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DOE OSTI2024

Electrically Powered High-Salinity Brine Separation Using Dimethyl Ether

Dewatering highly saline aqueous streams, from mining and geothermal leachates to industrial wastewater, is essential for effective resource recovery and safe disposal. Membraneless water extraction (MWE) uses a low-polarity solvent to separate water from concentrated aqueous solutions. In this study, we design a new MWE that uses dimethyl ether (DME) to selectively extract water from high-salinity brines, leveraging the volatility of DME to achieve rapid solvent recovery. By separating water and dissolved salts at a liquid–liquid interface, MWE minimizes the deleterious effects of scaling on vulnerable membrane and heat exchanger surfaces, reducing the need for extensive pretreatment and expensive materials. We begin by developing a computational framework for a multistage counterflow liquid–liquid contactor, which extracts water into DME, coupled with a multistage solvent regenerator that uses vapor compression to efficiently separate the desalinated water from the DME extractant. Excess Gibbs free energy and equation of state frameworks are used to model fluid phase equilibria in water–DME–sodium chloride (NaCl) mixtures, with interaction parameters estimated from experimental data. Incorporating equilibrium calculations into a system-scale computational model, we examine the performance of MWE using DME for the first time. Our analysis demonstrates that MWE can concentrate seawater desalination brine (>1.0 mol NaCl kg –1 ) to zero-liquid discharge salinities, with an energy consumption of under 50 kW h per m 3 of water extracted with a solvent recovery ratio greater than 99.9%. We highlight the importance of staging the vapor compression process to simultaneously minimize energy consumption while enabling brine concentration and product water solvent contamination. Finally, the thermodynamic framework developed here allows for the robust evaluation of new MWE solvents and systems for critical brine concentration and fractional precipitation applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1995

Mineralogic variations in fluvial sediments contaminated by mine tailings as determined from AVIRIS data, Coeur D'Alene River Valley, Idaho

The success of imaging spectrometry in mineralogic mapping of natural terrains indicates that the technology can also be used to assess the environmental impact of human activities in certain instances. Specifically, this paper describes an investigation into the use of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for mapping the spread of, and assessing changes in, the mineralogic character of tailings from a major silver and base metal mining district. The area under investigation is the Coeur d'Alene River Valley in northern Idaho. Mining has been going on in and around the towns of Kellogg and Wallace, Idaho since the 1880's. In the Kellogg-Smelterville Flats area, west of Kellogg, mine tailings were piled alongside the South Fork of the Coeur d'Alene River. Until the construction of tailings ponds in 1968 much of these waste materials were washed directly into the South Fork. The Kellogg-Smelterville area was declared an Environmental Protection Agency (EPA) Superfund site in 1983 and remediation efforts are currently underway. Recent studies have demonstrated that sediments in the Coeur d'Alene River and in the northern part of Lake Coeur d'Alene, into which the river flows, are highly enriched in Ag, Cu, Pb, Zn, Cd, Hg, As, and Sb. These trace metals have become aggregated in iron oxide and oxyhydroxide minerals and/or mineraloids. Reflectance spectra of iron-rich tailing materials are shown. Also shown are spectra of hematite and goethite. The broad bandwidth and long band center (near 1 micron) of the Fe(3+) crystal-field band of the iron-rich sediment samples combined with the lack of features on the Fe(3+) -O(2-) charge transfer absorption edge indicates that the ferric oxide and/or oxyhydroxide in these sediments is poorly crystalline to amorphous in character. Similar features are seen in poorly crystalline basaltic weathering products (e.g., palagonites). The problem of mapping and analyzing the downriver occurrences of iron rich tailings in the Coeur d'Alene (CDA) River Valley using remotely sensed data is complicated by the full vegetation cover present in the area. Because exposures of rock and soil were sparse, the data processing techniques used in this study were sensitive to detecting materials at subpixel scales. The methods used included spectral mixture analysis and a constrained energy minimization technique.

Farrand, W. H.↗

DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS2011

Spatially Resolved, In Situ Carbon Isotope Analysis of Archean Organic Matter

Spatiotemporal variability in the carbon isotope composition of sedimentary organic matter (OM) preserves information about the evolution of the biosphere and of the exogenic carbon cycle as a whole. Primary compositions, and imprints of the post-depositional processes that obscure them, exist at the scale of individual sedimentary grains (mm to micron). Secondary ion mass spectrometry (SIMS) (1) enables analysis at these scales and in petrographic context, (2) permits morphological and compositional characterization of the analyte and associated minerals prior to isotopic analysis, and (3) reveals patterns of variability homogenized by bulk techniques. Here we present new methods for in situ organic carbon isotope analysis with sub-permil precision and spatial resolution to 1 micron using SIMS, as well as new data acquired from a suite of Archean rocks. Three analytical protocols were developed for the CAMECA ims1280 at WiscSIMS to analyze domains of varying size and carbon concentration. Average reproducibility (at 2SD) using a 6 micron spot size with two Faraday cup detectors was 0.4 %, and 0.8 % for analyses using 1 micron and 3 micron spot sizes with a Faraday cup (for C-12) and an electron multiplier (for C-13). Eight coals, two ambers, a shungite, and a graphite were evaluated for micron-scale isotopic heterogeneity, and LCNN anthracite (delta C-13 = -23.56 +/- 0.1 %, 2SD) was chosen as the working standard. Correlation between instrumental bias and H/C was observed and calibrated for each analytical session using organic materials with H/C between 0.1 and 1.5 (atomic), allowing a correction based upon a C-13H/C-13 measurement included in every analysis. Matrix effects of variable C/SiO2 were evaluated by measuring mm to sub-micron graphite domains in quartzite from Bogala mine, Sri Lanka. Apparent instrumental bias and C-12 count rate are correlated in this case, but this may be related to a crystal orientation effect in graphite. Analyses of amorphous Archean OM suggest that instrumental bias is consistent for 12C count rates as low as 10% relative to anthracite. Samples from the ABDP-9 (n=3; Mount McRae Shale, approximately 2.5 Ga), RHDH2a (n=2; Carrawine Dolomite and Jeerinah Fm, approximately 2.6 Ga), WRL1 (n=3; Wittenoom Fm, Marra Mamba Iron Formation, and Jeerinah Fm, approximately 2.6 Ga), and SV1 (n=1; Tumbiana Fm, approximately 2.7 Ga) drill cores, each previously analyzed for bulk organic carbon isotope composition, yielded 100 new, in situ data from Neoarchean sedimentary OM. In these samples, delta C-13 varies between -53.1 and -28.3 % and offsets between in situ and bulk compositions range from -8.3 to 18.8%. In some cases, isotopic composition and mode of occurrence (e.g. morphology and mineral associations) are statistically correlated, enabling the identification of distinct reservoirs of OM. Our results support previous evidence for gradients of oxidation with depth in Neoarchean environments driven by photosynthesis and methane metabolism. The relevance of these findings to questions of bio- and syngenicity as well as the alteration history of previously reported Archean OM will be discussed.

Williford, Kenneth H.↗

DOE OSTI2025

Quantification and prediction of solidification textures under additive manufacturing conditions

Crystallographic textures are a major determinant of the macroscale anisotropic properties of polycrystalline metallic alloys produced in a wide range of additive manufacturing (AM) processes. Here, we introduce a statistical method that can accurately quantify the degree of orientational order of textures despite the large random fluctuations in the orientation of individual grains inherent in AM processes. The method, demonstrated for laser and resolidification of AlSi thin films, extends Z-scoring to a dynamical regime to assess the statistical significance of observed textures compared to randomly generated ones at different stages of solidification. We further show that, combined with phase-field modeling, this method can be used to infer fundamental anisotropic properties of the solid-liquid interface that are essential for texture prediction, and are compared here to the results of atomistic simulations. In addition, phase-field modeling reveals that, even at rapid AM solidification rates, the observed 〈110〉-dominated textures in the AlSi thin films are controlled predominantly by the anisotropy of the interface free-energy and sheds light on the physical mechanism of grain competition. These results significantly enhance both the existing tools for the quantification and prediction of AM crystallographic textures and our basic understanding of their formation.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing

Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Non-Magnetic, Tough, Corrosion- and Wear-Resistant Knives From Bulk Metallic Glasses and Composites

Quality knives are typically fabricated from high-strength steel alloys. Depending on the application, there are different requirements for mechanical and physical properties that cause problems for steel alloys. For example, diver's knives are generally used in salt water, which causes rust in steel knives. Titanium diver's knives are a popular alternative due to their salt water corrosion resistance, but are too soft to maintain a sharp cutting edge. Steel knives are also magnetic, which is undesirable for military applications where the knives are used as a tactical tool for diffusing magnetic mines. Steel is also significantly denser than titanium (8 g/cu cm vs. 4.5 g/cu cm), which results in heavier knives for the same size. Steel is hard and wear-resistant, compared with titanium, and can keep a sharp edge during service. A major drawback of both steel and titanium knives is that they must be ground or machined into the final knife shape from a billet. Since most knives have a mirrored surface and a complex shape, manufacturing them is complex. It would be more desirable if the knife could be cast into a net or near-net shape in a single step. The solution to the deficiencies of titanium, steel, and ceramic knives is to fabricate them using bulk metallic glasses (or composites). These alloys can be cast into net or near-net shaped knives with a combination of properties that exceed both titanium and steel. A commercially viable BMG (bulk metallic glass) or composite knife is one that exhibits one or all of the following properties: It is based on titanium, has a self-sharpening edge, can retain an edge during service, is hard, is non-magnetic, is corrosion-resistant against a variety of corrosive environments, is tough (to allow for prying), can be cast into a net-shape with a mirror finish and a complex shape, has excellent wear resistance, and is low-density. These properties can be achieved in BMG and composites through alloy chemistry and processing. For each desired property for knife fabrication and performance, there is an alloy development strategy that optimizes behavior. Although BMG knives have been demonstrated as far back as 1995, they never found commercial success because they had to be ground (which presented problems because the alloys contained beryllium), they weren't low cost (because they weren't cast to a net-shape), they were brittle (because they were made with a low-quality commercial material), and they had extremely poor corrosion resistance (because corrosion was not well-understood in these materials). Ultimately, these shortcomings prevented the widespread commercialization. In the current work, the inventors have applied more than a decade of research on BMGs from Caltech and JPL to develop a better understanding of how to make BMG knives that exhibit an optimal combination of properties, processing and cost. Alloys have been developed based in titanium (and other metals), that exhibit high toughness, high hardness, excellent corrosion resistance, no ferromagnetism, edge-retaining selfsharpening, and the ability to be cast like a plastic using commercially available casting techniques (currently used by commercial companies such as Liquidmetal Technologies and Visser Precision Casting). The inventors argue that depending on the application (diving, military, tactical, utility, etc.) there is an optimal combination of design and alloy composition. Moreover, with new casting technologies not available at the inception of these materials, net-shaped knives can be cast into complex shapes that require no aftermarket forming, except for sharpening using water-cooled polishing wheel. These combinations of discoveries seek to make low-cost BMG knives commercially viable products that have no equal among metal or ceramic knives. Current work at JPL focuses on net-shape casting of these alloys and testing their mechanical properties versus commercially available knives to demonstrate their benefits.

Hoffman, Douglas C.↗

DOE OSTI2025

Development of a Printable Prill Formulation Technique and Demonstration of Monomodal Prill Size on Compaction Density and Compressive Strength

Polymer-bonded explosive molding powder, or “prills,” are relied on for the fabrication of pressed high explosives since the 1950's. The wet granulation technique, also known as “slurry coating,” that is used to formulate prills, is a complex process that results in polydisperse and variable yields. This makes it difficult to study the mesoscale effect that prills have on the microstructure of a pressed article. The following study introduces a novel approach to energetic granulation that leverages techniques used in the additive manufacturing of paste-like energetic materials. This extrusion granulation, or prill printing technique, makes it possible to tailor the sizes and shapes of prills, allowing for their morphological influences to be studied in a controlled manner. The following work details the fabrication and characterization of four monomodal size lots of prills using an inert formulation (95 wt.% melamine, 5 wt.% polymer binder). Prills from each size lot were die-pressed using a fixed recipe to investigate how prill size impacts compaction density and therefore compressive strength. It was found that larger prills influence the pressing density by creating larger defects within the microstructure of a pressed article, resulting in a decrease in compressive strength.

direct ink write↗

DOE OSTI2025

Control over Banded Morphologies and Circular Dichroism in Chiral Halide Perovskites

Chiral halide perovskites (c-HPs) merge the chirality of organic cations with the semiconducting properties of metal halide frameworks, creating a family of chiral semiconductors with tunable chiroptoelectronic behavior. Here, we describe the impact of periodic banded morphologies of textured c-HP ( R/S -NEA) 2 PbBr 4 films (NEA = 1- (1-naphthyl)ethylammonium) on their chiroptical behavior. Due to the interplay between the crystalline and glassy phases, the c-HP film growth is driven by rhythmic precipitation, producing a distinctive controllable radial banded pattern with the ( R/S -NEA) 2 PbBr 4 inorganic planes oriented parallel to the substrate. The banded morphology can be controlled, as evidenced by the growth temperature dictating the ridge-to-ridge spacing as well as the density of banded regions. The resulting circular dichroism (CD) spectral shape, intensity, and polarity vary in a seemingly random manner across processing conditions. However, these spectral features can be explained by considering key features of the banded morphology, such as refraction of the incident light due to surface morphology, birefringence, and stacked, rotated crystallites. These effects cannot be canceled by averaging front and back CD spectra of c-HP films, and our model incorporating these effects reproduces all observed CD spectra remarkably well. The control over the c-HP morphology and prediction capabilities of our CD modeling leads to further understanding of this class of semiconductors and the possibility of exploiting structural features for light polarization control akin to enhanced metamaterials.

14 SOLAR ENERGY↗

DOE OSTI2025

Review of High‐Speed Digital Image Correlation: Advancements and Good Practices

This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.

experimental mechanics↗

DOE OSTI2026

Making Small-Volume Heat Pump Water Heaters Larger: A Design Framework for Integrated Phase Change Material Heat Exchangers

Those that use electricity for water heating, the majority use resistive elements rather than heat pump water heaters (HPWHs), the latter of which use 60-70% less energy than the former. One major barrier to wider HPWH adoption is the added equipment required, which prevents current 50-80-gallon tanks on the market from fitting into smaller utility closets sized for 30-40 gallons, such as those found in manufactured housing. Additionally, these smaller HPWHs tend to underperform relative to their larger counterparts. One solution that addresses both space and performance concerns is thermal energy storage, and in particular, phase change materials (PCMs). PCMs have been studied extensively in building envelope and HVAC systems, but remain a nascent technology in residential water heating. While water itself has a uniquely high energy storage capacity, PCMs have an even higher energy storage density, thus providing the potential to elevate the performance of 40-gallon HPWHs to that of 50-gallon or larger tanks. This research is part of a larger project that seeks to utilize thermal energy storage to enable decarbonized water heating in low-income communities. In this study, we outline the design process used to produce novel PCM heat exchangers for use in small-volume HPWH tanks, including the identification of design constraints and performance targets relevant to real-world applications. In order to ensure optimal PCM utilization and tank storage capacity, we focus here on co-maximizing surface area and PCM volume in the heat exchangers; therefore, this research targets triply periodic minimal surface (TPMS) lattices. TPMS lattices boast enhanced heat transfer capabilities compared to traditional heat exchanger geometries and offer highly tailorable designs; thus, they pair well with the growing field of additive manufacturing, or 3D printing. Starting with a suite of TPMS lattices, we demonstrate a systematic approach for narrowing down feasible designs that comply with identified constraints while meeting PCM performance objectives.

25 ENERGY STORAGE↗

NASA NTRS2011

Carbon Nanotube Based Nanotechnology for NASA Mission Needs and Societal Applications

Carbon nanotubes (CNT) exhibit extraordinary mechanical properties and unique electronic properties and therefore, have received much attention for more than a decade now for a variety of applications ranging from nanoelectronics, composites to meeting needs in energy, environmental and other sectors. In this talk, we focus on some near term potential of CNT applications for both NASA and other Agency/societal needs. The most promising and successful application to date is a nano chem sensor at TRL 6 that uses a 16-256 sensor array in the construction of an electronic nose. Pristine, doped, functionalized and metal-loaded SWCNTs are used as conducting materials to provide chemical variation across the individual elements of the sensor array. This miniaturized sensor has been incorporated in an iPhone for homeland security applications. Gases and vapors relevant to leak detection in crew vehicles, biomedical, mining, chemical threats, industrial spills and others have been demonstrated. SWCNTs also respond to radiation exposure via a change in conductivity and therefore, a similar strategy is being pursued to construct a radiation nose to identify radiation sources (gamma, protons, neutrons, X-ray, etc.) with their energy levels. Carbon nanofibers (CNFs) grown using plasma enhanced CVD typically are vertical, individual, freestanding structures and therefore, are ideal for construction of nanoelectrodes. A nanoelectrode array (NEA) can be the basis for an affinity-based biosensor to meet the needs in applications such as lab-on-a-chip, environmental monitoring, cancer diagnostics, biothreat monitoring, water and food safety and others. A couple of demonstrations including detection of e-coli and ricin will be discussed. The NEA is also useful for implantation in the brain for deep brain stimulation and neuroengineering applications. Miniaturization of payload such as science instrumentation and power sources is critical to reduce launch costs. High current density (greater than 100 mA/per square centimeters) field emission capabilities of CNTs can be exploited for construction of electron gun for electron microscopy and X-ray tubes for spectrometers and baggage screening. A CNT pillar array configuration has been demonstrated, not only meeting the high current density needs but more importantly providing long term emitter stability. Finally, supercapacitors hold the promise to combine the high energy density of a battery with the high power density of capacitors. Traditional graphite electrodes have not delivered this promise yet. A novel design and processing approach using MWCNTs has shown a record 550 F/g capacitance along with significant device endurance. This supercapacitor is suitable for railgun launch application for NASA, powering rovers and robots, consumer electronics and future hybrid vehicles.

Li, Jing↗

DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗

DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

DOE OSTI2025

In vitro toxicity assessment of uranium particulates on different human lung epithelial cell models

Inhalation of uranium aerosols produced via human activities such as mining can pose a threat to human respiratory systems. Uranium oxide particulates emit short-range alpha particles that elicit DNA and direct damage, beyond associated physiochemical heavy-metal toxicity, to internal epithelial tissues. The availability of reliable in vitro models to study radiation exposure can greatly enhance our ability to understand and combat the biological impacts of exposure. However, the toxicological effects of alpha emissions and/or the oxidation states of uranium particulates vary across different human lung epithelial cell models and have not been systematically compared. We have endeavored to address this limitation by comparing impacts in three different human lung cell models: primary human bronchial and tracheal epithelial cells, primary human small airway epithelial cells, and human adenocarcinoma alveolar basal epithelial cells. Other studies have mainly investigated the toxicity of depleted uranium. Here, we compared the exposure of uranium oxide particulates (U 3 O 8 and UO 3 ) of different enrichment states on the chosen cell systems. Each cell model was exposed to 0.1, 1, 10, 50, 100, and 500 µg/mL of depleted U 3 O 8 , highly-enriched U 3 O 8 , and natural UO 3 particulates for 24 hours in submerged monolayer cultures. We compared viability and superoxide dismutase activity results across cell lines and uranium enrichment/ oxidative states. The results showed that 1) the oxide state of the particulates affected cell viability, implying that uranium’s different oxidation states contribute to different toxicological responses, and 2) each cell model reacts differently when exposed to uranium oxides, which may provide insights into the mechanistic processes associated with the exposure of radiological particulates on different biological systems. For instance, increased uranium enrichment corresponds to increased toxicity for the primary cells, but not for the immortalized cells. Our study shows that a holistic approach that incorporates similarities between model systems and types of radionuclides is required to truly develop empirical solutions for radiation exposure.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

Analyzing Carbon Dioxide and Methane Emissions in California Using Airborne Measurements and Model Simulations

Greenhouse gas (GHG) concentrations have increased over the past decades and are linked to global temperature increases and climate change. These changes in climate have been suggested to have varying effects, and uncertain consequences, on agriculture, water supply, weather, sea-level rise, the economy, and energy. To counteract the trend of increasing atmospheric concentrations of GHGs, the state of California has passed the California Global Warming Act of 2006 (AB-32). This requires that by the year 2020, GHG (e.g., carbon dioxide (CO2) and methane (CH4)) emissions will be reduced to 1990 levels. To quantify GHG fluxes, emission inventories are routinely compiled for the State of California (e.g., CH4 emissions from the California Greenhouse Gas Emissions Measurement (CALGEM) Project). The major sources of CO2 and CH4 in the state of California are: transportation, electricity production, oil and gas extraction, cement plants, agriculture, landfills/waste, livestock, and wetlands. However, uncertainties remain in these emission inventories because many factors contributing to these processes are poorly quantified. To alleviate these uncertainties, a synergistic approach of applying air-borne measurements and chemical transport modeling (CTM) efforts to provide a method of quantifying local and regional GHG emissions will be performed during this study. Additionally, in order to further understand the temporal and spatial distributions of GHG fluxes in California and the impact these species have on regional climate, CTM simulations of daily variations and seasonality of total column CO2 and CH4 will be analyzed. To assess the magnitude and spatial variation of GHG emissions and to identify local “hot spots”, airborne measurements of CH4 and CO2 were made by the Alpha Jet Atmospheric eXperiment (AJAX) over the San Francisco Bay Area (SFBA) and San Joaquin Valley (SJV) in January and February 2013 during the Discover-AQ-CA study. High mixing ratios of GHGs were observed in-flight with a high degree of spatial variability. To provide an additional method to quantify GHG emissions, and analyze AJAX measurement data, the GEOS-Chem CTM is used to simulate SFBA/SJV GHG measurements. A nested-grid version of GEOS-Chem will be applied and utilizes varying emission inventories and model parameterizations to simulate GHG fluxes/emissions. The model considers CO2 fluxes from fossil fuel use, biomass/biofuel burning, terrestrial and oceanic biosphere exchanges, shipping and aviation, and production from the oxidation of carbon monoxide, CH4, and non-methane volatile organic carbons. The major sources of CH4 simulated in GEOS-Chem are domesticated animals, rice fields, natural gas leakage, natural gas venting/flaring (oil production), coal mining, wetlands, and biomass burning. Preliminary results from the comparison between available observations (e.g., AJAX and CALGEM CH4 emission maps) and GEOS-Chem results will be presented, along with a discussion of CO2 and CH4 source apportionment and the use of the GEOS-Chem-adjoint to perform inverse GHG modeling.

Johnson, Matthew S.↗

NASA NTRSDate not supplied

XRF Characterization of MMOD Impact Damage

Introduction The Hypervelocity Impact Technology (HVIT) Group at the NASA Johnson Space Center (JSC) has performed over 150 post flight MMOD inspections on returned space-ex-posed hardware and collected over 1,600 samples. Determination of the impactor source typically requires an analysis of the projectile residue in the damage feature. At this time, about 20% of the samples have been processed. Types of Samples Samples in the HVIT group collection can be classified into two types. Indirect (non-destructive) samples are produced when a pliable material (e.g., adhesive tape or dental mold) is inserted into an impact feature. With this type of sampling technique, crater material and (hopefully) projectile residue material is embedded in the sample media and can detected using further chemical characterizing techniques. Direct (destructive) sampling involves the in-tact extraction of material from the spacecraft and is done in a manner that the entire impact feature can be excised intact. This technique provides the greatest chance of successfully determining impactor source due to the entirety of the projectile remnants being present within the collected sample. Updated Projectile Residue Detection Techniques Previous assessments of projectile provenance have involved the use of samples that are first mounted in epoxy, sectioned to expose the crater interior, imaged with a scanning electron microscope (SEM), then any observed impactor residue was then analyzed using an energy dispersive spectrometer (EDS). This technique, while proven, may be supplemented with other types of elemental analysis techniques that can observe major element chemistry, in order to improve throughput of sample analysis. The Astromaterials Research and Exploration Science (ARES) Division at JSC, for example, operates a Bruker M4 Tornado micro-X-Ray fluorescence (XRF) spectrometer, capable of creating elemental maps of areas around these small impact features in a couple of hours, with a spot size <20 μm. The potential advantage with this technique lies in sample preparation, as no polishing or conductive coatings are needed. Early trials attempted to deter-mine impactor composition by collecting spectra from intact samples by aiming the X-Rays directly into the impact feature, but the signal:noise ratio was too low for confident material identification. Sample preparation for XRF then relies on sectioning the samples parallel to the impactor trajectory in order to expose the largest area of subsurface damage possible, thus maximizing the signal from any small impactor remnants. Complementary to the use of XRF for sample analysis, ARES also houses a Nikon XTH 320 micro-X-Ray computed tomography (XCT). This instrument can be used to create 3D visualizations of impact damage for the determination of crater volumes and dimensions, as well as for the determination of the best place to section each sample for the subsequent XRF analysis.

J L Hyde↗