Simulating Phase Change Heat Transfer Using Comsol and Fluent: Effect of the Mushy-Zone Constant

Kheirabadi, Ali C.; Groulx, Dominic

doi:10.1615/computthermalscien.2016014279

Cited by 25 publications

(12 citation statements)

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“…In other words, tuning the value of C is essential for every set of boundary conditions and material properties, which requires considerable trial and error evaluation. Previous studies on the influence of the permeability coefficient in simulations of melting and solidification have mainly concentrated on finding a value for C to diminish discrepancies between numerical and experimental data for a specific problem (see, for instance, [27][28][29][30][31][32][33]). Additionally, they have often focused on the phase-change materials for thermal energy storage applications for which material properties and operating conditions differ from those for materials processing applications (e.g., casting, welding and additive manufacturing).…”

Section: Introductionmentioning

confidence: 99%

“…Fadl and Eames [21] reported that the numerical predictions are less sensitive to the value of C in regions where heat transfer is dominated by conduction. Assessing the degree of sensitivity of the numerical prediction to the value of C for different materials and boundary conditions is an open question, despite its significance has been emphasised in the literature [21,22,30,38].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Numerical Predictions to the Permeability Coefficient in Simulations of Melting and Solidification Using the Enthalpy-Porosity Method

2019

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The high degree of uncertainty and conflicting literature data on the value of the permeability coefficient (also known as the mushy zone constant), which aims to dampen fluid velocities in the mushy zone and suppress them in solid regions, is a critical drawback when using the fixed-grid enthalpy-porosity technique for modelling non-isothermal phase-change processes. In the present study, the sensitivity of numerical predictions to the value of this coefficient was scrutinised. Using finite-volume based numerical simulations of isothermal and non-isothermal melting and solidification problems, the causes of increased sensitivity were identified. It was found that depending on the mushy-zone thickness and the velocity field, the solid–liquid interface morphology and the rate of phase-change are sensitive to the permeability coefficient. It is demonstrated that numerical predictions of an isothermal phase-change problem are independent of the permeability coefficient for sufficiently fine meshes. It is also shown that sensitivity to the choice of permeability coefficient can be assessed by means of an appropriately defined Péclet number.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity of Numerical Predictions to the Permeability Coefficient in Simulations of Melting and Solidification Using the Enthalpy-Porosity Method

2019

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“…with the modeling constants A m = 10 6 kg m −3 s −1 and ε = 10 −3 (Samara et al, 2012;Kheirabadi and Groulx, 2015). The function S(T ) forms a sink-term in the momentum equation.…”

Section: Momentum Equationmentioning

confidence: 99%

Convective Melting Modeling Approach for Phase Change Materials with Variable Boundary Heating

Rubinetti

Weiss

Chaudhuri

et al. 2018

Linköping Electronic Conference Proceedings

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The present study conceives a numerical model for phase change materials following the apparent heat capacity method where the phase change occurs within a chosen temperature interval. A multiphysical modeling approach to satisfy the coupled momentum, energy and continuity conservation equations whilst avoiding numerical singularities is applied. By means of a 2D test-case geometry with variable boundary heating the influence of natural convection within the melted liquid zone is visualized. Corresponding non-dimensional governing equations are analysed to quantify the dominant contributing terms. It turns out that for sufficiently small Grashof number, or consequently small Rayleigh numbers the influence of natural convection can be neglected, thus simplyfing the problem substantially. The modeling approach has been adapted to a 2D-axisymmetric geometry within the scope of experimental validation. The simulation results and experimental data show reasonably good agreement. The model is numerically stable and suitable to facilitate design of latent heat storage systems.

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“…One of these is C in equation (37). As other researchers have noted, the choice of C can have significant effect on flow patterns and melt boundaries [118,120].…”

Section: Discretization Schemementioning

confidence: 99%

“…x • grows larger [118,120]. In the present work, C is set to a relatively low value of 10 4 s -1 , while B is equal to 0.001.…”

Section: Governing Equations and Discretization Schemementioning

confidence: 99%

Improving the performance of finned latent heat thermal storage devices using a Cartesian grid solver and machine-learning optimization techniques

Augspurger¹

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Simulating Phase Change Heat Transfer Using Comsol and Fluent: Effect of the Mushy-Zone Constant

Cited by 25 publications

References 0 publications

Sensitivity of Numerical Predictions to the Permeability Coefficient in Simulations of Melting and Solidification Using the Enthalpy-Porosity Method

Sensitivity of Numerical Predictions to the Permeability Coefficient in Simulations of Melting and Solidification Using the Enthalpy-Porosity Method

Convective Melting Modeling Approach for Phase Change Materials with Variable Boundary Heating

Improving the performance of finned latent heat thermal storage devices using a Cartesian grid solver and machine-learning optimization techniques

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