Study on the Effect of Amygdales on Mechanical Characteristics of Basalt Using the Combined Finite-Discrete Element Method

Amygdaloidal basalt, as a heterogeneous rock, is widely exposed at Baihetan hydropower station, China. The geometric effect of amygdales needs further studies and quantifying the shape, orientation, and statistical distribution of amygdales plays an important role in the laboratory and numerical experiments. Therefore, digital image processing (DIP) was first utilized to build a heterogeneous model (HM) to calibrate against the laboratory test results. Then, the heterogeneous models (HMs) with prescribed geometric features were generated by the inverse Monte-Carlo (IMC) algorithm. The uniaxial compression experiments based on HMs were conducted to study the mechanism of the crack initiation and propagation in the amygdaloidal basalt. The tensile fractures were mainly occurred in the matrix, and the shear fractures were mainly occurred in the amygdales. With the increase in the elliptic coefficient of amygdales, the uniaxial compressive strength (UCS) showed a linear growth trend. With the increase in the orientation of amygdales, the UCS exhibited a “V-shaped” distribution characteristic. This paper provides a numerical method for studying the mechanical properties of rocks with flaws.

Utilizing nuclear data in delayed gamma-ray spectroscopy inverse Monte Carlo analysis

Safeguards verification of uranium and plutonium in high-radioactivity nuclear material is currently performed using destructive analysis techniques. However, the preparation method is a burden on both the safeguards inspectors and facility operators. While nondestructive assay (NDA) techniques would improve the efficiency and time, there are no passive NDA techniques available to directly verify the U and Pu content. As an alternative, the JAEA and JRC are collaboratively developing the Delayed Gamma-ray Spectroscopy (DGS) active-interrogation NDA technique to evaluate the fissile composition from the unique fission product yield distributions. To analyze the data we are developing an Inverse Monte Carlo (IMC) method that simulates the interrogation and evaluates the individual contributions from the mixed nuclear material to the composite spectrum. While the current nuclear data affects the ability to evaluate the composition, the IMC analysis method can be used to determine the systematic uncertainty contributions and has the potential to improve the nuclear data. We will present the current status of the DGS collaborative work as it relates to the development of the DGS IMC analysis.

Delivery of carbon, nitrogen, and sulfur to the silicate Earth by a giant impact

Earth’s status as the only life-sustaining planet is a result of the timing and delivery mechanism of carbon (C), nitrogen (N), sulfur (S), and hydrogen (H). On the basis of their isotopic signatures, terrestrial volatiles are thought to have derived from carbonaceous chondrites, while the isotopic compositions of nonvolatile major and trace elements suggest that enstatite chondrite–like materials are the primary building blocks of Earth. However, the C/N ratio of the bulk silicate Earth (BSE) is superchondritic, which rules out volatile delivery by a chondritic late veneer. In addition, if delivered during the main phase of Earth’s accretion, then, owing to the greater siderophile (metal loving) nature of C relative to N, core formation should have left behind a subchondritic C/N ratio in the BSE. Here, we present high pressure-temperature experiments to constrain the fate of mixed C-N-S volatiles during core-mantle segregation in the planetary embryo magma oceans and show that C becomes much less siderophile in N-bearing and S-rich alloys, while the siderophile character of N remains largely unaffected in the presence of S. Using the new data and inverse Monte Carlo simulations, we show that the impact of a Mars-sized planet, having minimal contributions from carbonaceous chondrite-like material and coinciding with the Moon-forming event, can be the source of major volatiles in the BSE.

MCMLpar and MCSLinv: A Parallel Version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters

The MCML program for Monte Carlo modeling of light transport in multi-layered tissues has been widely used in the past 20 years or so. Here, we have re-implemented MCML for solving the inverse problem. Our formulation features optimizing the profile log likelihood which takes into account uncertainties due to both experimental and Monte Carlo sampling. We limit the search space for the optimum parameters with relatively few Monte Carlo trials and then iteratively double the number of Monte Carlo trials until the search space stabilizes. At this point, the log likelihood can be fit with a quadratic function to find the optimum. The time-to-solution is only a few minutes in typical cases because we use importance sampling to determine the log likelihood on a grid of parameters at each iteration. Also, our implementation uses OpenMP and SPRNG to generate Monte Carlo trials in parallel.