A Model for Estimation of Viscosities of Complex Metallic and Ionic Melts

Sichen, Du; Bygdén, Johan; Seetharaman, Seshadri

doi:10.1007/bf02650073

Cited by 96 publications

(39 citation statements)

References 15 publications

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“…The values of n O Si ; n O i Si ; and n O i ; can be calculated using Eqs. [9] and [10], i.e., by assuming the system to be a pseudobinary. Each term must then be multiplied to derive the renormalized mole fractions of basic oxides.…”

Section: Methods For Calculating Numbers Of Oxygen Ionsmentioning

confidence: 99%

“…Models by Iida et al [5] and those based on the optical basicity concept [6][7][8] have narrow application ranges. The structural model by Du et al [9] does not consider the real existing structural units, whereas those by Zhang and Jahanshahi, [10] Shu and Zhang, [11] and Nakamoto et al [12] encounter difficulties when calculating the numbers of different types of oxygen ions involving in the model calculation (will be discussed in more detail subsequently). In this work, a new model for viscosity estimation is proposed; it is based on the numbers of bridging oxygen, nonbridging oxygen, and free oxygen present in the melt.…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Modeling Viscosities of CaO-MgO-FeO-MnO-SiO2 Molten Slags

Zhang

Chou

Xue

et al. 2011

Metall Mater Trans B

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A model for estimating the viscosity of silicate melts is proposed in this article. The structural characteristics of a silicate slag can be described by the numbers of the bridging oxygen, nonbridging oxygen, and free oxygen present in the slag. A method of calculating the numbers of the different types of oxygen ions is presented in this article, which involves a simple approximation of ''complete bridge breaking.'' With just a few parameters, the model provides both the temperature and composition dependencies of viscosity for the pure component: SiO 2 ; the binary systems: MgO-SiO 2 , CaO-SiO 2 , FeO-SiO 2 , and MnO-SiO 2 ; the ternary systems: CaOMgO-SiO 2 , CaO-FeO-SiO 2 , MgO-FeO-SiO 2 , and CaO-MnO-SiO 2 ; and the quaternary systems: CaO-MgO-MnO-SiO 2 and CaO-FeO-MnO-SiO 2 . It was found that the ability of different basic metal oxides to decrease viscosity varies and is in the following hierarchy: FeO > MnO > CaO > MgO. Two factors influence the viscosity: The first is related to the mutual interaction among different ions, and the stronger the interaction, the higher the viscosity. The second factor is the size (radius) of basic oxide cation, with viscous flow becoming increasingly more difficult (i.e., viscosity increases) as the cation size increases. However, there is a paradox in the effect of cation radius (of the basic oxide) on the two factors. Thus, varying cation size causes competitive effects; smaller cationic radii give stronger interactions among ions but less hindrance to viscous flow (and vice versa for large cation radii).

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Section: Methods For Calculating Numbers Of Oxygen Ionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Modeling Viscosities of CaO-MgO-FeO-MnO-SiO2 Molten Slags

Zhang

Chou

Xue

et al. 2011

Metall Mater Trans B

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“…These models with constant parameters cannot be applied over a wide composition range. Seetharaman et al (2000), Seetharaman and Sichen (1997) and Sichen et al (1994aSichen et al ( , 1994b estimated the viscosities of ionic melts using a model based on the absolute rate theory for description of flow processes. Although the use of thermodynamic data allows for the calculation of viscosity for wide composition range, the feature of the silicate melts are not directly considered in their models.…”

Section: Network Structured Fluidsmentioning

confidence: 99%

Cold Simulation of momentum transfer of a metal droplet falling through slag systems possessing net-work structured fluids

Mukherjee

Prakash

2011

Chemical Engineering Science

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“…The semi-theoretical method is applied in this paper to predict the viscosity of alloys. The viscosity g of pure liquid metals follows AndradeÕs relationship: [22] gðTÞ ¼ g o expðE=RTÞ ð Eq 7Þ…”

Section: Liquid Viscositymentioning

confidence: 99%

Alloy Thermal Physical Property Prediction Coupled Computational Thermodynamics with Back Diffusion Consideration

Guo

Samonds

2007

J Phs Eqil and Diff

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Simulation technologies are applied extensively in casting industries to understand the heat transfer and fluid transport phenomena and their relationships to the microstructure and the formation of defects. It is critical to have accurate thermo-physical properties as input for reliable simulations of the complex solidification and solid phase transformation processes. The thermo-physical properties can be calculated with the help of thermodynamic calculations of phase stability at given temperatures and compositions. A multicomponent alloy solidification model, coupled with a Gibbs free energy minimization engine and thermodynamic databases, has been developed. A back diffusion model is integrated so that the solidification conditions, such as cooling rate, can be taken into account.

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A Model for Estimation of Viscosities of Complex Metallic and Ionic Melts

Cited by 96 publications

References 15 publications

Modeling Viscosities of CaO-MgO-FeO-MnO-SiO2 Molten Slags

Modeling Viscosities of CaO-MgO-FeO-MnO-SiO2 Molten Slags

Cold Simulation of momentum transfer of a metal droplet falling through slag systems possessing net-work structured fluids

Alloy Thermal Physical Property Prediction Coupled Computational Thermodynamics with Back Diffusion Consideration

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