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342 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Determining velocity of detonation using high-resolution time domain reflectometry

Two methods are presented to make measurements of the length of a coaxial cable with submillimeter and submicrosecond accuracy. If the cable is destroyed by the passage of a strong shock, for instance, from the detonation of an explosive, a measure of the phase velocity of the detonation (VOD) can be obtained with high accuracy (≈0.25%). The first method introduces a series of short Gaussian electrical pulses (<300 ps) into the cable that reflect off the cable end and are compared to the time of the original outgoing pulse by using a fast digitizer (100 gigasamples per second). By curve fitting a Gaussian form to the outgoing and reflected pulses, a measurement of the cable length can be made with subsample accuracy. So, as long as the pulses do not overlap, a large number may be present in the cable at any time allowing pulse repetition rates of 40–135 MHz depending on the cable length. Averaging allows accurate VOD measurements to be made that are comparable with the best traditional techniques. The second method replaces the pulser with a wide-spectrum (0.1–10 GHz) amplified random noise source. A similar setup and digitizer are used before autocorrelating short (80–160 ns) subsamples of the total waveform to measure the reflected delay time. That is, a smaller amplitude version of the original waveform appears time shifted by twice the transit time in the cable and this time is estimated via autocorrelation. The advantage of this method is that a new subsample needs only to be selected from a time increment in which new spectral information is present for a new measure of the length to be made. In this manner, many more length estimates can be averaged to establish the VOD than with the Gaussian approach. As a result, a detailed description of the implementation of both methods is presented together with an example of the type of data collected and limitations found.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Undersizing of aged African biomass burning aerosol by an ultra-high-sensitivity aerosol spectrometer

The ultra-high-sensitivity aerosol spectrometer (UHSAS) differs from most other optical particle spectrometers by using a high-power infrared (IR) laser to detect small particles and reduce the sizing ambiguity due to the non-monotonicity of scattering with particle size. During the NASA ORACLES project (ObseRvations of Aerosols above CLouds and their intEractionS) over the southeast Atlantic Ocean, the UHSAS clearly undersized particles in the biomass burning plume extending from southern Africa. Since the horizontal and vertical extent of the plume was vast, the NASA P-3B research aircraft often flew through a fairly uniform biomass burning plume for periods exceeding 30 min, sufficient time to explore the details of the UHSAS response by selecting single particle sizes with a differential mobility analyzer (DMA) and passing them to the UHSAS. This was essentially an in-flight calibration of the UHSAS using the particles of interest. Two modes of responses appeared. Most particles were undersized by moderate amounts, ranging from not at all for 70 nm aerosols to 15 % for 280 nm particles. Mie scattering calculations show that composition-dependent refractive index of the particles cannot explain the pattern. Heating of brown carbon or tarballs in the beam causing evaporation and shrinking of the particles is the most plausible explanation, though mis-sizing due to non-sphericity cannot be ruled out. A small fraction (10 %–30 %) of the particles were undersized by 25 % to 35 %. Those were apparently the particles containing refractory black carbon. Laboratory calibrations confirm that black carbon is drastically undersized by the UHSAS, because particles heat to their vaporization point and shrink. A simple empirical correction equation was implemented that dramatically improves agreement with DMA distributions between 100 and 500 nm. It raised the median particle diameter by 18 nm, from 163 to 181 nm, during the August 2017 deployment and by smaller amounts during deployments with less intense pollution. Calculated scattering from UHSAS size distributions increased by about 130 %, dramatically improving agreement with scattering measured by nephelometers. The correction is only valid in polluted instances; clean marine boundary layer and free troposphere aerosols behaved more like the calibration spheres. We were unable to directly test the correction between 500 and 1000 nm, though aerodynamic particle sizer (APS) data appear to show that the correction is poor at the largest diameters, which is no surprise as the composition of those particles is likely to be quite different than that of the accumulation mode. This adds to the evidence that UHSAS data must be treated cautiously whenever the aerosol may absorb infrared light. Similar corrections may be required whenever brown carbon aerosol is present.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

DITDOS: A set of design specifications for distributed data inventories

The analysis of space science data often requires researchers to work with many different types of data. For instance, correlative analysis can require data from multiple instruments on a single spacecraft, multiple spacecraft, and ground-based data. Typically, data from each source are available in a different format and have been written on a different type of computer, and so much effort must be spent to read the data and convert it to the computer and format that the researchers use in their analysis. The large and ever-growing amount of data and the large investment by the scientific community in software that require a specific data format make using standard data formats impractical. A format-independent approach to accessing and analyzing disparate data is key to being able to deliver data to a diverse community in a timely fashion. The system in use at the Planetary Plasma Interactions (PPI) node of the NASA Planetary Data System (PDS) is based on the object-oriented Distributed Inventory Tracking and Data Ordering Specification (DITDOS), which describes data inventories in a storage independent way. The specifications have been designed to make it possible to build DITDOS compliant inventories that can exist on portable media such as CD-ROM's. The portable media can be moved within a system, or from system to system, and still be used without modification. Several applications have been developed to work with DITDOS compliant data holdings. One is a windows-based client/server application, which helps guide the user in the selection of data. A user can select a data base, then a data set, then a specific data file, and then either order the data and receive it immediately if it is online or request that it be brought online if it is not. A user can also view data by any of the supported methods. DITDOS makes it possible to use already existing applications for data-specific actions, and this is done whenever possible. Another application is a stand-alone tool to assist in the extraction of data from portable media, such as CD-ROM's. In addition to the applications, there is a set of libraries that can facilitate building new DITDOS compliant applications.

King, T. A.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-20108-DND

This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios de, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. In the version 3 algorithms, there are 6 pairs of 532 and 1064 nm lidar ratios. Some of these lidar ratios will be updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. The algorithms are being updated to eliminate the occasional confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions that are used to determine the presence of smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [de > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075< de <0.2]. The version 3 algorithms are limited to mixtures of dust and smoke, the so-called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio [LR = 35 sr] than polluted dust [currently LR = 55 sr]. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. In the v3 algorithms the frequency of dust and polluted dust aerosols at daytime is higher than at nighttime. We present possible reasons for this and present the v4 distributions resulting from both improved background slope corrections of the daytime depolarization ratios and changes to the daytime thresholds for the polluted dust and dusty marine types of version 4. To gauge the impact of the enhancements, we contrast the following between versions 3 and 4: aerosol type, parameter distributions of each type, layer heights of maximum frequency, and distributions of smoke in biomass burning regions. To illustrate specific impacts the presentation shows case studies of version 3 and version 4 vertical feature masks of the aerosol subtypes, where appropriate, for the above enhancements.

A Omar↗
NASA NTRS2004

National Airspace System Delay Estimation Using Weather Weighted Traffic Counts

Assessment of National Airspace System performance, which is usually measured in terms of delays resulting from the application of traffic flow management initiatives in response to weather conditions, volume, equipment outages and runway conditions, is needed both for guiding flow control decisions during the day of operations and for post operations analysis. Comparison of the actual delay, resulting from the traffic flow management initiatives, with the expected delay, based on traffic demand and other conditions, provides the assessment of the National Airspace System performance. This paper provides a method for estimating delay using the expected traffic demand and weather. In order to identify the cause of delays, 517 days of National Airspace System delay data reported by the Federal Aviation Administration s Operations Network were analyzed. This analysis shows that weather is the most important causal factor for delays followed by equipment and runway delays. Guided by these results, the concept of weather weighted traffic counts as a measure of system delay is described. Examples are given to show the variation of these counts as a function of time of the day. The various datasets, consisting of aircraft position data, enroute severe weather data, surface wind speed and visibility data, reported delay data and number of aircraft handled by the Centers data, and their sources are described. The procedure for selecting reference days on which traffic was minimally impacted by weather is described. Different traffic demand on each reference day of the week, determined by analysis of 42 days of traffic and delay data, was used as the expected traffic demand for each day of the week. Next, the method for computing the weather weighted traffic counts using the expected traffic demand, derived from reference days, and the expanded regions around severe weather cells is discussed. It is shown via a numerical example that this approach improves the dynamic range of the weather weighted traffic counts considerably. Time histories of these new weather weighted traffic counts are used for synthesizing two statistical features, six histogram features and six time domain features. In addition to these enroute weather features, two surface weather features of number of major airports in the United States with high mean winds and low mean visibility are also described. A least squares procedure for establishing a functional relation between the features, using combinations of these features, and system delays is explored using 36 days of data. Best correlations between the estimated delays using the functional relation and the actual delays provided by the Operations Network are obtained with two different combinations of features: 1) six time domain features of weather weighted traffic counts plus two surface weather features, and 2) six histogram features and mean of weather weighted traffic counts along with the two surface weather features. Correlation coefficient values of 0.73 and 0.83 were found in these two instances.

Chatterji, Gano B.↗
NASA NTRS2004

The Processing of Airspace Concept Evaluations Using FASTE-CNS as a Pre- or Post-Simulation CNS Analysis Tool

As NASA speculates on and explores the future of aviation, the technological and physical aspects of our environment increasing become hurdles that must be overcome for success. Research into methods for overcoming some of these selected hurdles have been purposed by several NASA research partners as concepts. The task of establishing a common evaluation environment was placed on NASA's Virtual Airspace Simulation Technologies (VAST) project (sub-project of VAMS), and they responded with the development of the Airspace Concept Evaluation System (ACES). As one examines the ACES environment from a communication, navigation or surveillance (CNS) perspective, the simulation parameters are built with assumed perfection in the transactions associated with CNS. To truly evaluate these concepts in a realistic sense, the contributions/effects of CNS must be part of the ACES. NASA Glenn Research Center (GRC) has supported the Virtual Airspace Modeling and Simulation (VAMS) project through the continued development of CNS models and analysis capabilities which supports the ACES environment. NASA GRC initiated the development a communications traffic loading analysis tool, called the Future Aeronautical Sub-network Traffic Emulator for Communications, Navigation and Surveillance (FASTE-CNS), as part of this support. This tool allows for forecasting of communications load with the understanding that, there is no single, common source for loading models used to evaluate the existing and planned communications channels; and that, consensus and accuracy in the traffic load models is a very important input to the decisions being made on the acceptability of communication techniques used to fulfill the aeronautical requirements. Leveraging off the existing capabilities of the FASTE-CNS tool, GRC has called for FASTE-CNS to have the functionality to pre- and post-process the simulation runs of ACES to report on instances when traffic density, frequency congestion or aircraft spacing/distance violations have occurred. The integration of these functions require that the CNS models used to characterize these avionic system be of higher fidelity and better consistency then is present in FASTE-CNS system. This presentation will explore the capabilities of FASTE-CNS with renewed emphasis on the enhancements being added to perform these processing functions; the fidelity and reliability of CNS models necessary to make the enhancements work; and the benchmarking of FASTE-CNS results to improve confidence for the results of the new processing capabilities.

Mainger, Steve↗
DOE OSTI2016

LONG TERM OPERATION ISSUES FOR ELECTRICAL CABLE SYSTEMS IN NUCLEAR POWER PLANTS

Nuclear power plants contain hundreds of kilometers of electrical cables including cables used for power, for instrumentation, and for control. It is essential that safety-related cable systems continue to perform following a design-basis event. Wholesale replacement of electrical cables in existing plants facing licensing period renewal is both impractical and cost-prohibitive. It is therefore important to understand the long term aging of cable materials to have confidence that aged cables will perform when needed. It is equally important in support of cable aging management to develop methods to evaluate the health of installed cables and inform selective cable replacement decisions. The most common insulation materials for electrical cables in nuclear power plants are cross-linked polyethylene and ethylene-propylene rubber. The mechanical properties of these materials degrade over time in the presence of environmental stresses including heat, gamma irradiation, and moisture. Mechanical degradation of cable insulation beyond a certain threshold is unacceptable because it can lead to insulation cracking, exposure of energized conductors, arcing and burning or loss of the ability of the cable system to function during a design-basis accident. While thermal-, radiation-, and moisture-related degradation of polymer insulation materials has been extensively studied over the last few decades, questions remain regarding the long-term performance of cable materials in nuclear plant-specific environments. Identified knowledge gaps include an understanding of the temperature-dependence of activation energies for thermal damage and an understanding of the synergistic effects of radiation and thermal stress on polymer degradation. Many of the outstanding questions in the aging behavior of cable materials relate to the necessity of predicting long-term field degradation using accelerated aging results from the laboratory. Materials degrade faster under more extreme conditions, but extension of behavior to long term degradation under more mild conditions, such as those experienced by most installed cables in nuclear power plants, is complicated by the fact that different degradation mechanisms may be involved in extreme and mild scenarios. The discrepancy in predicted results from short term, more extreme exposure and actual results from longer term, more mild exposures can be counter intuitive. For instance, due to the attenuation of oxidation penetration in material samples rapidly aged through exposure to high temperatures, the bulk of the samples may be artificially protected from thermal aging. In another example, simultaneous exposure of cable insulation material to heat and radiation may actually lead to less damage at higher temperatures than may be observed at lower temperatures. The Light Water Reactor Sustainability program of the United States (US) Department of Energy Office (DOE) of Nuclear Energy is funding research to increase the predictive understanding of electrical cable material aging and degradation in existing nuclear power plants in support of continued safe operation of plants beyond their initial license periods. This research includes the evaluation and development of methods to assess installed cable condition.

Fifield, Dr Leonard S↗
DOE OSTI2019

Accounting for hydrological and microbial processes on greenhouse gas budgets from river systems (Final Report)

River systems have a disproportionately large impact on carbon and nitrogen cycling on this planet. For instance, despite their much smaller size in area, it is estimated that global carbon dioxide emission from river systems exceed the emission from lakes and reservoirs, and are on par with the estimated oceanic and land carbon sinks. Closing carbon and nitrogen budgets in river systems requires knowledge of end member greenhouse gas (GHG) products (e.g. carbon dioxide, nitrous oxide, methane), which typically have not been measured in river systems. Moreover, while the production of these gasses is thought to be microbially derived, little is known about microbial activities in sediments of large rivers, despite their potentially high influence on biogeochemical budgets. Typically, when we think of rivers we visualize the channels that carve through the earth, but this only represents a small part of the hydrologic exchange that a river participates in. Rivers are more than conduits; they are dynamically exchanging water and materials with surrounding terrestrial and subsurface environments. In fact, 96% of the carbon dioxide produced within river systems occurs in hyporheic zones, or sediment regions beneath and alongside a river where groundwater and river water mix. In fact, it is widely recognized that microbial processes in hyporheic zones, also known as the river’s liver, strongly influence the fate of carbon, nutrients, and contaminants transported through watersheds. Despite this general knowledge, observational studies linking nitrogen and carbon gas production to microbial activity, and studies of overall gas emissions, are limited across river systems, but especially absent from larger rivers. To identify microbial processes responsible for GHG emissions from large rivers, and to quantify their environmental drivers and dependencies, we selected the Columbia River as a representative field site. This is the largest river west of the North American Continental Divide, and produces a drainage and discharge roughly double of the Nile in Egypt. This river is also an important source for agricultural irrigation and generates more hydroelectric power than any other North American river. Dams manage the release of water in the river, with these changes in river discharge patterns strongly impacting microbial activities in hyporheic zones. Specifically, we target a region down-river from the Priest Rapids Dam (WA) associated with the PNNL SFA, where water discharge from the dam is highly controlled, resulting in 1.5 m fluctuations in river stage daily. This dynamic river stage creates an ideal research opportunity for investigating hydrological perturbations in GHG production, uptake, and emission from large river systems. In this exploratory 1-year project, we conducted data-driven field research to provide observations needed to support predictions about GHG flux from river ecosystems. Using porewater dialysis and surface flux measurements, we observed where and when GHGs were produced and is the emission impacted by river stage. Using meta-omics tools, we determined what microbial processes control production and consumption of GHG in these systems. Finally, we provided key observations to parameterize models that could predict the effect of environmental conditions on GHG emission in the Columbia River near shore environment, at the scale that the modeled processes operate. Closing knowledge gaps in the linkages between river stage, microbial processes, and GHG production and emission, as well as the spatial distribution and ecosystem dependencies of these processes, is necessary to better parameterize land surface models. We provide some of the first, detailed GHG measurements paired microbial activity from large river systems. Our findings have direct relevance to many other large rivers with flows altered by hydroelectric energy, reservoirs, snowmelt, tides, or agriculture.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

3D Higher Order Modeling in the BEM/FEM Hybrid Formulation

Higher order divergence- and curl-conforming bases have been shown to provide significant benefits, in both convergence rate and accuracy, in the 2D hybrid finite element/boundary element formulation (P. Fink and D. Wilton, National Radio Science Meeting, Boulder, CO, Jan. 2000). A critical issue in achieving the potential for accuracy of the approach is the accurate evaluation of all matrix elements. These involve products of high order polynomials and, in some instances, singular Green's functions. In the 2D formulation, the use of a generalized Gaussian quadrature method was found to greatly facilitate the computation and to improve the accuracy of the boundary integral equation self-terms. In this paper, a 3D, hybrid electric field formulation employing higher order bases and higher order elements is presented. The improvements in convergence rate and accuracy, compared to those resulting from lower order modeling, are established. Techniques developed to facilitate the computation of the boundary integral self-terms are also shown to improve the accuracy of these terms. Finally, simple preconditioning techniques are used in conjunction with iterative solution procedures to solve the resulting linear system efficiently. In order to handle the boundary integral singularities in the 3D formulation, the parent element- either a triangle or rectangle-is subdivided into a set of sub-triangles with a common vertex at the singularity. The contribution to the integral from each of the sub-triangles is computed using the Duffy transformation to remove the singularity. This method is shown to greatly facilitate t'pe self-term computation when the bases are of higher order. In addition, the sub-triangles can be further divided to achieve near arbitrary accuracy in the self-term computation. An efficient method for subdividing the parent element is presented. The accuracy obtained using higher order bases is compared to that obtained using lower order bases when the number of unknowns is approximately equal. Also, convergence rates obtained using higher order bases are compared to those obtained with lower order bases for selected sample

Fink, P. W.↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Closing the Loop on Automotive Carbon Fiber Prepreg Manufacturing Scrap

The project demonstrated how to “close the loop” on carbon fiber by integrating industrial carbon fiber scrap into new functional components in an automotive lightweighting application. The project serves as a validation of discontinuous recycled carbon fiber in a commercial context, while generating comprehensive material data throughout the production chain. To this end, the project exhibited increasing complexity as material evaluation progressed from benchtop to commercial scale through full-scale part production, with key material properties thoroughly characterized throughout the process. Of particular focus was the form of the fiber that was fed into compounding, as recycled fiber has historically been problematic to feed at commercial-scale. Carbon fiber is energy-intensive to manufacture, so reuse of existing fiber material can reduce costs and increase sustainability. Additionally, by integrating recovered short fiber into a thermoplastic, regrind processes can be used to provide feedstock for later generations of product. While regrind plastics are not “infinitely recyclable”, reusing the manufacturing scrap over several generations of products can greatly increase material sustainability and lower the fractional embodied energy of each successive product. As such, this project supports the IACMI technical goals of (1) 25% lower carbon fiber-reinforced polymer (CFRP) cost, (2) 50% reduction in CFRP embodied energy, and (3) 80% composite recyclability into useful products. The initial stage of the project involved down selecting surface treatment (sizing) chemistries. Sizing evaluations were performed on Vartega’s chemically recycled intermediate modulus fiber along with standard modulus dry scrap which was oversized with sizing provided by Michelman. More dramatic improvements from sizing were found on the standard rather than the intermediate modulus fibers. The strength of the chemically recycled individual fibers were evaluated by Michelman and ORNL through single fiber testing and found to be comparable to similarly evaluated virgin fibers. UDRI’s mechanical testing on injection molded test specimens identified similar mechanical properties and fiber distribution relative to benchmark specimens. Additional surface chemistry tests and visualizations were performed by the Colorado School of Mines to confirm close conformance between the benchmark and recycled-fiber specimens. As the mechanical test results exceeded the 80% threshold established as the go/no-go(GNG), the project scale was increased to use commercial-scale equipment that would both better characterize the manufacturing utility of the target product format and allow qualitative assessment of a complex commercial part. An upscaled compounding evaluation was performed with a 27 mm twin-screw compounding extruding using oversized standard modulus fibers that were formatted to improve bulk solids transfer. The project team anticipated that milestone mechanical benchmarks could be achieved given the favorable performance of the sized standard modulus material identified in the initial micro-compounding trials. While the mechanical performance did meet the milestone target for that phase of the project, mechanical properties for this standard modulus-based compound were still less than those of the Ford specification. To compare the performance, the project team oversized intermediate modulus dry fibers and compounded them with the project resin at BASF using a 40 mm compounder. Test specimen mechanical performance exceeded the targets laid out in both the project milestone and the Ford specification. A series of prototype parts were successfully molded, albeit with instances of short shot components due to the high thermal conductivity of the carbon fiber compared to glass fiber for which the prototype tool was designed for. The project demonstrated that recycled carbon fiber is a viable option in fiber reinforced compound, providing greatly increased strength and modulus for applications that require them. The “agglomerated” format that facilitated effective bulk solids transfer of recovered fiber showed promise for industrial application.

36 MATERIALS SCIENCE↗
NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗
NASA NTRS2000

Maneuver Design and Calibration for the Genesis Spacecraft

Genesis is the fifth mission selected as part of NASA's Discovery Program. The objective of Genesis is to collect solar wind samples for a period of approximately two years while in a halo orbit about the Earth-Sun L I point. At the end of this period, the samples are to be returned to a specific recovery point on the Earth for subsequent analysis. This goal has never been attempted before and presents a formidable challenge in terms of mission design and operations, particularly planning and execution of propulsive maneuvers. To achieve a level of cost-effectiveness consistent with a Discovery-class mission, the Genesis spacecraft design was adapted to the maximum extent possible from designs used on earlier missions, such as Mars Global Surveyor (MGS) and Stardust, another sample collection mission. The spacecraft design for Genesis is shown. Spin stabilization was chosen for attitude control, in lieu of three-axis stabilization, with neither reaction wheels nor accelerometers included. This precludes closed-loop control of propulsive maneuvers and implies that any attitude changes, including spin changes and precessions, will behave like translational propulsive maneuvers and affect the spacecraft trajectory. Moreover, to minimize contamination risk to the samples collected, all thrusters were placed on the side opposite the sample collection canister. The orientation and characteristics of thrusters are indicated. For large maneuvers (>2.5 m/s), two 5 lbf thrusters will be used for delta v, with precession to the burn attitude, followed by spin-up from 1.6 to 10 rpm before the burn and spin down to 1.6 rpm afterwards, then precession back to the original spin attitude. For small maneuvers (<2.5 m/s), no spin change is needed and four 0.2 lbf thrusters are used for Av. Single or double 360 deg. precession changes are required whenever the desired delta v falls inside the two-way turn circle (about 0.4 m/s) based on the mass properties, spin rate and lever arm lengths based on thruster locations. In such instances, delta v resulting from spacecraft precession cannot be used effectively as a component of the desired delta v, and must therefore be removed by precessing at least one complete revolution around the turn circle. To eliminate cross-track execution errors, a second revolution in the opposite direction would also be performed. This paper will address the design of propulsive maneuvers in light of the aforementioned challenges and other constraints. Maneuver design will be performed jointly by the Navigation Team at JPL and the Spacecraft Team at LMA, based on the process indicated . Typical maneuver timelines will be presented which address considerations introduced by attitude changes. These include nutation, which is introduced by precessing or spinning down and must be given sufficient time to damp out prior to execution of subsequent events, as well as sun and earth pointing constraints, which must be considered to ensure sufficient spacecraft power and to minimize telecommunications interruptions, respectively. The paper will include a description of how individual propulsive maneuvers are resolved into components to account for delta v from translational burns and spacecraft attitude changes required to carry out such maneuvers. Contributions to maneuver delta v arising from attitude changes, based on mass properties for the period just after launch, are indicated. Similar curves will be presented spanning all mission phases from launch through return. A set of closed-form equations for resolving maneuver components, base on a specific delta v required for correction or deterministic changes to the spacecraft trajectory will be presented, as well. In addition to nominal maneuvers, special calibration maneuvers are planned to improve open-loop modeling of maneuvers and to reduce execution errors. Uncalibrated execution errors are indicated. Such errors could be reduced by 50% or more over the course of the mission. Special calibrations are of particular importance for the return leg of the mission, since the sample canister must be returned to a specific location within the Utah Test and Training Range (UTTR) for mid-air retrieval. An entry angle tolerance of no less than +/- 0.08 deg. is required to achieve this objective. Biasing of the final return maneuvers coupled with a specific maneuver mode to use a series of well-characterized spin changes to effect these maneuvers is part of the current Genesis baseline mission plan. Another important objective of calibrations is to better characterize precession maneuvers. Such maneuvers are part of most propulsive maneuvers, but are also required periodically to maintain sun-pointing for power or daily during solar-wind pointing during collection periods. Although relatively small, such maneuvers will have a significant cumulative impact on orbit determination, particularly in the halo portion of the mission. The current mission design also calls for three stationkeeping maneuvers during each halo orbit of approximately six months duration. These stationkeeping maneuvers may be sufficiently small that single or double 360 deg. precession changes may be required. Because there are no accelerometers on board the spacecraft, calibration can only be performed with the aid of ground-based radiometric tracking. To establish a high degree of accuracy in characterizing the magnitude of burns, the spacecraft spin axis should be along the line of sight to the Earth, providing Doppler measurements with <1 mm/sec accuracy in S-Band. Emission constraints allow such alignment only during certain portions of the mission when the Earth-spacecraft-sun geometry is favorable. The impact of precessions, or burns at times when geometry is not favorable, can be assessed by reconstruction of the spacecraft trajectory using tracking arcs of several days before and after the event.

Williams, Kenneth E.↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
NASA NTRSDate not supplied

Distributed Spacecraft Autonomy - Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC) measurements. DSA will be demonstrated onboard the NASA Ames Starling mission – a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets. The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasmasphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input dataset for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software, aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements, which define the range of observability for the satellites, as well as exploitative measurements, which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth’s magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions. As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown. Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middleware provides reliable delivery, routing, and topic subscription features over User Datagram Protocol (UDP). Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarm scalability from 2 satellites to 100 satellites is shown.

Distributed Autonomy↗
NASA NTRS2020

Distributed Spacecraft Autonomy (DSA): Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC)measurements. DSA will be demonstrated on board the NASA Ames Starling mission a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets.The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasma sphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input data set for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software,aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements,which define the range of observability for the satellites, as well as exploitative measurements,which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth's magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions.As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown.Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middle ware provides reliable delivery, routing, and topic subscription features over User Data gram Protocol (UDP).Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarms calability from 2 satellites to 100 satellites is shown.

Fugate, Jason↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Project 57 Air Monitoring Report: January 1 through December 31, 2017

On April 24, 1957, the Atomic Energy Commission (AEC) (now the Department of Energy [DOE]) conducted the Project 57 safety experiment in western Emigrant Valley northeast of the Nevada National Security Site (NNSS, formerly the Nevada Test Site) on lands withdrawn by the Department of Defense (DOD) for the Nevada Test and Training Range (NTTR). The test was undertaken to develop (1) a means of estimating plutonium distribution resulting from a non-nuclear detonation; (2) biomedical evaluation techniques for use in plutonium-laden environments; (3) methods of surface decontamination; and (4) instruments and field procedures for prompt estimation of alpha contamination (Shreve, 1958). Although the test did not result in the fission of nuclear materials, it did disseminate plutonium across the land surface. Following the experiment, the AEC fenced the contaminated area and returned control of the surrounding land to the DOD. Various radiological surveys were performed in the area and in 2007, the DOE expanded the demarked Contamination Area (CA) by posting signs 200 to 400 ft (60 to 120 m) outside of the original fence. Plutonium in soil attaches preferentially to smaller particles (Tamura, 1985; Friesen, 1992; Murarik et al., 1992; and Misra et al., 1993). Therefore, redistribution of soil particles by wind (dust) and water are the mechanisms most likely to transport plutonium beyond the boundary of the Project 57 CA. Monitoring was implemented in 2011 by Desert Research Institute (DRI) to determine if radionuclide contamination was detectable in samples of airborne dust and to characterize meteorological and environmental parameters that influence dust transport. Initially, two monitoring stations consisting of radiological, meteorological, and dust sampling equipment were installed near the southeast and northeast corners of the CA. In January 2015, the original monitoring stations were dismantled and moved farther to the west along the CA boundary. This move was made to place the monitoring stations downwind of ground zero and the High Contamination Area (HCA) during the dominant northerly and southerly winds. Samples of particles suspended in the air are collected every two weeks and submitted for laboratory assessments of gross alpha and gross beta radioactivity, and for determinations of gamma-emitting radionuclides. In calendar year (CY) 2017, the mean gross alpha concentration at monitoring station P57-4 was approximately 4.25 times the value at monitoring station P57-3. In CY2015 and CY2016, the difference in mean gross alpha concentration at the two stations was approximately 1.5. The mean gross alpha concentrations at P57-3 and P57-4 were approximately 1.9 and 7.9 times, respectively, the mean gross alpha concentrations at the surrounding Community Environmental Monitoring Program (CEMP) stations. The minimum gross alpha concentrations reported for the Project 57 stations are in the range of the minimum values observed at the CEMP stations. However, maximum gross alpha concentrations are greater than the maximum concentrations observed at the surrounding CEMP stations. Gamma spectroscopy analyses resulted in the detection of seven radionuclides. Except for americium-241 (Am-241), all reported radionuclides occur naturally in the environment. The Am-241 detection by gamma spectroscopy was confirmed by alpha spectroscopy. During each quarter of CY2017, four suspended particulate matter samples were selected for alpha spectroscopy analysis. Two were selected each quarter from the regular biweekly samples collected at each of the two monitoring stations. Of the eight samples from P57-3, one was reported to have plutonium-238 (Pu-238) above the minimum detectable activity (MDA) and five were reported to have plutonium 239/240 (Pu-239/240) above the MDA. Of the eight samples from P57-4, three were reported to have Pu-238 above the MDA and six were reported to have Pu-239/240 above the MDA. Soil material is also transported by saltation, which is a wind driven phenomena that bounces sand-sized soil particles across the land surface because they are too heavy to be suspended in the air. Samples of particles transported by saltation were collected downwind and upwind of the CA at both monitoring stations in May 2017 after a 190-day deployment and again in September 2017 after a 114-day deployment. Mass of the saltation samples was determined after oven drying. The September sample had greater mass than the May sample. The same relationship was observed for the suspended particulate matter samples during periods of saltation sample collection. The difference in the collected mass appears to be because of the amount of soil moisture. Higher soil moisture exerts additional isotopic bonds between soil particles and water molecules, and between water molecules that envelop soil particles, making it more difficult for wind to raise the particles as dust. In general, samples collected from saltation traps facing south contained more material than samples collected from traps facing north. This suggests that although saltation material may have been moving back and forth in conjunction with the two dominant wind directions, there was a net trend for saltation material to be transported toward the north. Radiological analyses of the saltation samples show that the smaller particle size faction (<63 μm) generally produced the highest activity levels of Am-241, Pu-238, and Pu-239/240. In general, the isotope activity levels decreased as the particle size increased. These results are consistent with the expectation that the greatest potential for transport of radiological contamination is with the smaller particles. Thermoluminescent dosimeters (TLDs) indicated that the average annual external radioactivity dose at the monitoring stations is slightly higher than the dose determined at the surrounding CEMP stations, but approximately half of the estimated national average dose received by the general public from exposure to natural sources. The TLDs at the Project 57 monitoring stations are exposed to both natural sources (terrestrial and cosmic) and radioactive releases from the Project 57 CA. Winds in excess of approximately 15 mph (24 km/hr) begin to generate dust movement by saltation or suspension, as determined by the monitoring instruments. Saltated particles, PM10 (i.e., inhalable dust), and PM2.5 (i.e., fine particulate dust) exhibit an approximately exponential increase with increasing wind speed. The greatest concentrations of dust occurred for winds exceeding 20 mph (32 km/hr). When the wind/dust analysis was performed for winds separated into the dominant northerly and southerly wind directions, it was evident that the northerly winds generated the higher PM10 concentrations and had the dominate influence on the PM10 concentration determined for all wind speeds when all wind directions were evaluated together. During the year, winds in excess of 20 mph (32 km/hr) occurred approximately one percent of the time. Although winds sufficient to generate dust occur at the Project 57 site, they are infrequent and of short duration. A preliminary assessment of individual wind events suggests that dust generation in response to specific wind conditions is highly variable. Wind speeds that would be expected to generate noticeable dust may not do so in every instance. Additionally, saltation transport in response to wind conditions was less common than expected. These variations were likely because of the influence of meteorological and environmental parameters other than wind.

54 ENVIRONMENTAL SCIENCES↗