A verification of thermophysical properties of a porous ceramic investment casting mould using commercial computational fluid dynamics software

Defects in cast metals remain a common problem in many areas of the foundry industry, particularly in the investment casting of large area, thin-walled components for aerospace applications. During previous research, the thermophysical properties, density and porosity of a fibre reinforced ceramic i...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 861; no. 1; pp. 12036 - 12043
Main Authors Jones, C A, Jolly, M R, Jarfors, A E W, Irwin, M, Svenningsson, R, Steggo, J, Eriksson, J
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.05.2020
Subjects
Aerodynamics
Casting
Casting defects
Ceramic fibers
Ceramic mold casting
Ceramic molding materials
Ceramics
Computational fluid dynamics
Fiber reinforced materials
Investment casting
Nickel base alloys
Programming languages
Superalloys
Temperature profiles
Thermocouples
Thermophysical properties
Three dimensional flow
Verification
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Summary:Defects in cast metals remain a common problem in many areas of the foundry industry, particularly in the investment casting of large area, thin-walled components for aerospace applications. During previous research, the thermophysical properties, density and porosity of a fibre reinforced ceramic investment casting mould were determined using several experimental techniques. Without verification, these experimental results remain nothing more than educated guesswork. The purpose of this study is to verify previous results and to more fully characterise the ceramic mould material with complementary measurements. A commercially available computational fluid dynamic (CFD) simulation package, Flow-3D®, was used in conjunction with a full-scale Ni-superalloy (IN718) casting to assess the accuracy of these results. By placing thermocouples strategically across the mould thickness, temperature profiles were determined and compared directly to predicted profiles extracted from the simulation by a custom-written Python script.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/861/1/012036