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Numerical modelling of tilt casting process for γ-TiAl alloys

Numerical modelling of tilt casting process for γ-TiAl alloys

Wang, H., Djambazov, G. ORCID: 0000-0001-8812-1269, Pericleous, K. ORCID: 0000-0002-7426-9999, Harding, R.A. and Wickins, M. (2012) Numerical modelling of tilt casting process for γ-TiAl alloys. International Journal of Cast Metals Research, 25 (2). pp. 65-74. ISSN 1364-0461 (Print), 1743-1336 (Online) (doi:https://doi.org/10.1179/1743133611Y.0000000024)

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Abstract

The tilt casting method is used to achieve tranquil filling of γ-TiAl turbine blades up to 400 mm long. The reactive titanium alloy is induction melted in a cold crucible, and the crucible with the attached mould is then rotated through 180° to transfer the metal into the mould. In the cold crucible, heat losses to the water cooled copper walls and base limit the superheat available, increasing the risk of premature freezing during mould filling. A compromise is required between fast and slow rotations to minimise the casting defects, such as misruns or gas entrainment. Simulations are presented using the authors’ Computational Fluid Dynamics code with several novel developments in front tracking, heat transfer algorithms and turbulence model adaptation, which accounts for an advancing solid front. The computational results are validated against prototype castings produced at the University of Birmingham, and the model is then used to optimise the tilt casting process.

Item Type: Article
Additional Information: [1] First published: 1 April 2012. [2] Published as: International Journal of Cast Metals Research, (2012), Vol. 25, (2), pp. 65-74.
Uncontrolled Keywords: tilt casting, 3D computational model, gamma-TiAl alloys, process parameters, interface tracking
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TS Manufactures
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group
Related URLs:
Last Modified: 02 Mar 2019 15:52
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/7733

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