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DOE OSTI2023

Non-Resource Conservation and Recovery Act Corrective Action Unit (CAU) Post-Closure Inspection Report, Nevada National Security Site, Nevada For Calendar Year 2022

This report includes visual inspection results, descriptions of maintenance and repair activities, and recommendations for calendar year (CY) 2022 for use restrictions (URs) at corrective action sites (CASs) located on the Nevada National Security Site (NNSS) and on the Nevada Test and Training Range (NTTR) that are accessed through the NNSS Main Gate. This document is formatted by project activity (i.e., Industrial Sites, Soils, and Defense Threat Reduction Agency [DTRA]); then by corrective action unit (CAU); and then by CAS.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is in review (May 2023). The Next-Generation Experiments: Arctic (NGEE Arctic), 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).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Shared virtual environments for aerospace training

Virtual environments have the potential to significantly enhance the training of NASA astronauts and ground-based personnel for a variety of activities. A critical requirement is the need to share virtual environments, in real or near real time, between remote sites. It has been hypothesized that the training of international astronaut crews could be done more cheaply and effectively by utilizing such shared virtual environments in the early stages of mission preparation. The Software Technology Branch at NASA's Johnson Space Center has developed the capability for multiple users to simultaneously share the same virtual environment. Each user generates the graphics needed to create the virtual environment. All changes of object position and state are communicated to all users so that each virtual environment maintains its 'currency.' Examples of these shared environments will be discussed and plans for the utilization of the Department of Defense's Distributed Interactive Simulation (DIS) protocols for shared virtual environments will be presented. Finally, the impact of this technology on training and education in general will be explored.

Loftin, R. Bowen↗

NASA NTRS2004

JDD, Inc. Database

JDD Inc, is a maintenance and custodial contracting company whose mission is to provide their clients in the private and government sectors "quality construction, construction management and cleaning services in the most efficient and cost effective manners, (JDD, Inc. Mission Statement)." This company provides facilities support for Fort Riley in Fo,rt Riley, Kansas and the NASA John H. Glenn Research Center at Lewis Field here in Cleveland, Ohio. JDD, Inc. is owned and operated by James Vaughn, who started as painter at NASA Glenn and has been working here for the past seventeen years. This summer I worked under Devan Anderson, who is the safety manager for JDD Inc. in the Logistics and Technical Information Division at Glenn Research Center The LTID provides all transportation, secretarial, security needs and contract management of these various services for the center. As a safety manager, my mentor provides Occupational Health and Safety Occupation (OSHA) compliance to all JDD, Inc. employees and handles all other issues (Environmental Protection Agency issues, workers compensation, safety and health training) involving to job safety. My summer assignment was not as considered "groundbreaking research" like many other summer interns have done in the past, but it is just as important and beneficial to JDD, Inc. I initially created a database using a Microsoft Excel program to classify and categorize data pertaining to numerous safety training certification courses instructed by our safety manager during the course of the fiscal year. This early portion of the database consisted of only data (training field index, employees who were present at these training courses and who was absent) from the training certification courses. Once I completed this phase of the database, I decided to expand the database and add as many dimensions to it as possible. Throughout the last seven weeks, I have been compiling more data from day to day operations and been adding the information to the database. It now consists of seven different categories of data (carpet cleaning, forms, NASA Event Schedules, training certifications, wall and vent cleaning, work schedules, and miscellaneous) . I also did some field inspecting with the supervisors around the site and was present at all of the training certification courses that have been scheduled since June 2004. My future outlook for the JDD, Inc. database is to have all of company s information from future contract proposals, weekly inventory, to employee timesheets all in this same database.

Miller, David A., Jr.↗

DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗

DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Development and Implementation of a Nuclear and Criticality Safety Engineering Pipeline Course at North Carolina State University

Savannah River Nuclear Solutions, owner of both the criticality safety program and accident analysis qualification at Savannah River Site, experienced increasing difficulty in recruiting, training, and retaining key talent areas. In an effort to curb attrition, provide a talent base to recruit from, and introduce potential new hires to niche subject areas, a pipeline college course was developed for a regional university. The course introduces a variety of topic areas particular to criticality safety and nuclear safety at Department of Energy nonreactor nuclear facilities. Several administrative and developmental hurdles were encountered before the course was successfully initiated at North Carolina State University in fall 2024.

criticality↗

DOE OSTI2022

Generation of Data-Driven Expected Energy Models for Photovoltaic Systems

Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant over-estimations, which impacts accurate diagnoses of field operations and maintenance issues. This research addresses this issue by using machine learning to develop a data-driven expected energy model that can more accurately generate inferences for energy production of PV systems. Irradiance and system capacity information was used from 172 sites across the United States to train a series of models using Lasso linear regression. The trained models generally perform better than the commonly used expected energy model from international standard (IEC 61724-1), with the two highest performing models ranging in model complexity from a third-order polynomial with 10 parameters (Radj2 = 0.994) to a simpler, second-order polynomial with 4 parameters (Radj2=0.993), the latter of which is subject to further evaluation. Subsequently, the trained models provide a more robust basis for identifying potential energy anomalies for operations and maintenance activities as well as informing planning-related financial assessments. We conclude with directions for future research, such as using splines to improve model continuity and better capture systems with low (≤1000 kW DC) capacity.

14 SOLAR ENERGY↗

NASA NTRS1972

Feasibility study for locating archaeological village sites by satellite remote sensing techniques

The author has identified the following significant results. As a result of ground truth activities, the accurate geographic location of an old fish camp was established as 64 deg 12 min 38 sec N, 158 deg 30 min 42 sec W. Previously the location of this very large abandoned village site was given only on a map sketched in 1935. This location and two other nearby sites will be used as ERTS training areas.

Cook, J. P.↗

NASA NTRS1975

Spectral signature selection for mapping unvegetated soils

Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

May, G. A.↗

NASA NTRS1990

Biomedical program at Space Biospheres Ventures

There are many similarities and some important differences between potential health problems of Biosphere 2 and those of which might be anticipated for a space station or a major outpost on Mars. The demands of time, expense, and equipment would not readily allow medical evacuation from deep space for a serious illness or major trauma, whereas personnel can easily be evacuated from Biosphere 2 if necessary. Treatment facilities can be somewhat less inclusive, since distance would not compel the undertaking of heroic measures or highly complicated surgical procedures on site, and with personnel not fully trained for these procedures. The similarities are given between medical requirements of Biosphere 2 and the complex closed ecological systems of biospheres in space or on Mars. The major problems common to all these would seem to be trauma, infection, and toxicity. It is planned that minor and moderate degrees of trauma, including debridement and suturing of wounds, x ray study of fractures, will be done within Biosphere 2. Bacteriologic and fungal infections, and possibly allergies to pollen or spores are expected to be the commonest medical problem within Biosphere 2.

Walford, Roy↗

NASA NTRS1991

Spatial transportability of geobotanical spectral signatures associated with soil geochemical anomalies

Consideration is given to a specific problem of spectral signature extension through examination and comparison of multitemporal spectral data signatures developed over a generalized, along-strike geochemical trend located within eastern deciduous hardwood forest. The spectral geobotanical signatures associated with soil Pb, Cu, and Zn anomalies were examined in south-central Virginia. Spectral discrimination between a training pair of anomalous and control sites within the Mineral district was 100 percent successful. It is concluded that spectral geobotanical signatures serve to identify regions of lowered biomass, lowered vigor, and/or differing species composition.

Bell, R.↗

NASA NTRS2007

Spaceflight Decompression Sickness Contingency Plan

A viewgraph presentation on the Decompression Sickness (DCS) Contingency Plan for manned spaceflight is shown. The topics include: 1) Approach; 2) DCS Contingency Plan Overview; 3) Extravehicular Activity (EVA) Cuff Classifications; 4) On-orbit Treatment Philosophy; 5) Long Form Malfunction Procedure (MAL); 6) Medical Checklist; 7) Flight Rules; 8) Crew Training; 9) Flight Surgeon / Biomedical Engineer (BME) Training; and 10) DCS Emergency Landing Site.

Dervay, Joseph P.↗

NASA NTRS2010

Telescience Support Center Data System Software

The Telescience Support Center (TSC) team has developed a databasedriven, increment-specific Data Require - ment Document (DRD) generation tool that automates much of the work required for generating and formatting the DRD. It creates a database to load the required changes to configure the TSC data system, thus eliminating a substantial amount of labor in database entry and formatting. The TSC database contains the TSC systems configuration, along with the experimental data, in which human physiological data must be de-commutated in real time. The data for each experiment also must be cataloged and archived for future retrieval. TSC software provides tools and resources for ground operation and data distribution to remote users consisting of PIs (principal investigators), bio-medical engineers, scientists, engineers, payload specialists, and computer scientists. Operations support is provided for computer systems access, detailed networking, and mathematical and computational problems of the International Space Station telemetry data. User training is provided for on-site staff and biomedical researchers and other remote personnel in the usage of the space-bound services via the Internet, which enables significant resource savings for the physical facility along with the time savings versus traveling to NASA sites. The software used in support of the TSC could easily be adapted to other Control Center applications. This would include not only other NASA payload monitoring facilities, but also other types of control activities, such as monitoring and control of the electric grid, chemical, or nuclear plant processes, air traffic control, and the like.

Rahman, Hasan↗

NASA NTRS2004

1-AU calibration activities for Stardust Earth Return

In January 2006, the Stardust spacecraft will deliver its Sample Return Capsule to the recovery site at the Utah Test and Training Range. Stardust will return dust samples from comet Wild 2 colleced in January 2004. During Earth return, the trajectory will be perturbed by small firings of the spacecraft reaction control thrusters. Calibration of these fireings is essential to ensure meeting Earth entry requirements. This paper will describe such calibrations performed between superior conjunctions when Stardust was about 1 AU from the Sun.

Williams, Ken↗

NASA NTRSDate not supplied

Lunar Surface Cargo Offloading Concepts

A sustainable presence on the lunar surface will serve as a vital training ground and technology demonstration test site in preparation for future human missions to Mars. Robotic lunar surface campaigns will focus on the exploration of resources providing information on the availability and extraction of usable resources, such as oxygen and water, and prepare the surface for a sustained human presence. Landers, outfitted with sensor packages, will be used to conduct risk-reduction activities and aid in the development of technologies prior to the crewed lunar missions that drive the need to for a logistics supply chain that requires offloading. A series of landers will be required on other planetary surfaces to build up the capabilities, capitalizing on those resources, required for sustained human presence. In each of those landers will be cargo including ascent vehicles, habitats, supplies, science packages, spare parts, fluids commodities for fuel and life support, and others varying in volume and ranging from mass in hundreds of kilograms to an estimated 6-14 metric tons to support Human Landing Systems and surface logistics requirements. This paper will examine the challenge of offloading examples of these cargo elements from different categories of landers on the lunar surface using a variety of methodologies. Challenges on the lunar surface arise with the conditions present (thermal, lighting, communications, regolith consistency), the desire to minimize mass of all landed systems, the desire to perform much of the activities with limited to minimal human interaction, and the overall configuration of the landers that are responsible for landing the cargo.

Lunar↗

NASA NTRSDate not supplied

Lunar Surface Cargo Offloading Concepts

Lunar↗

NASA NTRS2022

Power System Design Trades for a Pressurized Lunar/Mars Rover

To enable future human exploration missions, the lunar surface will serve as a crucial training ground and technology demonstration test site where NASA will prepare for future human missions to Mars and other destinations. Key enablers in this exploration are rover systems intended to operate in both the lunar and Mars surface environments. This study focused primarily on the Mars surface environment because, compared to the near-continuous illumination near the lunar poles, the day/night cycles and the reduced solar illumination on Mars make it much more difficult to use solar power. However, solar power is not the only power generation technology available. Top-level energy studies have shown that a radioisotope power system (RPS) has promise for supplying both power and thermal energy for crewed rovers on both the Moon and Mars. This design study investigates how an RPS could potentially meet the power and thermal needs of a pressurized rover with applications for both destinations. The design focus is on what service an RPS can provide and how it would be integrated into a rover (power and thermal interfaces, placement, radiation shielding, fairing installation on the pad, etc.).

RPS↗