Analytical and numerical identification of the skeleton thermal conductivity of a geopolymer foam using a multi-scale analysis AITranslate
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Highlights • Samples of highly porous geopolymer foam were successfully made. • The overall thermal conductivity of samples were measured. • Multi-scale analytical approach was used to determine thermal skeleton thermal conductivity. • Finite element modeling taking into account the microstructure characteristics was used. • Analytical, numerical and experimental results highlight reliable numerical values. This work focuses on the identification of the thermal conductivity of potassium geopolymer foams by experimental and numerical/analytical approaches. The porous foams, synthesized by a direct foaming process, display pore volume fractions ranging between 65% and 85% with thermal conductivities from 0.35 to 0.12 W m−1 K−1 respectively. A quantitative characterization of the pore volume fractions has been made by image analysis, taking into account the multi-scale aspect of these foams. Motivated by the character of the microstructure, an inverse multi-scale analytical method was applied to find the value of the thermal conductivity of the skeleton λs. In fact, due to the range of pore volume fractions, standard analytical relations describing thermal conductivity were found to be of limited use. In order to take into account the local aspects of the microstructure and to treat the actual pore volume fractions, an inverse numerical approach, based on a finite element calculation coupled with a homogenization method, was used. As a result, the multi-scale analytical approach evaluates the value of λs between 0.95 and 1.19 W m−1 K−1. Numerical results, taking faithfully into account local parameters of the pore network, give λs values within the range 1.09–1.12 W m−1 K−1. The numerical values of λs are in agreement with values obtained in the literature.
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DOI:https://doi.org/10.1016/j.commatsci.2013.09.062
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Highlights • Samples of highly porous geopolymer foam were successfully made. • The overall thermal conductivity of samples were measured. • Multi-scale analytical approach was used to determine thermal skeleton thermal conductivity. • Finite element modeling taking into account the microstructure characteristics was used. • Analytical, numerical and experimental results highlight reliable numerical values. This work focuses on the identification of the thermal conductivity of potassium geopolymer foams by experimental and numerical/analytical approaches. The porous foams, synthesized by a direct foaming process, display pore volume fractions ranging between 65% and 85% with thermal conductivities from 0.35 to 0.12 W m−1 K−1 respectively. A quantitative characterization of the pore volume fractions has been made by image analysis, taking into account the multi-scale aspect of these foams. Motivated by the character of the microstructure, an inverse multi-scale analytical method was applied to find the value of the thermal conductivity of the skeleton λs. In fact, due to the range of pore volume fractions, standard analytical relations describing thermal conductivity were found to be of limited use. In order to take into account the local aspects of the microstructure and to treat the actual pore volume fractions, an inverse numerical approach, based on a finite element calculation coupled with a homogenization method, was used. As a result, the multi-scale analytical approach evaluates the value of λs between 0.95 and 1.19 W m−1 K−1. Numerical results, taking faithfully into account local parameters of the pore network, give λs values within the range 1.09–1.12 W m−1 K−1. The numerical values of λs are in agreement with values obtained in the literature.
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| GB/T 7714-2015 | [1] Joseph Henon, Fabienne Pennec, Arnaud Alzina, et al. Computational Materials Science, 2014(82). DOI:10.1016/j.commatsci.2013.09.062. |
| MLA | [1] Joseph Henon, et al., Computational Materials Science, no. 82, 2014, https://doi.org/10.1016/j.commatsci.2013.09.062. |
| APA | [1] Joseph Henon, Fabienne Pennec, Arnaud Alzina, Joseph Absi, David Stanley Smith, & Sylvie Rossignol. (2014). Computational Materials Science(82). https://doi.org/10.1016/j.commatsci.2013.09.062 |
| IEEE | [1] Joseph Henon, Fabienne Pennec, Arnaud Alzina, Joseph Absi, David Stanley Smith, and Sylvie Rossignol, Computational Materials Science, no. 82, 2014, doi: 10.1016/j.commatsci.2013.09.062. |
