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DOE OSTI2019

Improving Operational Efficiency, Compliance and Community Perception with Real-time Data - 19260

The nuclear and medical radiation industry has always faced a significant burden in effectively balancing exposure and productivity. Maintenance operations and development of new techniques require significant staffing resources to monitor and review personnel exposures and changes in operational radiation levels. Currently utilised monitoring technology and review techniques remain unchanged since the 1950's, relying upon mostly analogue instrumentation and paper reporting. After utilising isolated-area monitoring during a reactor decommissioning project it became apparent that current systems created false assurance as the units continually failed without warning due to 'dirty' power. These instrument failures could have easily led to an unnecessary exposure while operators continued to assume they were being safely monitored. This paper is a philosophical discussion on how current shortcomings could be addressed, security increased, and exposure risks minimised, by utilising real-time monitoring and automated reporting. The community and media expectations of reported data and nuclear monitoring systems continue to be coloured by sensational technology seen in movies and emerging technology available through smart homes and smart cities initiatives. For the general public to be more engaged with real radiation levels and effects, the nuclear industry needs to more effectively communicate safe levels. Organisations can have regain control of exposure levels discussions through the controlled release of background and expected exposure level data. An increase in real world monitoring data communicated in a controlled and more effective way will aid in the general public accepting, and being educated about, the minimal increase above natural background levels of the nuclear industry and other radiation facilities. This is particularly as Australia is in the process of implementing its Nuclear Waste Facility, with community acceptance the number one inhibitor of being able to move forward. Storage, mobile assets, and processing all present unique management, security, exposure, and telemetry challenges for a facility operations manager. Active monitoring and automated review of the monitoring data can alert operators to any unexpected events before any resultant increases in radiation levels cause an unplanned exposure to operators or the environment. Integration of radiation monitoring data with global positioning telemetry enables better organisational awareness of mobile asset location and early detection of any potential shifting and reduction in shielding integrity of sources and encapsulated waste during transport. Additionally, the real-time monitoring can be set-up to enhance an organisations' or nation's security capabilities. For instance, the real time monitor will send an automated alert in the case of an unexpected movement of a source through a monitored facility or for an unscheduled movement of a monitored vehicle. There are further benefits to be gained in control, ALARA principles and effluent management by increasing real-time monitoring capabilities through the stages of the waste cycle. Increasing operational effectiveness while delivering operational cost savings sounds like an attractive proposition. With increased access to operational area radiation data comes drastically improved real-time management of exposures for new, and experimental practices. Increased live data can be utilised to identify optimum practices for reducing operational and environmental exposures or releases. This increased operational data effectively comes with the added benefit of a reduction in the required health physics personnel resources for general operations. By reducing exposure and the detachment of required health physics resources from routine operations, there is greater availability for these resources to support maintenance operations and project-based work. The review of this increased data will create internal capability to identify low-- and high-risk activities, resulting in the ability to reduce the number of highly skilled workers in an industry where the availability of this talent is declining. During my time as the Radiation Safety and Nuclear Security Manager at a research facility, I implemented a real-time monitoring system. The system identified a secondary risk that had previously been unidentified and posed a higher exposure and security risk than the radiation practice being monitored. What stops industry from implementing technology that encourages agile work practices, while minimising security risks and personnel exposure? (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Fission with Exotic Nuclei (Abbreviated Report)

Nuclear fission is a key mechanism involved in the synthesis of heavy elements in the Cosmos and is the primary explanation for the stability of superheavy elements. Nevertheless, our knowledge of fission remains extremely fragmented. Most experiments have been conducted only on a tiny number of stable actinide nuclei and are often incomplete, leading to gaps in our basic understanding of the process. For many radioactive isotopes, basic fission data such as the charge or mass distribution of the fragments is unknown. These gaps cannot always be filled by simulation alone. Common fission models contain too many free parameters and lack predictive power. In contrast, the fundamental theory of fission under development at LLNL is much more predictive, but its current computational cost is too high to be used extensively for data evaluations. A unique window of opportunity to resolve these limitations has recently opened: the U.S. nuclear science community is ramping up major experimental programs at the Facility for Rare Isotope Beams (FRIB, the DOE flagship facility in low-energy nuclear science), and techniques from machine learning have shown great potential to simplify the use of a fundamental, quantum-mechanical theory of fission. This project has two components. On the experimental side, we acquired and deployed at the HIGS facility a new dual Frisch-Grid ionization chamber to measure correlated fragment-mass, kinetic energy, and angular distributions of fission fragments from induced fission. This new device was used to perform measurements of charge, mass and total kinetic energy of fission fragments in the photofission of 238 U and eight gamma-ray beam energies between 6.2 and 13 MeV, which allowed extracting high-precision independent yields for this reaction. The device was also used to perform measurements of the same quantities in the neutron-induced fission of 234 U with monoenergetic beams of energy between 5 and 8 MeV. In parallel, we collaborated with a team at Commissariat à l’énergie atomique et aux énergies alternatives (CEA) to perform a series of measurements of fission yields in inverse kinematics for the two isotopes of 236 U and 240 Pu. The experiment took place at the Grand Accélérateur National d’Ions Lourds in France in June 2023. The deployment of the VAMOS spectrometer with a new array called PISTA allowed determining the excitation energy of the fissioning system within 1 Mega-electronvolts. The second component of the project involved using deep neural networks to build fast and reliable emulators of our current fission models. In an invited paper published in Frontier in Physics, we showed that autoencoders could successfully compress nuclear wavefunctions in nuclear density functional theory. We achieved a dimensionality reduction of the order of two orders of magnitude while keeping the error in the total energy to less than 0.01%. In a second paper submitted to Physical Review Letters in June 2023 with our collaborators at CEA, we showed that variational autoencoders can learn the collective degrees of freedom driving the fission process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis

In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

A New Bond Albedo for Performing Orbital Debris Brightness to Size Transformations

We have developed a technique for estimating the intrinsic size distribution of orbital debris objects via optical measurements alone. The process is predicated on the empirically observed power-law size distribution of debris (as indicated by radar RCS measurements) and the log-normal probability distribution of optical albedos as ascertained from phase (Lambertian) and range-corrected telescopic brightness measurements. Since the observed distribution of optical brightness is the product integral of the size distribution of the parent [debris] population with the albedo probability distribution, it is a straightforward matter to transform a given distribution of optical brightness back to a size distribution by the appropriate choice of a single albedo value. This is true because the integration of a powerlaw with a log-normal distribution (Fredholm Integral of the First Kind) yields a Gaussian-blurred power-law distribution with identical power-law exponent. Application of a single albedo to this distribution recovers a simple power-law [in size] which is linearly offset from the original distribution by a constant whose value depends on the choice of the albedo. Significantly, there exists a unique Bond albedo which, when applied to an observed brightness distribution, yields zero offset and therefore recovers the original size distribution. For physically realistic powerlaws of negative slope, the proper choice of albedo recovers the parent size distribution by compensating for the observational bias caused by the large number of small objects that appear anomalously large (bright) - and thereby skew the small population upward by rising above the detection threshold - and the lower number of large objects that appear anomalously small (dim). Based on this comprehensive analysis, a value of 0.13 should be applied to all orbital debris albedo-based brightness-to-size transformations regardless of data source. Its prima fascia genesis, derived and constructed from the current RCS to size conversion methodology (SiBAM Size-Based Estimation Model) and optical data reduction standards, assures consistency in application with the prior canonical value of 0.1. Herein we present the empirical and mathematical arguments for this approach and by example apply it to a comprehensive set of photometric data acquired via NASA's Liquid Mirror Telescopes during the 2000-2001 observing season.

Mulrooney, Mark K.↗

NASA NTRS1992

Deep near-infrared survey of the Southern Sky (DENIS)

DENIS (Deep Near-Infrared Survey of the Southern Sky) will be the first complete census of astronomical sources in the near-infrared spectral range. The challenges of this novel survey are both scientific and technical. Phenomena radiating in the near-infrared range from brown dwarfs to galaxies in the early stages of cosmological evolution, the scientific exploitation of data relevant over such a wide range requires pooling expertise from several of the leading European astronomical centers. The technical challenges of a project which will provide an order of magnitude more sources than given by the IRAS space mission, and which will involve advanced data-handling and image-processing techniques, likewise require pooling of hardware and software resources, as well as of human expertise. The DENIS project team is composed of some 40 scientists, computer specialists, and engineers located in 5 European Community countries (France, Germany, Italy, The Netherlands, and Spain), with important contributions from specialists in Australia, Brazil, Chile, and Hungary. DENIS will survey the entire southern sky in 3 colors, namely in the I band at a wavelength of 0.8 micron, in the 1.25 micron J band, and in the 2.15 micron K' band. The sensitivity limits will be 18th magnitude in the I band, 16th in the J band, and 14.5th in the K' band. The angular resolution achieved will be 1 arcsecond in the I band, and 3.0 arcseconds in the J and K' bands. The European Southern Observatory 1 m telescope on La Silla will be dedicated to survey use during operations expected to last four years, commencing in late 1993. DENIS aims to provide the astronomical community with complete digitized infrared images of the full southern sky and a catalogue of extracted objects, both of the best quality and in readily accessible form. This will be achieved through dedicated software packages and specialized catalogues, and with assistance from the Leiden and Paris Data Analysis Centers. The data will be mailed on DAT tapes from La Silla to the two Data Analysis Centers for further processing. Two centers are necessary because of the shear quantity of data and because of the complementary roles the Centers will develop, each exploiting its own particular expertise. The Leiden Data Analysis Center (LDAC) will extract objects, establish their parameters, and archive them into a source catalogue. The LDAC will collaborate with the Groningen Space Research group that has gained experience in infrared image handling from the IRAS satellite. The Paris Data Analysis Center (PDAC) will be responsible for archiving and preprocessing the raw data to provide a homogeneous set of data suitable for further reduction in both the Leiden and Paris data analysis streams. The PDAC will also extract and archive images for the sources flagged by the LDAC as extended, and create a catalogue of galaxies. In exploiting the DENIS data we foresee the collaboration with other data analysis centers, such as the Observatoire de Lyon where the relevant DENIS catalogue of galaxies can be incorporated into their extragalactic database. The Point Sources and the Small Extended Sources catalogues could be incorporated in the Late Type Star database at Montpellier, and in the SIMBAD database as CDS. At Groningen the IRAS Point Source catalogue and/or image data can be merged with the DENIS catalogues. At Meudon algorithms and software will be developed with main goal assessing the limits reachable for the homogeneity and intrinsic consistency between the ensemble of the images in the data base (flat-fielding, relative positioning of the fields, bootstrapped flux calibration) but also for the data analysis.

Deul, E.↗

DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗

NASA NTRS2008

Optical Communications from Planetary Distances

Future planetary campaigns, including human missions, will require data rates difficult to realize by microwave links. Optical channels not only provide an abundance of bandwidth, they also allow for significant size, weight, and power reduction. Moreover, optical-based tracking may enhance spacecraft navigation with respect to microwave-based tracking. With all its advantages, optical communications from deep space is not without its challenges. Due to the extreme distance between the two ends of the link, specialized technologies are needed to enable communications in the deep space environment. Although some of the relevant technologies have been developed in the last decade, they remain to be validated in an appropriate domain. The required assets include efficient pulsed laser sources, modulators, transmitters, receivers, detectors, channel encoders, precise beam pointing technologies for the flight transceiver and large apertures for the ground receiver. Clearly, space qualification is required for the systems that are installed on a deep space probe. Another challenge is atmospheric effects on the optical beam. Typical candidate locations on the ground have a cloud-free line of sight only on the order of 60-70% of the time. Furthermore, atmospheric losses and background light can be problematic even during cloud-free periods. Lastly, operational methodologies are needed for efficient and cost effective management of optical links. For more than a decade, the National Aeronautics and Space Administration (NASA) has invested in relevant technologies and procedures to enable deep space optical communications capable of providing robust links with rates in the order of 1 Gb/s from Mars distance. A recent publication indicates that potential exists for 30-dB improvement in performance through technology development with respect to the state-of-the-art in the early years of this decade. The goal is to fulfill the deep space community needs from about 2020 to the foreseeable future. It is envisioned that, at least initially, optical links will be complemented by microwave assets for added robustness, especially for human missions. However, it is expected that as optical techniques mature, laser communications may be operated without conventional radio frequency links. The purpose of this paper is to briefly review the state-of-the-art in deep space laser communications and its challenges and discuss NASA-supported technology development efforts and plans for deep space optical communications at JPL.

Laser Communications↗

DOE OSTI2022

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modeling and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use (Final Report)

To increase carbon (C) deposition in the soil and enhance crop resource use efficiency, characterizing root form and function is essential. Several root and soil traits have been linked to increased root-to-soil C transfer. Technology that could provide high-resolution characterization of many of these traits in field conditions would revolutionize our ability to study and understand how to increase C sequestration. In this effort, we developed an initial early prototype backscatter X-ray system for non-destructive imaging of root traits. We collected initial backscatter X-ray data in field and lab settings and carried out early analysis of these data. Along with this prototype, we also developed a suite of root phenotyping approaches including advanced minirhizotron image analysis, soil core imaging, and mesocosm imaging. Minirhizotron (MR) tubes are clear tubes inserted into the soil in the field and used to image roots and the surrounding soil. Our team has developed deep learning-based methods that can segment roots from soil that can learn from imprecise image-level labels. The ability to learn or fine-tune our deep learning algorithms from image-level labels allows easier and faster application of these approaches to new locations and new plant species. We have successfully implemented and applied our MR analysis approaches to thousands of switchgrass MR images collected across geographical regions. An advantage of MR imaging is the ability to collect root and soil images over time. Our soil core analysis included collecting hundreds of soil core samples from harvested switchgrass fields and imaging these cores with both X-ray CT and backscatter X-ray imaging. Initial segmentation approaches for the X-ray CT images of these cores have been developed and applied. An advantage of soil core analysis is that it preserves the three-dimensional structures of the roots and soil in the core collected. Our group also developed photogrammetry-based mesocosm root imaging and phenotyping approaches. In this approach, a plant was grown in a large mesocosm with a three-dimensional grid of thin supporting lines inserted throughout the mesocosm. After the plant (and, correspondingly, the root architecture is grown and established) the soil media was removed and the supporting lines approximately preserved the three-dimensional root architecture. Then, we applied photogrammetry techniques to create a three-dimensional digital representation of the root architecture for which we developed analysis algorithms including skeletonization. We carried out our phenotyping development with powerful switchgrass resources and physiological and agroecosystem modeling to deliver novel technology. This project contributes to multiple ARPA-E missions including reduction of foreign imports of energy, reduction of energy-related emissions including greenhouse gases, and ensuring that the United States maintains a technological lead in developing and deploying advanced energy technology. Furthermore, the developed tools could transform public and private plant breeding and could be broadly applicable to other crops and, potentially, other application areas. Our team of engineers, plant and soil scientists, and modelers i) developed an early prototype backscatter X-ray platform that can operate in field conditions; ii) developed a suite of root phenotyping and characterization approaches as described above; iii) developed and carried out plant biology and physiology roots studies and; iv) developed and implemented mechanistic physiological modeling.

42 ENGINEERING↗

NASA NTRS2001

Relative effectiveness of kinetic analysis vs single point readings for classifying environmental samples based on community-level physiological profiles (CLPP)

The relative effectiveness of average-well-color-development-normalized single-point absorbance readings (AWCD) vs the kinetic parameters mu(m), lambda, A, and integral (AREA) of the modified Gompertz equation fit to the color development curve resulting from reduction of a redox sensitive dye from microbial respiration of 95 separate sole carbon sources in microplate wells was compared for a dilution series of rhizosphere samples from hydroponically grown wheat and potato ranging in inoculum densities of 1 x 10(4)-4 x 10(6) cells ml-1. Patterns generated with each parameter were analyzed using principal component analysis (PCA) and discriminant function analysis (DFA) to test relative resolving power. Samples of equivalent cell density (undiluted samples) were correctly classified by rhizosphere type for all parameters based on DFA analysis of the first five PC scores. Analysis of undiluted and 1:4 diluted samples resulted in misclassification of at least two of the wheat samples for all parameters except the AWCD normalized (0.50 abs. units) data, and analysis of undiluted, 1:4, and 1:16 diluted samples resulted in misclassification for all parameter types. Ordination of samples along the first principal component (PC) was correlated to inoculum density in analyses performed on all of the kinetic parameters, but no such influence was seen for AWCD-derived results. The carbon sources responsible for classification differed among the variable types with the exception of AREA and A, which were strongly correlated. These results indicate that the use of kinetic parameters for pattern analysis in CLPP may provide some additional information, but only if the influence of inoculum density is carefully considered. c2001 Elsevier Science Ltd. All rights reserved.

NASA Center KSC↗

DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗

NASA NTRS2011

Wireless Sensor Networks for Developmental and Flight Instrumentation

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network and ZigBee Pro 2007 standards are finding increasing use in home automation and smart energy markets providing a framework for interoperable software. The Wireless Connections in Space Project, funded by the NASA Engineering and Safety Center, is developing technology, metrics and requirements for next-generation spacecraft avionics incorporating wireless data transport. The team from Stennis Space Center and Mobitrum Corporation, working under a NASA SBIR grant, has developed techniques for embedding plug-and-play software into ZigBee WSN prototypes implementing the IEEE 1451 Transducer Electronic Datasheet (TEDS) standard. The TEDS provides meta-information regarding sensors such as serial number, calibration curve and operational status. Incorporation of TEDS into wireless sensors leads directly to building application level software that can recognize sensors at run-time, dynamically instantiating sensors as they are added or removed. The Ames Research Center team has been experimenting with this technology building demonstration prototypes for on-board health monitoring. Innovations in technology, software and process can lead to dramatic improvements for managing sensor systems applied to Developmental and Flight Instrumentation (DFI) aboard aerospace vehicles. A brief overview of the plug-and-play ZigBee WSN technology is presented along with specific targets for application within the aerospace DFI market. The software architecture for the sensor nodes incorporating the TEDS information is described along with the functions of the Network Capable Gateway processor which bridges 802.15.4 PAN to the TCP/IP network. Client application software connects to the Gateway and is used to display TEDS information and real-time sensor data values updated every few seconds, incorporating error detection and logging to help measure performance and reliability in relevant target environments. Test results from our prototype WSN running the Mobitrum software system are summarized and the implications to the scalability and reliability for DFI applications are discussed. Our demonstration system, incorporating sensors for life support system and structural health monitoring is described along with test results obtained by running the demonstration prototype in relevant environments such as the Wireless Habitat Testbed at Johnson Space Center in Houston. An operations concept for improved sensor process flow from design to flight test is outlined specific to the areas of Environmental Control and Life Support System performance characterization and structural health monitoring of human-rated spacecraft. This operations concept will be used to highlight the areas where WSN technology, particularly plug-and-play software based on IEEE 1451, can improve the current process, resulting in significant reductions in the technical effort, overall cost and schedule for providing DFI capability for future spacecraft. RELEASED -

Alena, Richard↗

NASA NTRSDate not supplied

TechEdSat 7, 10, 13, 15: Exo-brake Experiments on the ISS, First Virgin Orbit, and First Firefly-Alpha Test Flights.

The TechEdSat flight series (TES-n), developed by the Nano Orbital Workshop (NOW) group at NASA Ames, has been studying cube satellite re-entry technologies with a focus on Exo-Brake drag device research. An exo-atmospheric braking device, the Exo-Brake uses the tenuous upper atmosphere acting in a free molecular flow regime to enable controllable adjustment of the drag profile of the host spacecraft, currently enabling rapid disposal of small spacecraft after mission conclusion, and eventual targeted de-orbit to a desired entry point at the Von Karman line, or approximately 100km in altitude. The TES-7 mission, flown on the first successful Virgin Orbit flight January 17, 2021, was injected into a 500km, 61-degree inclination orbit, with an expected orbital lifetime of approximately ten years. After successful deployment of a unique hydrogen gas-cell inflation design ‘disposal’-type Mylar Exo-Brake intended to rapidly de-orbit the spacecraft, the maximum expected orbital lifetime of the TES-7 spacecraft was successfully reduced from ten years to 1.3 years, an 87% reduction in orbit lifetime via a passive, fuel-less system. The subsequent TES-13 mission utilized a new, less complex ‘disposal’-type Exo-Brake design made from collapsible rigid struts rather than inflated struts to reduce the engineering, construction, and safety complexities introduced by the prior hydrogen gas inflation design. The rigid strut Exo-Brake design is stowed via rotational compression and deploys using only stored spring energy once released via electronic actuator. This new rotation compression storage design has become the new Exo-Brake design standard used on TES missions thanks to its simplicity. Following on the success of this new Exo-Brake design demonstrated by TES-13, the design was further refined to enabled active manipulation of the Exo-Brake effective drag area though a winch-like system. Both the TES-10 and TES-15 spacecraft were equipped with geometrically similar Exo-Brake devices capable of such active drag modulation, with the intent of studying the operational impact of having such devices on cube satellite missions. To increase the survival time of the Exo-Brake at low altitudes and thus gain more experiment time and better guidance capability, TES-15 was equipped with a modulated Exo-Brake constructed from 3M™ Nextel™ 440 ceramic oxide fiber, the same fabric material used in the Space Shuttle TPS system, rather than Mylar. After launching on Alpha Flight 2 October 1st, 2022, Firefly’s first successful launch, TES-15 met the same modulation restrictions as TES-10, with modulation prohibited above 200km to avoid possible collision with other spacecraft. In this case, a low deployment orbit caused an extremely short mission life, which, coupled with delayed spacecraft identification, exacerbated collision concerns and caused performance data of the high-temperature Exo-Brake design to be inconclusive. As such, the Nano Orbital Workshop has taken a new approach of only conducting modulated Exo-Brake research on missions deploying below ISS altitude to avoid collision avoidance restrictions, and is working to improve spacecraft identification and location reporting techniques to increase experiment durations. Upcoming TES mission will therefore use one of two classes of Exo-Brake depending on their target altitude, as to be described in the submission.

M Murbach↗

NASA NTRSDate not supplied

TechEdSat 7, 10, 13, 15: Exo-brake Experiments on the ISS, First Virgin Orbit, and First Firefly-Alpha Test Flights

Marcus Murbach↗

DOE OSTI2017

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO 2 storage monitoring

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. Here, we present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO 2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

42 ENGINEERING↗

DOE OSTI2025

Spatial evidence of cryptic methane cycling and methylotrophic metabolisms along a land–ocean transect in salt marsh sediment

Methylotrophic methanogenesis in the sulfate-rich zone of coastal and marine sediments couples with anaerobic oxidation of methane (AOM), forming the cryptic methane cycle. This study provides evidence of cryptic methane cycling in the sulfate-rich zone across a land–ocean transect of four stations–two brackish, one marine, and one hypersaline–within the Carpinteria Salt Marsh Reserve (CSMR), southern California, USA. Samples from the top 20 cm of sediment from the transect were analyzed through geochemical and molecular (16S rRNA) techniques, in-vitro methanogenesis incubations, and radiotracer incubations utilizing 35 S-SO 4 , 14 C-mono-methylamine, and 14 C-CH 4 . Sediment methane concentrations were consistently low (3 to 28 µM) at all stations, except for the marine station, where methane increased with depth reaching 665 µM. Methanogenesis from mono-methylamine was detected throughout the sediment at all stations with estimated CH 4 production rates in the sub-nanomolar to nanomolar range per cm 3 sediment and day. 16S rRNA analysis identified methanogenic archaea (Methanosarcinaceae, Methanomassiliicoccales, and Methanonatronarchaeacea) capable of producing methane from methylamines in sediment where methylotrophic methanogenesis was found to be active. Metabolomic analysis of porewater showed mono-methylamine was mostly undetectable (<3 µM) or present in trace amounts (<10 µM) suggesting rapid metabolic turnover. In-vitro methanogenesis incubations of natural sediment showed no linear methane buildup, suggesting a process limiting methane emissions. AOM activity, measured with 14 C-CH 4 , overlapped with methanogenesis from mono-methylamine activity at all stations, with rates ranging from 0.03 to 19.4 nmol cm −3 d −1 . Geochemical porewater analysis showed the CSMR sediments are rich in sulfate and iron. Porewater sulfate concentrations (9–91 mM) were non-limiting across the transect, supporting sulfate reduction activity (1.5–2,506 nmol cm −3 d −1 ). Porewater sulfide and iron (II) profiles indicated that the sediment transitioned from a predominantly iron-reducing environment at the two brackish stations to a predominantly sulfate-reducing environment at the marine and hypersaline stations, which coincided with the presence of phyla (Desulfobacterota) involved in these processes. AOM activity overlapped with sulfate reduction and porewater iron (II) concentrations suggesting that AOM is likely coupled to sulfate and possibly iron reduction at all stations. However, 16S rRNA analysis identified anaerobic methanotrophs (ANME-2) only at the marine and hypersaline stations while putative methanogens were found in sediment across all stations. In one sediment horizon at the marine station, methanogen families (Methanosarcinaceae, Methanosaetaceae, Methanomassiliicoccales, and Methanoregulaceae) and ANME 2a,2b, and 2c groups were found together. Collectively, our data suggest that at the brackish stations methanogens alone may be involved in cryptic methane cycling, while at the marine and hypersaline stations both groups may be involved in the process. Differences in rate constants from incubations with 14 C-labeled methane and mono-methylamine suggest a non-methanogenic process oxidizing mono-methylamine to inorganic carbon, likely mediated by sulfate-reducing bacteria. Understanding the potential competition of sulfate reducers with methanogens for mono-methylamine needs further investigation as it might be another important process responsible for low methane emissions in salt marshes.

Anaerobic oxidation of methane↗

NASA NTRS2003

5' modification of duplex DNA with a ruthenium electron donor-acceptor pair using solid-phase DNA synthesis

Incorporation of metalated nucleosides into DNA through covalent modification is crucial to measurement of thermal electron-transfer rates and the dependence of these rates with structure, distance, and position. Here, we report the first synthesis of an electron donor-acceptor pair of 5' metallonucleosides and their subsequent incorporation into oligonucleotides using solid-phase DNA synthesis techniques. Large-scale syntheses of metal-containing oligonucleotides are achieved using 5' modified phosporamidites containing [Ru(acac)(2)(IMPy)](2+) (acac is acetylacetonato; IMPy is 2'-iminomethylpyridyl-2'-deoxyuridine) (3) and [Ru(bpy)(2)(IMPy)](2+) (bpy is 2,2'-bipyridine; IMPy is 2'-iminomethylpyridyl-2'-deoxyuridine) (4). Duplexes formed with the metal-containing oligonucleotides exhibit thermal stability comparable to the corresponding unmetalated duplexes (T(m) of modified duplex = 49 degrees C vs T(m) of unmodified duplex = 47 degrees C). Electrochemical (3, E(1/2) = -0.04 V vs NHE; 4, E(1/2) = 1.12 V vs NHE), absorption (3, lambda(max) = 568, 369 nm; 4, lambda(max) = 480 nm), and emission (4, lambda(max) = 720 nm, tau = 55 ns, Phi = 1.2 x 10(-)(4)) data for the ruthenium-modified nucleosides and oligonucleotides indicate that incorporation into an oligonucleotide does not perturb the electronic properties of the ruthenium complex or the DNA significantly. In addition, the absence of any change in the emission properties upon metalated duplex formation suggests that the [Ru(bpy)(2)(IMPy)](2+)[Ru(acac)(2)(IMPy)](2+) pair will provide a valuable probe for DNA-mediated electron-transfer studies.

Non-NASA Center↗

DOE OSTI2022

Functional Data Analysis for Extracting the Intrinsic Dimensionality of Spectra: Application to Chemical Homogeneity in the Open Cluster M67

High-resolution spectroscopic surveys of the Milky Way have entered the Big Data regime and have opened avenues for solving outstanding questions in Galactic archeology. However, exploiting their full potential is limited by complex systematics, whose characterization has not received much attention in modern spectroscopic analyses. In this work, we present a novel method to disentangle the component of spectral data space intrinsic to the stars from that due to systematics. Using functional principal component analysis on a sample of 18,933 giant spectra from APOGEE, we find that the intrinsic structure above the level of observational uncertainties requires ≈10 functional principal components (FPCs). Our FPCs can reduce the dimensionality of spectra, remove systematics, and impute masked wavelengths, thereby enabling accurate studies of stellar populations. To demonstrate the applicability of our FPCs, we use them to infer stellar parameters and abundances of 28 giants in the open cluster M67. We employ Sequential Neural Likelihood, a simulation-based Bayesian inference method that learns likelihood functions using neural density estimators, to incorporate non-Gaussian effects in spectral likelihoods. By hierarchically combining the inferred abundances, we limit the spread of the following elements in M67: Fe ≲ 0.02 dex; C ≲ 0.03 dex; O, Mg, Si, Ni ≲ 0.04 dex; Ca ≲ 0.05 dex; N, Al ≲ 0.07 dex (at 68% confidence). Our constraints suggest a lack of self-pollution by core-collapse supernovae in M67, which has promising implications for the future of chemical tagging to understand the star formation history and dynamical evolution of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗