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2017 | OriginalPaper | Buchkapitel

Numerical Simulation of Macrosegregation in a 535 Tons Steel Ingot with a Multicomponent-Multiphase Model

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Abstract

To accurately simulate the formation of macrosegregation, a major defect commonly encountered in large ingots, solidification researchers have developed various mathematical models and conducted corresponding steel ingot dissection experiments for validation. A multicomponent and multiphase solidification model was utilized to predict macrosegregation of steel ingots in this research. The model described the multi-phase flow phenomenon during solidification, with the feature of strong coupling among mass, momentum, energy, and species conservation equations. Impact factors as thermo-solutal buoyancy flow, grains sedimentation, and shrinkage-induced flow on the macroscopic scale were taken into consideration. Additionally, the interfacial concentration constraint relations were derived to close the model by solving the solidification paths in the multicomponent alloy system. A finite-volume method was employed to solve the governing equations of the model. In particular, a multi-phase SIMPLEC (semi-implicit method for pressure-linked equations-consistent) algorithm was utilized to solve the velocity-pressure coupling for the specific multiphase flow system. Finally, the model was applied to simulate the macrosegregation in a 535 tons steel ingot. The simulated results were compared with the experimental results and the predictions reproduced the classical macrosegregation patterns. Good agreement is shown generally in quantitative comparisons between experimental results and numerical predictions of carbon, chromium and molybdenum concentration. It is demonstrated that the multicomponent-multiphase solidification model can well predict macrosegregation in steel ingots and help optimize the ingot production process.

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Metadaten
Titel
Numerical Simulation of Macrosegregation in a 535 Tons Steel Ingot with a Multicomponent-Multiphase Model
verfasst von
Kangxin Chen
Wutao Tu
Houfa Shen
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-57864-4_18

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