A benchmark solidification experiment on an Sn–10%wtBi alloy

Quillet, G.; Ciobanas, A.; Lehmann, Peter; Fautrelle, Yves

doi:10.1016/j.ijheatmasstransfer.2006.07.030

Cited by 32 publications

(24 citation statements)

References 10 publications

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“…[5][6][7][10][11][12][13][14][15][16][17][18] A constant need persists for a better scientific understanding of the physical process for modeling and predicting the transport phenomena of any solidification process, which needs to be validated with controlled experiments. [5,19,20] Limited in situ experimental evidence is available of solid movement during solidification reported in the literature, and most of those experiments were performed with transparent metal-analog systems. [21][22][23][24][25] Appolaire et al [25] described an experimental arrangement for the solidification of an undercooled NH 4 Cl-H 2 O solution.…”

Section: Introductionmentioning

confidence: 99%

Grain Floatation During Equiaxed Solidification of an Al-Cu Alloy in a Side-Cooled Cavity: Part I—Experimental Studies

Kumar

Walker

Sundarraj

et al. 2011

Metall Mater Trans B

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Experimental studies were performed to investigate the role and influence of grain movement on macrosegregation and microstructure evolution during equiaxed solidification. Casting experiments were performed with a grain-refined Al-Cu alloy in a rectangular sand mold. For the aluminum alloy studied, the equiaxed grains are lighter than the bulk melt and thus float up. Experiments were designed to investigate floatation phenomena of equiaxed grains in the presence of thermosolutal convection. Cooling curves were recorded at key locations in both the casting and the chill. Quantitative image analysis and spatial chemical analysis were performed on the solidified casting to observe the chemical and microstructural inhomogeneity created by the melt convection and solid floatation. Several notable features that can be attributed to grain movement were observed in temperature histories, macrosegregation patterns, and microstructures. In our experiments, the floatation of grains influences the thermal conditions and the overall flow direction in the casting cavity. In some cases, the induced flow resulting from the grain movement caused a flow reversal. This in turn influences the solidification direction, microstructure evolution, and the overall macrosegregation behavior.

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Section: Introductionmentioning

confidence: 99%

Grain Floatation During Equiaxed Solidification of an Al-Cu Alloy in a Side-Cooled Cavity: Part I—Experimental Studies

Kumar

Walker

Sundarraj

et al. 2011

Metall Mater Trans B

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“…Important effort is developed in this direction using both numerical (closure problems [13], phase field [26]) and experimental methods (X-ray imaging [27]). In parallel, benchmark on alloy solidification such as the one presented in [28] are expected to provide important information concerning numerical approximation and comparison with experiments.…”

Section: Resultsmentioning

confidence: 99%

Channel segregation during columnar solidification influence of inertia

Kumar¹,

Založnik²,

Combeau³

et al. 2012

AIP Conference Proceedings

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Abstract. The numerical prediction of channel segregations during two-dimensional columnar solidification of Sn-Pb alloy is analyzed when inertia is considered in the dendritic mushy zone. This contribution is included in the momentum transport equation through the quadratic Forchheimer correction term. A significant influence of the Forchheimer term in the vicinity of the liquidus region is found. The natural convective flow field and therefore the global shape of the mushy zone are modified giving rise to significant decrease of channel segregation, due to additional drag in these regions.

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“…Voller and Vušanović have used this CDF approach to investigate the grid dependence of macrosegregation simulations. A result, that when used in conjunction with experimental measurements for a Sn-10%Bi alloy [3] suggests that the positive segregated region may have a power-law tail. Thus indicating that as numerical or measurement resolution is refined one will, up to the formation of a second phase, encounter higher and higher values of solute concentration.…”

Section: Statistical Measuresmentioning

confidence: 93%

Simple metrics for verification and validation of macrosegregation model predictions

Vušanović

Voller

2016

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. While the numerical simulation of macrosegregation is now a common place activity efforts can still be enhanced by developing quantitative measures of the results. Here, on treating the nodal field of concentration predictions from a macrosegregation simulation as a sample from a statistical distribution, we demonstrate how statistical measures can be used in verification and validation. The first set of such measures is simply the central moments of the distribution, i.e., the mean, the standard deviation, and the skewness; measurements that provide quantitative checks of mass balance and grid convergence. In addition, building on recently reported work [1], we also demonstrate how to construct and use a cumulative distribution function (CDF) of the nodal concentration field; a measure that can be used to determine the fraction of the casting volume concentrations less than a specified value. We show how the CDF can be used to compare the influence of various process conditions and phenomena related to domain size, cooling rate, permeability, and micro-segregation. IntroductionCurrent macrosegregation simulations are advanced and when coupled with sophisticated graphical outputs lead to detailed predictions of the distribution of solute in cast products. Ultimately our objective with these simulations is twofold. In the first place we might like to compare differences in process settings such as cooling conditions, and geometry. Secondly, we may be interested in understanding the basic phenomena underlying the simulations, e.g., microsegregation, and mushy region flow behavior. Clearly, graphical output provides an immediate qualitative evaluation of changing process settings or model phenomena. But this powerful tool is lacking in obvious quantitative measures that can be used in case to case comparisons. One way of obtaining quantitative measures is to treat the predicted nodal values of solute concentrations from a macrosegregation calculation as a statistical distribution. In this way measures of central moments and derived distribution functions [1] can be used as quantitative representations of macrosegregation behavior. The objective of this paper is to introduce these measures and, through a number of example macrosegregation simulations, show how they can be used to evaluate process settings and phenomenological models.

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A benchmark solidification experiment on an Sn–10%wtBi alloy

Cited by 32 publications

References 10 publications

Grain Floatation During Equiaxed Solidification of an Al-Cu Alloy in a Side-Cooled Cavity: Part I—Experimental Studies

Grain Floatation During Equiaxed Solidification of an Al-Cu Alloy in a Side-Cooled Cavity: Part I—Experimental Studies

Channel segregation during columnar solidification influence of inertia

Simple metrics for verification and validation of macrosegregation model predictions

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