2021
DOI: 10.1007/s11440-021-01244-3
|View full text |Cite
|
Sign up to set email alerts
|

Thermal properties of GMZ bentonite pellet mixtures subjected to different temperatures for high-level radioactive waste repository

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
7
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
7

Relationship

1
6

Authors

Journals

citations
Cited by 36 publications
(7 citation statements)
references
References 42 publications
0
7
0
Order By: Relevance
“…Over the past decades, much research have explored the thermal properties of various types of bentonite. Related experiments can be found in the works of Madsen (1998) and Dixon (2019) on MX80 bentonite [9,10], suggesting that thermal properties can be affected by material source, texture, and dry density, Villar (2000) and Rutqvist (2020) on Febex bentonite, proposing that the thermal conductivity is linearly dependent on saturation in the numerical model [11,12], Ould-Lahoucine et al (2002) and Huang (2020) on Kunigel-V1 bentonite [13,14], which does not behave very differently from MX80 in terms of the moisture distribution and heat transfer characteristics with the same boundary conditions assumed in the simulation model, Chen and Huang (2004) on ZH-clay which is found to be a temperature-sensitive Ca-bentonite produced in Taiwan [15], Ye et al (2010) and Xu et al (2021) on GMZ bentonite, drawing the conclusions that thermal conductivity increased with increasing temperature, and the different pore-size distribution can influence the conductive heat transfer [16,17], and Cho et al (2008) and Yoon (2019) on Kyeongju bentonite, describing the thermal conductivity of compacted bentonite as a function of water fraction at each dry density [18,19]. Additionally, there are research indicating that soil thermal conductivity is strongly affected by soil structure and chemical composition [20,21].…”
Section: Introductionmentioning
confidence: 80%
“…Over the past decades, much research have explored the thermal properties of various types of bentonite. Related experiments can be found in the works of Madsen (1998) and Dixon (2019) on MX80 bentonite [9,10], suggesting that thermal properties can be affected by material source, texture, and dry density, Villar (2000) and Rutqvist (2020) on Febex bentonite, proposing that the thermal conductivity is linearly dependent on saturation in the numerical model [11,12], Ould-Lahoucine et al (2002) and Huang (2020) on Kunigel-V1 bentonite [13,14], which does not behave very differently from MX80 in terms of the moisture distribution and heat transfer characteristics with the same boundary conditions assumed in the simulation model, Chen and Huang (2004) on ZH-clay which is found to be a temperature-sensitive Ca-bentonite produced in Taiwan [15], Ye et al (2010) and Xu et al (2021) on GMZ bentonite, drawing the conclusions that thermal conductivity increased with increasing temperature, and the different pore-size distribution can influence the conductive heat transfer [16,17], and Cho et al (2008) and Yoon (2019) on Kyeongju bentonite, describing the thermal conductivity of compacted bentonite as a function of water fraction at each dry density [18,19]. Additionally, there are research indicating that soil thermal conductivity is strongly affected by soil structure and chemical composition [20,21].…”
Section: Introductionmentioning
confidence: 80%
“…12,66,69 We also note that thermal strain is effective-stress dependent, and in particular, the thermal volumetric strain under high confining stress may occur as contractive rather than expansive because of the mechanical weakening of contacts. 13 Finally, we note that our simulations evaluate the thermal strain in undrained conditions and that additional processes related to osmotic 15,22,54,56,58,59,63,64 Experiments were performed using Kyeongju bentonite, 15 GMZ07 bentonite, 54 Kunigel V1 bentonite, 22 Gyeongju bentonite, 58 and MX-80 bentonite. 56,59,63,65 Figure 4.…”
Section: Resultsmentioning
confidence: 99%
“…Heat capacity of smectite and bentonite at T ≈ 298 K as a function of water mass fraction, with color representing dry density ρ d , including MD simulation predictions from this work (squares) and literature data. ,, Results from Honorio and Brochard are based on MD simulations of perfectly ordered, infinite montmorillonite particles. Experiments were performed using pure water, dry Wyoming Na-montmorillonite, GMZ07 bentonite, BENESA bentonitic clay, Gyeongju bentonite, Wyoming bentonite, Volclay bentonite, , and Sardinian bentonite . Here and in the following figures, the confidence intervals on our MD simulation predictions, if not visible, are smaller than the symbol size.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Extensive research has been subsequently conducted to develop more sophisticated soil models with improved hardening rules based on the concepts of multi-surface and bounding surface plasticity [1,16,25,48,51,56,57,59]. Following the approach of Gens and Nova [20], the influence of structure can also be included within the critical-state based soil models [3,19,26,27,32,43,52,58]. A versatile extension to the non-associative anisotropic model, SANICLAY [17], was proposed by Taiebat et al [53] for modeling sensitive or structured clays with considerations of both isotropic and frictional destructuration.…”
mentioning
confidence: 99%