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contributor authorPrzytarski, Pawel J.
contributor authorLengani, Davide
contributor authorSandberg, Richard
date accessioned2025-04-21T10:15:58Z
date available2025-04-21T10:15:58Z
date copyright12/17/2024 12:00:00 AM
date issued2024
identifier issn0889-504X
identifier otherturbo_147_4_041014.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305831
description abstractStagnation pressure loss coefficient is still the most commonly used loss metric for performance evaluation and is routinely used to validate simulations. This is because it is easy to measure and readily available from the experiments. However, it was previously shown (TURBO-24-1006) that the stagnation pressure loss coefficient can become unreliable when high levels of inflow unsteadiness are present. As the current design trends are moving toward more compact machines and higher work coefficients, the levels of unsteadiness are likely to increase. It is therefore desirable to assess how higher inflow unsteadiness levels affect the performance of new blade designs and what loss metrics should be used to reliably estimate it. Motivated by the need to understand how performance prediction changes under highly unsteady inflow conditions, we perform a series of high-fidelity scale-resolving simulations. We use this data to construct energy budgets for a variety of mid-span compressor cases with varying Reynolds numbers and inflow turbulence intensities. This allows us to systematically assess the impact of inflow conditions on loss prediction when using stagnation pressure based loss metrics. Stagnation pressure loss coefficient was found to be least reliable for the high inflow turbulence intensities and at high Reynolds numbers.
publisherThe American Society of Mechanical Engineers (ASME)
titleThe Impact of Inflow Unsteadiness on Loss Prediction
typeJournal Paper
journal volume147
journal issue4
journal titleJournal of Turbomachinery
identifier doi10.1115/1.4067241
journal fristpage41014-1
journal lastpage41014-8
page8
treeJournal of Turbomachinery:;2024:;volume( 147 ):;issue: 004
contenttypeFulltext


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