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contributor authorLee, Wen Yao
contributor authorDawes, William N.
contributor authorCoull, John D.
date accessioned2022-02-04T22:22:54Z
date available2022-02-04T22:22:54Z
date copyright9/11/2020 12:00:00 AM
date issued2020
identifier issn0889-504X
identifier otherturbo_142_9_091007.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4275450
description abstractCasting deviations introduce geometric variability that impacts the aerodynamic performance of turbomachinery. These effects are studied for a high-pressure turbine rotor blade from a modern aero-engine. A sample of 197 blades were measured using structured-light three-dimensional scanning, and the performance of each blade is quantified using Reynolds-averaged Navier–Stokes (RANS) simulations. Casting variation is typically managed by applying geometric tolerances to determine the suitability of a component for service. The analysis demonstrates that this approach may not be optimal since it does not necessarily align with performance, in particular the capacity and efficiency. Alternatively, functional acceptance based on the predicted performance of each blade removes the uncertainty associated with geometric tolerancing and gives better performance control. Building on these findings, the paper proposes a method to set the orientation of the fir-tree, which is machined after casting. By customizing the alignment of each blade, performance variability and scrap rates can be significantly reduced. The method uses predictions of performance to reorient the castings to compensate for manufacturing-induced errors, without changing the design-intent blade geometry and with minimal changes to the manufacturing facility.
publisherThe American Society of Mechanical Engineers (ASME)
titlePhysics-Based Part Orientation and Sentencing: A Solution to Manufacturing Variability
typeJournal Paper
journal volume142
journal issue10
journal titleJournal of Turbomachinery
identifier doi10.1115/1.4047613
journal fristpage0101001-1
journal lastpage0101001-10
page10
treeJournal of Turbomachinery:;2020:;volume( 142 ):;issue: 010
contenttypeFulltext


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