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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB’s sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.

Magnetic Fusion Energy↗
DOE OSTI2020

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Origin and characterization of single-electron events in Skipper-CCDs for light dark matter searches

This manuscript summarizes the work carried out within the SENSEI (Sub-Electron-Noise Skipper-CCD Experimental Instrument) collaboration, an effort focused on the search for light dark matter using Skipper-CCDs. Firstly, it presents the data acquisition and processing protocol developed to establish a selection criteria for events compatible with the dark matter signal. This protocol also enabled the structure and tools used for the study and characterization of phenomena that impact the quality of measurements made in the search for dark matter (measurement of the Fano factor, determination of the Compton background, and characterization of the high-energy surface background), which were carried out during this work. Secondly, it discusses the comprehensive study conducted on the origin of single-electron events in Skipper-CCDs, which allowed for the selection of detector operating parameters and improved the detection sensitivity in the search for dark matter. It is highlighted the characterization of the spatial correlation between single-electron events and high-energy events, among other developed event selection criteria, and the optimization of the output device to reduce sources of luminescence during its operation. As a result of this study, the lowest levels of dark current and spurious charge ever measured in a CCD are reported. Finally, the latest published results from SENSEI are presented. The obtained limits for the scattering of a light mediator are the best reported to date across the entire investigated mass range, while for the heavy mediator, they are the best for masses below 10 MeV. In terms of dark matter absorption, the most restringent limits below 10 eV are reported. These results, achieved with only a very small fraction of the total projected exposure for SENSEI, position Skipper-CCDs as leaders in the search for light dark matter worldwide.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Heliophysics Environmental & Radiation Measurement Experiment Suite (HERMES): A Small External Payload for the Lunar Gateway with Big Challenges

Currently scheduled for liftoff in 2024, Gateway will be an outpost orbiting the moon for astronauts headed to and from the lunar surface and serve as a staging point for deep space exploration. In January of 2020 NASA headquarters contacted Goddard Space Flight Center to request that they develop a Heliophysics instrumentation package for Gateway. This package would later become known as HERMES-Heliophysics Environmental & Radiation Measurement Experiment Suite. HERMES consists of a Miniaturized Electron pRoton Telescope (MERIT), an Electron Electrostatic Analyzer (EEA), Solar Probe Analyzers (SPAN)-A-ions, and Noise Eliminating Magnetometer Instrument in a Small Integrated System (NEMISIS), which consists of one fluxgate and two Magneto-Inductive Magnetometers. From the beginning the HERMES mission faced a number of Challenges. It was constrained to fit in a small, half meter, cube and it was required to weigh no more than 25kg. A new boom design for the magnetometer would be required and for safety reasons it must be able to retract autonomously with power removed. To complicate matters the location of the SORI-Small ORU- (Orbital Replacement Unit) Robotics Interface, the primary interface for the HERMES platform to the Gateway elements, was undetermined. Also, the mechanical, thermal and electrical interfaces are not fully defined. The Canadian Space Agency is still in process of designing the version of the SORI that will be flown on the Power and Propulsion Element (PPE) and Habitation and Logistics Outpost (HALO) elements, each of which are being developed by different contractors. At the time of initiating the HERMES project, neither of the Gateway module providers were under contract. Additionally, we would later learn the ISS heritage SORI modules were not originally designed for launching on the Gateway elements with a payload directly attached but rather were intended to be brought up on a separate carrier outfitted with launch locks and specialized launch structures from which the robotic arm on Gateway would then be used to detach the payload and install it on the SORI adapters while on orbit. Launching the integrated Payload/SORI on the PPE and HALO elements complicates the stiffness requirements and coupled loads analysis. Adding to this are serious constraints on Field-Of-View (FOV) for solar viewing and severe radiation exposure considerations brought on by slowly raising the orbit through the Van Allen Belts. Just to make things a little more challenging the budget for the entire project was intended to be a low-cost tailored Class-D mission approach. Plus, the effects of Corona VIrus Disease 2019 (COVID-19) were not factored in from the beginning. This paper will discuss what’s being done to overcome these challenges and put HERMES on track for a 2024 Launch Readiness Date (LRD).

Irving Joseph Burt↗
NASA NTRS1997

Boron Abundances in A and B-type Stars

Boron abundances in A- and B-type stars may be a successful way to track evolutionary effects in these hot stars. The light elements - Li, Be, and B - are tracers of exposure to temperatures more moderate than those in which the H-burning CN-cycle operates. Thus, any exposure of surface stellar layers to deeper layers will affect these light element abundances. Li and Be are used in this role in investigations of evolutionary processes in cool stars, but are not observable in hotter stars. An investigation of boron, however, is possible through the B II 1362 A resonance line. We have gathered high resolution spectra from the IUE database of A- and B-type stars near 10 solar mass for which nitrogen abundances have been determined. The B II 1362 A line is blended throughout; the temperature range of this program, requiring spectrum syntheses to recover the boron abundances. For no star could we synthesize the 1362 A region using the meteoritic/solar boron abundance of log e (B) = 2.88; a lower boron abundance was necessary which may reflect evolutionary effects (e.g., mass loss or mixing near the main-sequence), the natal composition of the star forming regions, or a systematic error in the analyses (e.g., non-LTE effects). Regardless of the initial boron abundance, and despite the possibility of non-LTE effects, it seems clear that boron is severely depleted in some stars. It may be that the nitrogen and boron abundances are anticorrelated, as would be expected from mixing between the H-burning and outer stellar layers. If, as we suspect, a residue of boron is present in the A-type supergiants, we may exclude a scenario in which mixing occurs continuously between the surface and the deep layers operating the CN-cycle. Further exploitation of the B II 1362 A line as an indicator of the evolutionary status of A- and B-type stars will require a larger stellar sample to be observed with higher signal-to-noise as attainable with the Hubble Space Telescope.

Lambert, David L.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2021

Geothermal Play-Fairway Analysis of Washington State Prospects: Final Report

The Washington State Geothermal Play-Fairway Analysis overcomes the exploration challenges posed by dense vegetation, glacial deposits, and extreme precipitation. The geothermal play-fairways we target are locations where heat, permeability, and saturated porosity are present in sufficient volume to provide adequate heat exchange at depths accessible by modern drilling technology. The three study areas lie along the Cascade Range magmatic arc and are near Mount Baker, Mount St. Helens, and the Wind River Valley. The seven-year project is divided into three phases. In Phase 1 we build on a previous statewide assessment of geothermal resources and develop an initial modeling approach. The results are a series of favorability, uncertainty, and risk maps for three targeted study areas. Based on these initial results, we collect new geologic and geophysical data to further refine our modeling and reduce exploration uncertainty in Phase 2. We improve the modeling method to handle the new data and update the favorability, uncertainty, and risk maps. We also update the conceptual geothermal resource models. In Phase 3 we validate our modeling approach by drilling two temperature-gradient holes and collecting and analyzing core, image logs, and new geochemistry. Our modeling approach improves on an earlier statewide method through a more-rigorous and detailed assessment of heat and permeability. Permeability potential is assessed through geomechanical modeling of the deformation that can generate and maintain reservoir porosity and permeability. Metrics to inform heat potential include temperature-gradient wells, which are sparse in Washington; proximity of Quaternary volcanic vents and young intrusive rock; spring temperature; and reservoir temperature inferred from geothermometry. We weight the individual components using an expert-guided approach known as the Analytical Hierarchy Process. During Phase 2 we also develop a fluid-filled fracture model, and an infrastructure model that helps to delineate areas which are more favorable for geothermal development based on proximity to transmission lines, elevation, land ownership and use restrictions, and availability of process water. New geologic and geophysical data is collected during Phase 2 in each of our three main study areas. At Mount Baker and north of Mount St. Helens we conduct 1:24,000-scale geologic mapping and lidar analysis to better constrain the location and character of surface faults; detailed mapping in the Wind River Valley was completed just prior to the start of this project. Ages of intrusive rocks are determined with 40 Ar/ 39 Ar geochronology, though all of our samples are Miocene or older. We collect ground based gravity observations (a total of 1,580 new stations) in all of our study areas and ground-based magnetic lines (a total of 93 km) at Mount Baker. These data are combined with existing gravity and aeromagnetic data and used to constrain fault locations and geometry. Two to three cross sections are constructed at each study area using the mapped surface geology and forward-modeling of the gravity and magnetic data; these cross sections form the basis for our updated conceptual models. We collect magnetotelluric surveys at Mount Baker and Mount St. Helens and these data are inverted to form a resistivity model from the surface to about 10 km depth; each model shows conductive zones that can be interpreted as upwelling geothermal fluids. At Mount St. Helens we deploy a passive seismic array and use the newly detected events to refine the location of the Saint Helens seismic zone. We also employ ambient-noise tomography to develop a detailed seismic-velocity model for the study area and use this model to help constrain our cross sections and conceptual model. Based on the new data collected during Phase 2—and our updated models—we develop a campaign of temperature-gradient holes and core analysis to validate our modeling in Phase 3. Drill hole MB76-31 is located near Little Park Creek, 11 km west-southwest of the summit of Mount Baker, and is 1,471 ft deep. About 410 ft of core from the lower portion of the hole—and image logs from ~175 ft below ground surface to the bottom—are collected and analyzed. Water samples are collected and processed for geothermometry. Drill hole MSH17-24 is located along upper Schultz Creek, 16 km north-northeast of Mount St. Helens and has core from 470 ft to the bottom at 1,053 ft. We did not collect image logs due to borehole stability concerns, but water samples are collected and analyzed for geothermometry. Repeat temperature-gradient measurements are made at both sites and thermal conductivity is measured from core samples. At MB76-31, the equilibrated temperature gradient of 64°C/km and calculated heat flow of 141–159 mW/m 2 is more than twice the regional average. Detailed mapping and analysis of the core, coupled with correlation to the image logs, indicates a history of permeability generation consistent with our predictions of high permeability. Because the site has high favorability in the Phase 2 model, we consider the results a positive validation of the modeling. At site MSH17-24, the equilibrated temperature gradient of ~15°C/km and calculated heat flow of 41–43 mW/m 2 are similar to regional. Geochemical analysis of the water samples indicates a meteoric source without any geothermal component. Detailed outcrop-based mapping of fault exposures near the drill site and analysis of image logs from nearby boreholes indicates a history of permeability generation consistent with our predictions. Because the site has low favorability in the Phase 2 model, we consider the results a positive validation of the modeling. Together, the two sites provide a reasonably positive validation of the Phase 2 modeling and should encourage future use of this modeling approach.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS2000

TPS Materials and Costs for Future Reusable Launch Vehicles

There is considerable interest in developing new reusable launch vehicles (RLVs) for reducing the cost of transporting payload to and from orbit. This work reviews thirteen candidate thermal protection system (TPS) options currently available for RLVs. It is useful to begin with the current Shuttle TPS layout as a reference. The nose cap and wing leading edge , which reach the highest temperatures, are made of reinforced carbon-carbon (RCC) that is protected from oxidation by an external coating (about 0.020" thick) of silicon-carbide. Most of the windward surface is 9 lb/cubic ft ceramic tiles (LI-900) with a thin (about 0.012") coating of Reaction Cured Glass (RCG). The leeward side of the vehicle is covered largely by AFRSI, a quilted ceramic blanket, and FRSI, a polyamide felt. These four materials can be considered first generation reusable TPS. Since the time of the Shuttle design, considerable progress has been made advancing TPS technologies in terms of thermal performance, robustness, and cost. For each of the major systems, a second generation ceramic TPS has been developed, tested, and characterized. Metallic-based systems have also been developed. For applications requiring RCC in the past, advanced carbon-carbon (ACC) is now available. This material has better mechanical properties, somewhat higher temperature capability to 2900F and greatly increased oxidation resistance. New carbon fiber reinforced silicon-carbide matrix composites (C/SiCs) have shown additional improvement in properties over ACC with use temperatures to 3000F and above. For rigid tiles, NASA Ames has made two significant advancements. The first is a tile substrate called Alumina Enhanced Thermal Barrier, or AETB, that incorporates alumina fibers for improved dimensional stability at high temperatures, to 2600F and above. This material can be made to densities as low as 8 lb/cubic ft. The second is a coating preparation called Toughened Uni-piece Fibrous Insulation, or TUFT, that penetrates about 0.1 in. into the tile substrate. The resulting composite, with a functionally gradient density near the surface, provides orders of magnitude increased damage resistance compared with RCG coated LI-900, with only a small weight increase. The TPS that combines these two developments is called AETB-8/TUFI and has been adopted for high damage areas on the Shuttles. Two notable developments have occur-red in flexible ceramic blanket technology. The first is aluminoborosilicate-based fibers with use temperatures of 2200F and above," in comparison to quartz and silica fiber used in AFRSI which have multi-use temperature limits of 1200 to 1400F. Blankets incorporating these new high temperature fibers are referred to as AFRSI-HT. The second is an integral weaving techniques that produces a fluted core blanket with a smoother surface and greater resistance to aero-acoustic noise, to levels as high as 170 dB. This Ames innovation is called Tailorable Advanced Blanket Insulation, or TABI. Finally, for felt-based TPS, Boeing is developing Polybenzimidazole Blanket Insulation, or PBI, with a multi-use temperature limit of 1000F and above, in contrast to Shuttle FRSI which has a multi-use temperature limit of about 700F. 1.6 NASA Langley and BF Goodrich (formerly Rohr Corp.) have led the development of metallic-based TPS. This activity uses essentially three approaches: metallic tiles which encase a fibrous ceramic batting in a box fabricated largely from metallic honeycombs, typically Nickel based alloys; metallic honeycomb sheets, made of Nickel-based alloys, incorporating a fibrous back-side insulation encapsulated in a metallic foil bag, providing reduced weight; and metallic multi-wall, which is comprised of dimpled Titanium metal sheets, which are stacked and then diffusion bonded at contact points to form the TPS. The Nickel-based systems can be used up to temperatures of about 1800F, and the Titanium system to about 100F. These thirteen TPS materials have pros and cons to their usage in terms of temperature capability, weight, initial cost, and maintenance. Carbon-carbon and C/SiC systems have the highest temperature capability but are relatively expensive and heavy, requiring significant time, expertise, and costly facilities and tools for design and fabrication. Second generation ceramic tiles are relatively light, durable, simple to fabricate and easy to install; however, waterproofing is a concern. Blankets and felts are light, simple, inexpensive, and easy to install over curved vehicle surfaces, but durability and waterproofing are concerns. Metallics are robust and appear to have eliminated waterproofing, but they tend to be heavy and relatively expensive, requiring costly facilities and tools. If thin metal sheets are used to reduce weights, then issues arise from possible metal fatigue and corrosion caused by thermal cycling, pressure oscillations, and environmental exposure. For application to future RLVs, system analyses show that a significant component of the vehicle life cycle cost is from the TPS; however, it is difficult to quantify and to compare the potential savings of advanced systems without performing full vehicle designs using each of the different options. Because this entails a considerable effort and also tends to submerge TPS cost impacts under unrelated vehicle design assumptions, there is a clear need for a simpler quantitative method to evaluate the cost impact of different TPS options. To this end, this work introduces a TPS life-cycle cost parameter which is easily computed and applicable to generic RLVs.

Rasky, Dan J.↗
DOE OSTI2021

Reducing Ground-based Astrometric Errors with Gaia and Gaussian Processes

Stochastic field distortions caused by atmospheric turbulence are a fundamental limitation to the astrometric accuracy of ground-based imaging. This distortion field is measurable at the locations of stars with accurate positions provided by the Gaia DR2 catalog; we develop the use of Gaussian process regression (GPR) to interpolate the distortion field to arbitrary locations in each exposure. We introduce an extension to standard GPR techniques that exploits the knowledge that the 2D distortion field is curl-free. Applied to several hundred 90 s exposures from the Dark Energy Survey as a test bed, we find that the GPR correction reduces the variance of the turbulent astrometric distortions ≈12× , on average, with better performance in denser regions of the Gaia catalog. The rms per-coordinate distortion in the riz bands is typically ≈7 mas before any correction and ≈2 mas after application of the GPR model. The GPR astrometric corrections are validated by the observation that their use reduces, from 10 to 5 mas rms, the residuals to an orbit fit to riz-band observations over 5 yr of the r = 18.5 trans-Neptunian object Eris. We also propose a GPR method, not yet implemented, for simultaneously estimating the turbulence fields and the 5D stellar solutions in a stack of overlapping exposures, which should yield further turbulence reductions in future deep surveys.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

rHealth One Demonstration Aboard ISS: A Microfluidic Bioanalyzer Based on Sheath/Hydrodynamic Focusing Flow Cytometry

The Exploration Medical Capability (ExMC) element aims to provide astronauts with the means for their own health monitoring, diagnosis, and treatment during exploration missions. As space flight ventures further from earth, the need for autonomous medical care increases under greater constraints on size, mass, and resources. One pillar for diagnosis that crew would be separated from is laboratory analysis. Even now on ISS, blood samples must be collected and returned to earth for testing. In response, ExMC is assessing how assays for hematology, bone health, radiation exposure etc. could be addressed through commercial-off-the-shelf (COTS) and Small Business Innovation Research (SBIR) funded bioanalyzers that are miniaturizing lab technology. Since missions will reach distances where there are no timely replacements, validation on the International Space Station (ISS) is a necessary part of that assessment. In partnership with NASA Johnson Space Center (JSC) Immunology Lab and the Research Operations and Integration (ROI) element, ExMC conducted a technology demonstration on ISS of the rHEALTH ONE, a flow cytometry based bioanalyzer, to assess future devices based on this design. In flow cytometry, there are predominantly three ways to focus the cells (or particles) into a single file stream: hydrodynamic focusing, microcapillary, and acoustic focusing. The rHEALTH ONE utilizes the commercial standard, sheath-based hydrodynamic focusing. rHEALTH itself represents both a company and a suite of medical tools NASA has funded through SBIR grants towards the development of a promising diagnosis instrument for exploration missions. rHEALTH ONE is the interim version of the technology, functional as a benchtop analyzer and test bed for the next generation of rHEALTH in development. Several modifications were made to the rHEALTH ONE analyzer for operation in microgravity. For function, fluid management was key. A sheath-based analyzer uses sheath fluid to flow the sample, cleaning fluid to prevent biological contamination, and a reservoir to collect the liquid waste. The rHEALTH ONE analyzer uses bottles dependent on gravity to separate the air and liquid pathways and keep the liquids contained. It uses only 1 psig of air pressure to directly push the liquid through the device, requiring little-to-no resistance at inlet and outlet. A microtubing assembly with self-sealing luer connectors was designed – featuring 0.014 mm thick durable medical balloons to hold water inside the supply bottles – to create safe containment and easy access for the crew while maintaining the analyzer’s function. For safety, copper tape was added to the interior of the plastic housing to reduce electromagnetic interference, fluid and electrical connections were secured against vibration and leaks, and gaps were further sealed to ensure containment of the optical block and lasers. Water as the sheath and cleaning fluid and TOX 0 samples were used to reduce the biohazard risk to the crew. In May 2022, European Space Agency astronaut Samantha Cristoforetti demonstrated the rHEALTH ONE aboard ISS for its sample loading, flow cytometry, and data collection capability in microgravity. The JSC Immunology Lab provided flow cytometry expertise, designed the sample test protocol, and manufactured and benchmarked the flight samples on a ‘gold-standard’ flow cytometer. The analyzer was primed with water, purged of air, and four calibration solutions of polystyrene microparticles were tested to characterize its performance. Tightly controlled procedures and excellent execution prevented air bubble interference during air/water separation, fluid transfer, sample mixing and loading. Data showed a slight increase in signal noise on 3 of 5 channels and an anomaly of fluorophores migrating in one sample (confirmed by JSC post-flight). Flight results correctly detected the change in sample, matched the analyzer’s ground performance, and were consistent with the gold-standard’s ground results. Although areas were noted for improvement, these outcomes signified complete mission success.

R. S. Miller↗