Gas migration at the granite-bentonite interface under semi-rigid boundary conditions in the context of HLRW disposal

The corrosion of waste canisters in the deep geological disposal facilities (GDFs) for high-level radioactive waste (HLRW) can generate gas, which can escape from the engineered barrier system. Such escape can happen through the interfaces between the bentonite buffer blocks and the host rock, and also at the interfaces between the bentonite blocks. This paper presents new insights and a quantitative assessment of the impact of the interface between clay and host rock on gas transport through a series of water infiltration and gas breakthrough experiments on granite and on granite-bentonite specimens with smooth and grooved interfaces.The water permeability values from water infiltration tests on granite and granite-bentonite samples (10-19 ~ 10-20 m2) were found to be slightly higher than that of bentonite. The gas permeability of the mock-up samples with smooth interface was one order of magnitude larger than that of the mock-up with grooved interfaces. The gas results of breakthrough pressures for the granite and the granite-bentonite mock-up samples indicated significantly lower pressures than that of bentonite. The results highlight the potential existence of preferential gas migration channels between the rock and bentonite buffer which require further considerations in the safety case assessment.<br/

Quasi-Brittle Fracture Modeling of Preflawed Bitumen Using a Diffuse Interface Model

Fundamental understandings on the bitumen fracture mechanism are vital to improve the mixture design of asphalt concrete. In this paper, a diffuse interface model, namely, phase-field method is used for modeling the quasi-brittle fracture in bitumen. This method describes the microstructure using a phase-field variable which assumes one in the intact solid and negative one in the crack region. Only the elastic energy will directly contribute to cracking. To account for the growth of cracks, a nonconserved Allen-Cahn equation is adopted to evolve the phase-field variable. Numerical simulations of fracture are performed in bituminous materials with the consideration of quasi-brittle properties. It is found that the simulation results agree well with classic fracture mechanics

Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.Comment: 10 pages, 8 figure

Resonant Quantum Principal Component Analysis

Principal component analysis has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the principal components of it, i.e. the eigenvectors of the density matrix with largest eigenvalues. However, due to the substantial resource requirement, its experimental implementation remains challenging. Here, we develop a resonant analysis algorithm with the minimal resource for ancillary qubits, in which only one frequency scanning probe qubit is required to extract the principal components. In the experiment, we demonstrate the distillation of the first principal component of a 4$\times$4 density matrix, with the efficiency of 86.0% and fidelity of 0.90. This work shows the speed-up ability of quantum algorithm in dimension reduction of data and thus could be used as part of quantum artificial intelligence algorithms in the future.Comment: 10 pages, 7 figures, have been waiting for the reviewers' responses for over 3 month