams. For this purpose, a reliable loss model is needed for the flow through each stationary or rotating axial blade cascade. A loss coefficient assessment is conducted through the establishment of a correlation between the maximum profile velocity ratio and a circulation parameter, dedicated specifically to turbine cascades. A detailed examination of published wind tunnel cascade tests available in the literature provides enough experimental data to support the proposed correlation. Afterwards, the surface diffusion is quantified and the total pressure loss estimation is obtained from the boundary layer momentum thickness and conservation equations for the downstream flow. Further validation of the proposed loss model is presented from published experimental results in turbine cascades and stages. The simulation methodology is also demonstrated in two single-stage steam turbine units applied to the oil refining industry, in comparison with performance factory tests results. View Full-Text
Keywords: axial turbine; profile loss; diffusion factor; cascade correlation; performance estimation axial turbine; profile loss; diffusion factor; cascade correlation; performance estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Leibsohn Martins, G. Axial Turbine Cascade Correlation. Appl. Sci. 2016, 6, 420.

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