This report summarizes important nuances in local water concerns and potential climate impacts that could influence the roll-out of technologies associated with energy transitions. Current investments in clean energy technologies are very high, which is driving a lot of investments in related manufacturing (i.e., hydrogen, solar, wind, and batteries) and mining (e.g., lithium, copper, and graphite) around the world. To understand how water and climate dynamics could be influencing these activities, we conducted a phased literature review for three countries: China, Germany, and France. China was selected due to its global dominance in manufacturing of solar panels, batteries, and electrolyzers as well as production of rare earth elements while Germany and France were selected due to their emerging leadership in energy transitions-related manufacturing within the European Union. For each of these three nations, we identified areas where manufacturing is occurring within the country and then evaluated relevant water resources and climate impacts. Multiple sources were consulted for this review, including BloombergNEF, international reports, industry sources, peer-reviewed literature, climate data, and media coverage.
Climate and its impacts on the natural environment, and on the ability of the natural environment to support population and the built environment, stands as a threat multiplier that impacts national and global security. The Water Intersections with Climate Systems Security (WICSS) Strategic Initiative is designed to improve understanding of water’s role in, among other topics, the connection of critical infrastructure to climate in light of competing national and global security interests (including transboundary issues and stability), and identifying research gaps aligned with Sandia, and Federal agency priorities. With this impetus in mind, the WICSS Strategic Initiative team conceptualized a causal loop diagram (CLD) of the relationship between and among climate, the natural environment, population, and the built environment, with an understanding that any such regionally focused system must have externalities that influence the system from beyond its’ control, and metrics for better understanding the consequences of the set of interactions. These are discussed in light of a series of worldviews that focus on portions of the overall systems relationship. The relationships are described and documented in detail. A set of reinforcing and balancing loops are then highlighted within the context of the model. Finally, forward-looking actions are highlighted to describe how this conceptual model can be turned into modeling to address multiple problems described under the purview of the Strategic Initiative.
This report documents the development of the Blue Canyon Dome (BCD) testbed, including test site selection, development, instrumentation, and logistical considerations. The BCD testbed was designed for small-scale explosive tests (~5 kg TNT equivalence maximum) for the purpose of comparing diagnostic signals from different types of explosives, the assumption being that different chemical explosives would generate different signatures on geophysical and other monitoring tools. The BCD testbed is located at the Energetic Materials Research and Testing Center near Socorro, New Mexico. Instrumentation includes an electrical resistivity tomography array, geophones, distributed acoustic sensing, gas samplers, distributed temperature sensing, pressure transducers, and high-speed cameras. This SAND report is a reference for BCD testbed development that can be cited in future publications.
Due to natural heterogeneity in rock specimens, classifying rock characteristics can present difficulties. 3D printing geo-architectured rock specimens has the potential to reduce the heterogeneity and help evaluate characteristics with reproducible microstructures, bedding, and strength to advance mechanical interpretations. This testing focused on 3D printing effects on strength and rock behavior by varying amount of binder, printing direction, and atmospheric conditions. A powder-based Gypsum 3D printer was used to create 1.5-inch diameter cylindrical samples. Unconfined compressive strength (UCS) testing was completed on these samples to gather failure plots and peak strength. Multiple batches of cylindrical samples were printed with varying printing direction, binder amount, and atmospheric conditions. UCS results show that the strongest samples were those that were printed perpendicular to the loading direction compared to those printed parallel or 45 degrees. Due to reactions of the printing material with water, those at dry conditions were the strongest. Samples with the most binder amount proved to also be stronger than those with less. 3D printing of rock samples has to the potential to reduce heterogeneity rock presents, however additional factors introduced by the printing process can affect overall rock strength and behavior. Test results of the 3D printed geo-architected rock specimens demonstrated reasonable reproducibility and appear to be a promising path towards increasing the ability to characterize natural rock.
Quantifying in-situ subsurface stresses and predicting fracture development are critical to reducing risks of induced seismicity and improving modern energy activities in the subsurface. In this work, we developed a novel integration of controlled mechanical failure experiments coupled with microCT imaging, acoustic sensing, modeling of fracture initiation and propagation, and machine learning for event detections and waveform characterization. Through additive manufacturing (3D printing), we were able to produce bassanite-gypsum rock samples with repeatable physical, geochemical and structural properties. With these "geoarchitected" rock, we provided the role of mineral texture orientation on fracture surface roughness. The impact of poroelastic coupling on induced seismicity has been systematically investigated to improve mechanistic understanding of post shut-in surge of induced seismicity. This research will set the groundwork for characterizing seismic waveforms by using multiphysics and machine learning approaches and improve the detection of low-magnitude seismic events leading to the discovery of hidden fault/fracture systems.
This document serves to guide a researcher through the process of predicting atmospheric conditions in a region of interest utilizing the Weather Research and Forecasting (WRF) model. This documentation is specific to WRF and WRF Preprocessing System (WPS) version 3.8.1. WRF is an atmospheric prediction system designed for meteorological research and numerical atmospheric prediction. In WRF, simulations may be generated utilizing real data or idealized atmospheric conditions. Output from WRF serves as input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.