Abstract

The thermal conductivity of bentonite plays a crucial role in analyzing the heat transfer process and determining the temperature field distribution within deep geological repositories. Despite considerable efforts in modeling the thermal conductivity of compacted bentonite and its mixtures, a comprehensive synthesis of these studies has not been previously undertaken. This research aimed to thoroughly review predictive models for the thermal conductivity of compacted bentonite and its mixtures, assessing their performance against a substantial dataset comprising 495 measurements of GMZ and MX80 bentonite. Through a systematic compilation and evaluation of seven models for compacted bentonite and three models for bentonite mixtures, the study identified TC2008 and LC2016 as the most accurate models for GMZ and MX80 compacted bentonite, respectively, whereas PT2021 emerged as the superior predictor for GMZ and MX80 bentonite mixtures. This exploration revealed the absence of a single, universally accurate model capable of predicting the thermal conductivities across all bentonite variants, highlighting the necessity for researchers to judiciously select the most fitting model for predicting the thermal conductivity of bentonite. Furthermore, we expressed the inherent limitations in current thermal conductivity models for compacted bentonite and its mixtures, and proposed directions for future inquiry in this domain.

Details

Title
A review and evaluation of thermal conductivity model of compacted bentonite and its mixture
Author
Ye, W M 1 ; Shao, C Y 2 ; Chen, L 3 

 Department of Geotechnical Engineering, Tongji University , Shanghai , China; Key Laboratory of Geotechnical & Underground Engineering of Ministry of Education and Department of Geotechnical Engineering, Tongji University , Shanghai , China 
 Department of Geotechnical Engineering, Tongji University , Shanghai , China 
 Beijing Research Institute of Uranium Geology , Beijing , China 
First page
012060
Publication year
2024
Publication date
May 2024
Publisher
IOP Publishing
ISSN
17551307
e-ISSN
17551315
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3058785443
Copyright
Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.