(A) multi-objective optimal design of high-level waste repository considering thermo-hydro-mechanical properties

Abstract

A deep geological disposal method securely disposes of the high-level nuclear waste by placing the waste in the repository system which consists of canister containing the waste, buffer, backfill, and near-field rock. The buffer is the key barrier component of the system, and its ultimate objective is to prevent the potential radionuclide from leaking out of the damaged canister. A bentonite has been extensively proposed as a buffer candidate material in the repository. The purpose of this study is to analyze and optimize the performance of the repository by applying the properties of Korean bentonite to a numerical analysis model. The intrusion of groundwater and heat from the waste change the temperature and chemical composition of the bentonite, consequently can alter the properties of the bentonite. Laboratory tests were conducted on Korean Kyeongju bentonite blocks that were compacted at different dry densities. All the tests were conducted at three different temperatures by supplying de-ionized water, NaCl solution, and CaCl2 solution. Based on the results, thermochemical effects on the Korean domestic bentonite were investigated. Among the properties of Kyeongju bentonite to be applied to the repository system, Barcelona Basic Model (BBM) parameters are difficult to obtain by direct experiment. By using the simple swelling test results and the numerical model, which consider the thermal-hydraulic-mechanical interaction, a method of determining Barcelona Basic Model (BBM) parameters of Kyeongju bentonite was proposed. The parameters were derived by comparing test data and numerical analysis results using ANN, one of the machine learning techniques. A coupled thermal-hydraulic-mechanical numerical model was constructed to which all Kyeongju bentonite properties were applied and the optimal design conditions of a Korean repository system were investigated, concerning the effect of initial conditions of buffer, backfill and near-field rock. Five influential factors, i.e., porosity and saturation of each of buffer bentonite and backfill material, and permeability of the surrounding rock were set to determine relationships with buffer temperature, saturation time, and swelling time. After constructing metamodels based on a small number of simulation results, a multi-objective optimization was performed, using the genetic algorithm. As a result of the optimization process, a Pareto optimal set has been derived, and the process and the results provide a guideline for designing a repository system.