Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under
  High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle
  Method
Jonghyuk Baek Yanran Wang Xiaolong He Yu Lu John S. McCartney J. S. Chen
In deep geological repositories for high level nuclear waste with close
canister spacings, bentonite buffers can experience temperatures higher than
100 {\deg}C. In this range of extreme temperatures, phenomenological
constitutive laws face limitations in capturing the thermo-hydro-mechanical
(THM) behavior of the bentonite, since the pre-defined functional constitutive
laws often lack generality and flexibility to capture a wide range of complex
coupling phenomena as well as the effects of stress state and path dependency.
In this work, a deep neural network (DNN)-based soil-water retention curve
(SWRC) of bentonite is introduced and integrated into a Reproducing Kernel
Particle Method (RKPM) for conducting THM simulations of the bentonite buffer.
The DNN-SWRC model incorporates temperature as an additional input variable,
allowing it to learn the relationship between suction and degree of saturation
under the general non-isothermal condition, which is difficult to represent
using a phenomenological SWRC. For effective modeling of the tank-scale test,
new axisymmetric Reproducing Kernel basis functions enriched with singular
Dirichlet enforcement representing heater placement and an effective convective
heat transfer coefficient representing thin-layer composite tank construction
are developed. The proposed method is demonstrated through the modeling of a
tank-scale experiment involving a cylindrical layer of MX-80 bentonite exposed
to central heating.