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Cardona, Cristina Garcia

Publications and source records attributed to Cardona, Cristina Garcia.

Reinforcement learning for block decomposition of planar CAD models

Abstract The problem of hexahedral mesh generation of general CAD models has vexed researchers for over 3 decades and analysts often spend more than 50% of the design-analysis cycle time decomposing complex models into simpler blocks meshable by existing techniques. The decomposed blocks are required for generating good quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical simulations of physical systems governed by conservation laws. We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks. Even though the simple examples presented here can also be meshed using many conventional methods, we believe this work is proof-of-principle of a AI-based decomposition method that can eventually be generalized to complex 2D and 3D CAD models. Our method uses reinforcement learning to train an agent to perform a series of optimal cuts on the CAD model that result in a good quality block decomposition. We show that the agent quickly learns an effective strategy for picking the location and direction of the cuts and maximizing its rewards. This paper is the first successful demonstration of an agent autonomously learning how to perform this block decomposition task effectively, thereby holding the promise of a viable method to automate this challenging process for more complex cases.

97 MATHEMATICS AND COMPUTING↗

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE (FY23 Version, Rev. 1)

This report summarizes the development of neutron imaging capabilities and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature-driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. Yttrium hydride is the leading candidate to serve as a solid neutron moderator in microreactor cores, owing to its high density of hydrogen atoms as well as its superior thermal stability compared to all other metal hydrides. The experimental results and technique developments reported herein support the U.S. Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, it addresses the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in high-temperature microreactor designs by means of high-spatial-resolution neutron imaging. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. In principle, neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metal hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on three in-situ heating measurements of YH x performed over the 2020 to 2022 LANSCE operation cycles. The aims of these experiments ranged from furnace commissioning, determining sample quality, i.e., hydrogen uniformity via neutron computed tomography, and studying the effects of applied temperature-gradients on YH x pellets. Analyses and results from these neutron imaging measurements are given along with outlooks and guidelines for optimal future hydrogen diffusion measurements. Our conclusions are as follows. Image analyses indicate that centimeter-sized yttrium hydride cylindrical pellets exhibit uniform, whole-body hydrogen desorption and absorption without clear temperature dependence as reflected in the image attenuation at the opposing ends of each sample. This suggests that despite the large magnitude in temperature gradients applied by the furnace heating elements, the sample equilibrates to an unknown intermediate temperature. The origin of this result is likely the combination of short sample length (∼1cm) and use of a TZM can for containment where the latter created a thermal short across the sample. Nevertheless, the results from the most recent measurements indicate that neither significant concentration gradients of hydrogen were formed in centimeter-sized samples through the entire temperature range (25 °C to 950 °C) nor any formed due to temperature gradients on the order of 50 °C/cm up to 700 °C/cm. Furthermore, images from the FY2021 and FY2022 measurements indicate that samples of YH x , fabricated from either the direct hydride or powder metallurgy methods, are highly uniform in their hydrogen concentration to within the measurements’ spatial resolutions. The following questions arise from these latest results: 1) What is the intermediate temperature of the pellets in the TZM cans? 2) How quickly does the temperature equilibrate within the sample? and, 3) Do the observed changes in image attenuation follow known pressure-composition-temperature relations of yttrium hydride?

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

We report rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

97 MATHEMATICS AND COMPUTING↗