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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The objective is to Improve the understanding of geothermal resource at Tohatchi Hot Springs, New Mexico using exploratory data analysis and machine learning (ML) methods.
New Mexico Engineering and Surveying Practice Act - “...it is a matter of public safety, interest and concern that the practices of engineering and surveying merit and receive the confidence of the public ..."
This report examines the commercial viability of the Afton solar energy zone (SEZ) in southern New Mexico based on market conditions in 2022. The Bureau of Land Management may designate a SEZ on federal land to expedite environmental review and permitting for solar energy projects. The purpose of this analysis is to understand whether current market conditions support solar development in the SEZ.
The Otowi Member of the Bandelier Tuff erupted at ca. 1.60 Ma from the Valles caldera (New Mexico, USA). It consists of as much as 400 km 3 (dense rock equivalent) of strongly differentiated high-silica rhyolite and shows systematic upward variations in crystallinity, mineral chemistry, and trace element concentrations through its thickness, but the major element composition is almost constant and is near the low-pressure granite minimum. Incompatible trace elements in whole pumice fragments and glasses show well-correlated linear covariations. Upward zoning to lower abundances of incompatible trace elements is accompanied by development of overgrowths on quartz and alkali feldspar, although earlier-formed interiors of quartz and feldspar have near-constant compositions throughout the tuff, modified by cation diffusion in the case of feldspar. Melt inclusions in remnant quartz cores show diverse Pb isotope ratios, pointing to a wide range of distinct protoliths that contributed rhyolitic melt to the Otowi magma. Mineral thermometers suggest a modest temperature gradient through the melt body, perhaps of 40 °C, at the time of eruption. Chemical, textural, and mineralogical variations and volume-composition relations through the tuff are consistent with an origin for zoning by melting of a high-crystallinity cumulate layer beneath cognate supernatant liquid to produce denser, remobilized liquid of accumulative composition (i.e., the “modified mush model”). Melting may have occurred in several episodes. The latest of these episodes, probably thousands of years prior to eruption, introduced new rhyolitic liquid into the system and was associated with a thermal excursion, recorded in core compositions of pyroxene, during which much of the earlier crystal mass was dissolved. This left inherited cores and interiors of accumulated quartz and feldspar mantled with new growth having less-evolved compositions (higher Ti, Sr, and Ba). Changing solubility of zircon during cumulate melting produced a reversal of Zr concentrations. There is no clear petrologic evidence of a recharge eruption trigger; nonetheless, compositional zoning resulted mainly from repeated recharge-induced remobilization of quartz-feldspar cumulate. The Otowi system was built, evolved, and modified by several events over the course of a few hundred thousand years.
This study investigates the potential for New Mexico basalts to serve as geologic CO₂ sequestration media through mineral carbonation. Batch-type experiments were conducted using crushed basalts from the Carrizozo and Taos regions at low PCO₂ (0.7–2 bar) and temperatures up to 40°C over 180 days. The experiments evaluated the kinetics of CO₂-water-rock interaction, cation release, and critical element mobility (Cu, Ni, Co, Zn). Solid and fluid samples were analyzed using SEM, EMPA, ICP-OES, and ICP-MS, and geochemical modeling was performed with GEM-Selektor and PHREEQC. Results indicated that up to 80% of the injected CO₂ was mineralized within 100 days, forming secondary carbonates such as siderite and ankerite. Additionally, significant mobilization of critical metals was observed, which has implications for resource recovery and mine-waste valorization in future CO₂ sequestration projects.
During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.
We present an analysis and interpretation of potential cliff stability at a low-level waste disposal facility at Los Alamos National Laboratory, New Mexico, using cliff morphologic and fracture characteristics coupled with carbon-14 surface exposure dating. Our study is important as it directly bears on the licensing criteria for low-level radioactive waste sites. We find that future characteristic cliff failures will likely not breach disposal pits and shafts over the 1000-year minimum regulatory period. Further, we find, using a multivariate regression model, that slope angle and cliff face aspect are sub-equal in importance to predict regions of high risk of failure when combined with surface exposure ages and assuming that old exposure ages are most indicative of stability (instability) and therefore can aid decision making in final design implementation.
Aerosol sampling with next-generation sequencing was used to characterize microbial communities in a cafeteria and medical facility waiting room in Los Alamos, New Mexico, USA. We detected sequences from human, bacteria, archaea, fungi, other eukaryotes, and viruses, providing insights into the diversity of the aerosol microbiome.
Experiments evaluating the uniformity and intensity of rainfall produced by the NGEE Arctic Rainfall Simulator (NARS) were conducted at Los Alamos National Laboratory, New Mexico, over summer 2022. Petri dishes were placed in a grid within the NARS plot. Simulated rainfall was collected in each petri dish and the intensity and uniformity of the simulator was subsequently calculated. This data package contains two .csv files, one that summarizes the rainfall intensity and uniformity for each experiment, the other that contains individual petri dish water volume and intensity for each plot location and experiment. The Python scripts to control NARS are also included. The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Rain intensity control and data collection are automated using a Raspberry Pi microcomputer. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Geothermal heat pumps can be great alternatives to air conditioners and furnaces, and require less electricity to run. This case study focuses on Central New Mexico Community College and is part of a series: https://www.energy.gov/eere/geothermal/geothermal-heat-pump-case-studies.
Here, we utilized metagenomic and size-filtered virome sequencing to recover 4,157 medium, high, or complete quality viral genomes from soils taken from three high elevation sites in New Mexico, USA. Among recovered viral genomes, 90% were from size-filtered samples, indicating the importance of this enrichment in assessments of complex viromes.
This report presents the sampling and analytical results for chemical constituents for soils potentially impacted by the reconstruction of the intersection at New Mexico (NM) State Road 4 (NM 4) and East Jemez Road (NM State Road 502 [NM 502]).
This report presents the results of a Section 110 cultural resource survey of Sandia National Laboratories’ (Sandia) 9930 and 9939 Complexes, in the Coyote Test Field (CTF), located on lands permitted to the Department of Energy (DOE) by the Department of Defense (DOD), Kirtland Air Force Base (KAFB), Bernalillo County, Albuquerque, New Mexico. The Department of Energy, National Nuclear Security Administration Sandia Field Office (DOE/NNSA SFO) and National Technology and Engineering Solutions of Sandia, LLC, in accordance with Section 110 and Section 106 of the National Historic Preservation Act (NHPA) and its implementing regulations 36 C.F.R. Part 800, states that every federal agency must establish their own historic preservation program for the identification, evaluation, and protection of historic properties and to ensure that historic preservation is fully integrated into the ongoing programs of federal agencies. Sandia’s Cultural Resources Program (CRP) establishes areas of potential effects based upon projected Sandia mission needs. Some of these areas have no immediate undertakings planned but are in areas where undertakings can happen at a future date.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, New Mexico. Activities at the site support research and development programs with a wide variety of national security missions, resulting in technologies for nonproliferation, homeland security, energy and infrastructure, and defense systems and assessments.
Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.
Sandia National Laboratories, hereinafter referred to as Sandia, is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. This Annual Site Environmental Report was prepared in accordance with and as required by DOE O 231.1B, Admin Change 1, Environment, Safety and Health Reporting , and is approved for public release. The U.S. Department of Energy (DOE) and its management and operating contractor for Sandia are committed to safeguarding the environment, continually assessing sustainability practices, and ensuring the validity and accuracy of the monitoring data presented here. This report summarizes the environmental protection, restoration, and monitoring programs in place for Sandia National Laboratories, New Mexico (SNL/NM), during calendar year 2020.
Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.