Results for “baseline characterization” — Page 64

NASA NTRSDate not supplied

Effects of Mild Hypobaric Hypoxia on Visual Field Impairment

INTRODUCTION: The primary objective of the Exploration Atmosphere (EA) study is to validate a new prebreathe protocol necessary before exposure to a suited hypobaric environment during extravehicular activity (EVA) from a habitat ‘exploration atmosphere’ of 56.5kPa (8.2 psia), 34% O2, 66% N2. Prebreathe protocols must be time and resource efficient while also controlling Decompression Sickness (DCS) risk to within acceptable limits. The habitat hypobaric exploration atmosphere also results in a mildly hypoxic environment (piO2 = 128mmHg). As a secondary objective of the EA study, we characterized the effects of 11-day exposure to a mild hypobaric hypoxic environment on visual performance. METHODS: Two, 11-day hypobaric chamber tests were performed (EA-1and EA-2, n=8 each) in NASA’s 20-foot chamber at Johnson Space Center where subjects lived in the exploration atmosphere. Subjects also underwent simulated 6-hour EVAs at 85% O2 and 29.6 kPa during EVA on days 3, 5, 7, 9, and 11. Visual acuity (VA), a measure of spatial resolution, and contrast sensitivity (CS), a measure of ability to distinguish ever finer increments of brightness, were assessed on non-EVA days by using gapped Landolt C testing. The luminance was controlled by using a booth, a light monitor, and a lighting rheostat. EA-1 data had revealed problems in lighting control impacting consistency of the data. Procedures were subsequently updated for the EA-2 test. RESULTS: EA-1 data revealed large variance and data recording errors and was removed from the analysis. One participant left the study at Test Day 3 during EA-2. EA-2 ANOVA results showed CS (mean change=0.010 logCSWeber) and VA (mean change, -0.016 logMAR) between pre-test and 11-day test phases; however, neither VA nor CS changes were statistically significant. There were non-statistically significant declines in VA across test phases. DISCUSSION: Overall, visual field performance did not exhibit clinically significant changes (3 lines or greater change in LogMar chart) during exposure to the mild hypoxic exploration atmosphere environment compared to pre-test baseline. The consistency and stability of VA data during EA-2 suggests that the mild hypobaric hypoxic environment did not cause clinically significant negative impacts to participants’ visual field performance.

Visual Acuity↗

DOE OSTI2022

A reduced model for the ITER divertor based on SOLPS solutions for ITER Q = 10 baseline conditions: B. A reduced model based on reversed-direction two point modeling

Edge codes such as SOLPS coupled to neutral codes such as EIRENE have become so comprehensive and sophisticated that they now constitute, in effect, 'code-experiments' that, as for actual experiments, can benefit from interpretation using simple models and conceptual frameworks, i.e. reduced models. The first task is the identification of options for the reduced model control parameters that are best suited for control of the action of the divertor, i.e. for control of target power loading and sputter–erosion, primarily. A strong correlation between the electron temperature at the divertor target, T e,t , and the neutral deuterium D 2 density at the target, n D2,t , flux-tube resolved, has recently been reported for a number of code studies including SOLPS-4.3 modeling of a set of ~50 ITER baseline cases: Q DT = 10, q 95 = 3, P SOL = 100 MW, metallic walls, and Ne seeding (Pitts et al 2019). Part A of the present study reports new results for largely the same ITER cases, confirming the strong correlation reported earlier between local values of T e,t , and (i) n D2,t , and (ii) normalized volumetric losses of power and pressure in the divertor. Strong correlations have now also been found, and are reported here for the first time, between T e,t and all of the divertor target quantities of practical interest. A physical explanation for this surprising result has not as yet been fully identified; nevertheless it has encouraging implications for reduced modeling of the ITER divertor. For such ITER conditions, (i) the global Ne injection rate, Inj Ne (Ne s –1 ), and (ii) the electron temperature at the location on the target where the peak power deposition occurs, T e,t @q ⊥,pk (eV), are found to be promising reduced model control parameters. In this part B, a reduced model for the ITER divertor is developed and described in detail, based on reversed-direction two point modeling, Rev2PM. The input to the reduced model is a value of the variable pair (T e,t @q ⊥,pk , Inj Ne )for a chosen case and the output are values of the various target as well as divertor-entrance quantities of practical interest, e.g. q ⊥,pk , the electron density at the X-point, n e,Xpt , etc. The reduced model was quantitatively characterized using one half of the code cases; it was then used to successfully predict (replicate) the code values of e.g. n e,Xpt for the other half.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRSDate not supplied

Lessons Learned From Designing and Building Low Organic Outgassing Cleanrooms for NASA OSIRIS-REx and Hayabusa2 Curation Facility

NASA Johnson Space Center Astromaterials Acquisition and Curation Office completed construction and commissioning of the OSIRIS-REx and Hayabusa2 cleanroom laboratory suites along with new precision cleaning and advanced curation laboratories consisting of ISO Class 5, 6, and 7 cleanrooms. The new curation facilities are designed for initial receiving, basic characterization, curation processing, and preliminary examination of carbonaceous asteroidal material. The facilities are also designed to enable long-term pristine sample storage to preserve the scientific integrity of each sample to enable future research by the international science community. The scientific study of organics is critical for both missions. The OSIRIS-REx mission instituted a stringent contamination control plan for low organics. Given these mission requirements and long-term storage preservation requirements, cleanroom construction materials were carefully selected to not hinder the scientific search for amino acids and the study of organics in the samples. Cleanroom candidate construction materials were researched for the HVAC system, floors, walls, ceiling, and plenum areas. The team chose several candidate materials that were further tested for their specific outgassing characteristics using ASTM E-595 per ASTM E-2312. The information gained provided the foundation for the final design selection of the cleanroom materials. While material selection is important before and during the facility design phase, the construction phase is where these choices are implemented by the prime construction contractor through final product and material submittals. These submittals are the last check point and changes are often submitted due to engineering/construction conflicts, especially when renovating an existing building. Any new material or equipment/product change must be identified and scrutinized for low particulate shedding and outgassing properties. In some cases, quick testing of new material is required, and compromises must be made in real-time. Careful selection and implementation of cleanroom materials significantly reduced organic and inorganic contamination beyond normal cleanroom baselines.

astromaterials curation↗

NASA NTRSDate not supplied

Lessons Learned From Designing and Building Low Organic Outgassing Cleanrooms for NASA OSIRIS-REx and Hayabusa2 Curation Facility

NASA Johnson Space Center Astromaterials Acquisition and Curation Office completed construction and commissioning of the OSIRIS-REx and Hayabusa2 cleanroom laboratory suites along with new precision cleaning and advanced curation laboratories consisting of ISO Class 5, 6, and 7 cleanrooms. The new curation facilities are designed for initial receiving, basic characterization, curation processing, and preliminary examination of carbonaceous asteroidal material. The facilities are also designed to enable long-term pristine sample storage to preserve the scientific integrity of each sample to enable future research by the international science community. The scientific study of organics is critical for both missions. The OSIRIS-REx mission instituted a stringent contamination control plan for low organics. Given these mission requirements and long-term storage preservation requirements, cleanroom construction materials were carefully selected to not hinder the scientific search for amino acids and the study of organics in the samples. Cleanroom candidate construction materials were researched for the HVAC system, floors, walls, ceiling, and plenum areas. The team chose several candidate materials that were further tested for their specific outgassing characteristics using ASTM E-595 per ASTM E-2312. The information gained provided the foundation for the final design selection of the cleanroom materials. While material selection is important before and during the facility design phase, the construction phase is where these choices are implemented by the prime construction contractor through final product and material submittals. These submittals are the last check point and changes are often submitted due to engineering/construction conflicts, especially when renovating an existing building. Any new material or equipment/product change must be identified and scrutinized for low particulate shedding and outgassing properties. In some cases, quick testing of new material is required, and compromises must be made in real-time. Careful selection and implementation of cleanroom materials significantly reduced organic and inorganic contamination beyond normal cleanroom baselines.

astromaterials curation↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗

NASA NTRS2019

Understanding Martian Alteration Processes by Comparing In-Situ Chemical Measurements from Multiple Landing Sites

Characterizing the history of aqueous activity at the martian surface has been an objective of the Mars Exploration Rovers (MER) and the Mars Science Laboratory (MSL). Although the geologic context of the three landing sites are different, comparisons across the datasets can provide greater insight than using data from one mission alone. The Alpha Particle X-ray Spectrometer (APXS) is common to all three rovers (Spirit at Gusev crater, Opportunity at Meridiani Planum, and Curiosity at Gale crater) and provides a consistent basis for these comparisons. Soil and Dust: Fine grained basaltic soils and dust are remarkably uniform in chemical composition across multiple landing sites. These similarities in the concentrations of major, minor, and a few trace elements (Fig. 1) are indicative of planet-wide consistency in the composition of source materials for the soils. S and Cl vary by a factor of two in the soil and dust, but there is no clear association with any bulk cation (e.g., no correlation between S and total Ca, Mg, or Fe in soils). These volatile elements, however, are clearly associated with the nanophase-ferric iron component in the soil established by Mössbauer spectroscopy [1,2]. S and Cl likely originated as acidic species from volcanic out-gassing and subsequently coalesced on dust and sand grain surfaces, possibly with an affinity towards Fe3+ sites. Importantly, given the mobility of S and Cl in aqueous exposures, soil samples maintaining the typical molar S/Cl ratio of ~3.7:1 indicate minimal interactions with liquid water after the addition of S and Cl. In contrast to this well-established baseline, soil samples have been discovered at all three landing sites with atypical S/Cl ratios (e.g., subsurface soils), indicative of a more complex aqueous history.

Yen, A. S.↗

NASA NTRS2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Jianxiu Qiu↗

DOE OSTI2025

Measurement of $\nu_\mu$ CC Interactions With Two-Proton Final State in MINERvA

This dissertation presents a measurement of charged–current (CC) muon–neutrino interactions with exactly two protons and no pions in the final state (CC~$2p\,0\pi$), using data collected by the MINERvA detector in the NuMI medium–energy beam at Fermilab. Such two–proton topologies are a sensitive probe of nuclear dynamics in the few–GeV regime, including multi–nucleon correlations (npnh, notably $2p2h$) and intranuclear final–state interactions (FSI) such as pion absorption and nucleon rescattering. A precise experimental characterization of these processes is essential both for neutrino–interaction theory and for reducing systematic uncertainties in oscillation experiments that rely on accurate modeling of neutrino–nucleus interactions. Events are selected by requiring a $\nu_\mu$ CC interaction with a reconstructed $\mu^-$ and two proton tracks originating from a common vertex in MINERvA’s finely segmented scintillator tracker, with no reconstructed mesons. Muon charge and momentum are constrained by matching to the MINOS Near Detector, while proton identification exploits energy–loss profiles and stopping–proton features. Backgrounds from pion–producing channels that enter the signal region through FSI or reconstruction effects are constrained with data–driven sidebands (Michel–electron and isolated–cluster “blob” samples) and tuned via a simultaneous fit across signal and sideband regions. To correct detector resolution and acceptance effects, the analysis employs iterative Bayesian unfolding with extensive validation: statistical pseudo–experiments, and robustness checks against generator systematic “universes” and additional strong shape warps. Single–differential cross sections are reported for three observables tailored to the two–proton final state: the opening–angle cosine $\cos\!\left(\theta_{pp}\right)$, the leading–proton momentum, and the subleading–proton momentum. Systematic uncertainties include contributions from neutrino flux, interaction modeling (e.g., npnh and resonance parameters, pion FSI), and detector response (calibration, reconstruction efficiencies). The resulting distributions provide targeted constraints on the interplay of multi–nucleon dynamics and FSI that shape CC~$2p\,0\pi$ final states on hydrocarbon. Comparisons to modern GENIE–based simulations highlight kinematic regions where model components require refinement. These measurements thus inform generator tuning and improve the reliability of neutrino–energy reconstruction strategies for current and future long–baseline oscillation programs.

Syrotenko, Vladyslav S. [Tufts U.]↗

NASA NTRS2000

Economic Metrics for Commercial Reusable Space Transportation Systems

The success of any effort depends upon the effective initial definition of its purpose, in terms of the needs to be satisfied and the goals to be fulfilled. If the desired product is "A System" that is well-characterized, these high-level need and goal statements can be transformed into system requirements by traditional systems engineering techniques. The satisfaction of well-designed requirements can be tracked by fairly straightforward cost, schedule, and technical performance metrics. Unfortunately, some types of efforts, including those that NASA terms "Programs," tend to resist application of traditional systems engineering practices. In the NASA hierarchy of efforts, a "Program" is often an ongoing effort with broad, high-level goals and objectives. A NASA "project" is a finite effort, in terms of budget and schedule, that usually produces or involves one System. Programs usually contain more than one project and thus more than one System. Special care must be taken in the formulation of NASA Programs and their projects, to ensure that lower-level project requirements are traceable to top-level Program goals, feasible with the given cost and schedule constraints, and measurable against top-level goals. NASA Programs and projects are tasked to identify the advancement of technology as an explicit goal, which introduces more complicating factors. The justification for funding of technology development may be based on the technology's applicability to more than one System, Systems outside that Program or even external to NASA. Application of systems engineering to broad-based technology development, leading to effective measurement of the benefits, can be valid, but it requires that potential beneficiary Systems be organized into a hierarchical structure, creating a "system of Systems." In addition, these Systems evolve with the successful application of the technology, which creates the necessity for evolution of the benefit metrics to reflect the changing baseline. Still, economic metrics for technology development in these Programs and projects remain fairly straightforward, being based on reductions in acquisition and operating costs of the Systems. One of the most challenging requirements that NASA levies on its Programs is to plan for the commercialization of the developed technology. Some NASA Programs are created for the express purpose of developing technology for a particular industrial sector, such as aviation or space transportation, in financial partnership with that sector. With industrial investment, another set of goals, constraints and expectations are levied on the technology program. Economic benefit metrics then expand beyond cost and cost savings to include the marketability, profit, and investment return requirements of the private sector. Commercial investment criteria include low risk, potential for high return, and strategic alignment with existing product lines. These corporate criteria derive from top-level strategic plans and investment goals, which rank high among the most proprietary types of information in any business. As a result, top-level economic goals and objectives that industry partners bring to cooperative programs cannot usually be brought into technical processes, such as systems engineering, that are worked collaboratively between Industry and Government. In spite of these handicaps, the top-level economic goals and objectives of a joint technology program can be crafted in such a way that they accurately reflect the fiscal benefits from both Industry and Government perspectives. Valid economic metrics can then be designed that can track progress toward these goals and objectives, while maintaining the confidentiality necessary for the competitive process.

Shaw, Eric J.↗

NASA NTRS2022

Changes in Anthropogenic Precursor Emissions Drive Shifts in the Ozone Seasonal Cycle Throughout the Northern Midlatitude Troposphere

Simulations by six Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models indicate that the seasonal cycle of baseline tropospheric ozone at northern midlatitudes has been shifting since the mid-20th century. Beginning in ∼ 1940, the magnitude of the seasonal cycle increased by ∼10 ppb (measured from seasonal minimum to maximum), and the seasonal maximum shifted to later in the year by about 3 weeks. This shift maximized in the mid-1980s, followed by a reversal – the seasonal cycle decreased in amplitude and the maximum shifted back to earlier in the year. Similar changes are seen in measurements collected from the 1970s to the present. The timing of the seasonal cycle changes is generally concurrent with the rise and fall of anthropogenic emissions that followed industrialization and the subsequent implementation of air quality emission controls. A quantitative comparison of the temporal changes in the ozone seasonal cycle at sites in both Europe and North America with the temporal changes in ozone precursor emissions across the northern midlatitudes found a high degree of similarity between these two temporal patterns. We hypothesize that changing precursor emissions are responsible for the shift in the ozone seasonal cycle; this is supported by the absence of such seasonal shifts in southern midlatitudes where anthropogenic emissions are much smaller. We also suggest a mechanism by which changing emissions drive the changing seasonal cycle: increasing emissions of NOx allow summertime photochemical production of ozone to become more important than ozone transported from the stratosphere, and increasing volatile organic compounds (VOCs) lead to progressively greater photochemical ozone production in the summer months, thereby increasing the amplitude of the seasonal ozone cycle. Decreasing emissions of both precursor classes then reverse these changes. The quantitative parameter values that characterize the seasonal shifts provide useful benchmarks for evaluating model simulations, both against observations and between models.

Ozone↗

DOE OSTI2021

High Throughput In-Line Coating Metrology Development for Solid Oxide Fuel Cell Manufacturing

Coatings play key roles in solid oxide fuel cell (SOFC) stack durability. For example, diffusion barrier coatings on Cr-containing interconnect and balance of plant (BOP) components protect electrodes from Cr poisoning over the long operational lifetimes (>10,000 hours) of the fuel cell stack. Common defects in coatings, such as cracks, pinholes, and porosity, result in a failure to protect the electrodes, resulting in shorter operational lifetime and thus higher cost. It is very unlikely, even in the best coating process, that all these defects can be mitigated, hence identifying critical defects in parts, and removing defective parts from production before they can damage the stack, becomes paramount. Furthermore, these quality control techniques must be operational in the production/assembly line (in-line), i.e., high throughput and non-destructive, and cost effective. Redox Power Systems, LLC (Redox) together with the National Renewable Energy Laboratory (NREL) developed much needed high throughput, in-line metrology techniques for protective coatings. The overall goal of the project is to lower cost while increasing robustness, reliability, and endurance of SOFC stacks. To accomplish this, we had several objectives, including: to identify key coating and substrate defects that lead to coating failure through the use of detailed characterization methods (e.g., microscopy, XRD, EDS, electrochemistry); to assess capabilities of in-line metrology techniques, e.g., optical profilometry (Redox) and thermography (NREL), to probe these defects, or evidence thereof; demonstrate long-term performance of “defect-free” protective coatings, as identified by in-line metrology, in solid oxide fuel cell (SOFC) stack operation. In the first part of this project, the ability to identify key defects expected to lead to coating and SOFC degradation using in-line metrology tools were evaluated. Coated interconnect samples with controlled defect types and populations were tested under conditions similar to SOFC operation, followed by detailed post-test analysis to reveal the defects responsible for observed degradation. In the second part of the project, the optimal in-line metrology techniques and methodologies were used to map the defect distribution in full-size interconnects with critical defects intentionally allowed to exist in some cases. These interconnects underwent SOFC testing for extended periods (up to ~3,000 hours) followed by post-test analysis to evaluate the effectiveness of in-line metrology techniques in mitigating MCO coating related degradation. Key accomplishments in this project included the following: Demonstrated ASR of < 0.05 ohm-cm 2 at 650 °C for 1,000 hours with low defect (determined by in-line metrology) interconnect samples (average ASR=37 milliohms-cm 2 after over 1,000 hours). Demonstrated that low defect coatings on interconnects (as screened using in-line metrology) have low volatilization of chromium at ~650 °C for 1,000 hours as detected using Cr-getter material (< 5 at% increase above baseline); 1022 hour duration tests under humidified, elevated temperature (750 °C rather than 650 °C) compared a base case against different coating thicknesses. Demonstrated capability to identify initial key defects of interest with in-line metrology techniques using up to 8 cm by 10 cm having coatings with and without intentional defects of interest using thermal imaging and optical profilometry. Correlated key defects identified using metrology techniques with observed coating performance (e.g., ASR and Cr volatility). Conducted several 4 cm by 4 cm cell tests using MCO-interconnects that were pre-screened using some of the metrology techniques developed in the project (e.g., optical profilometry). An analysis of ASR measurements were able to show that defect-free coatings resulted in the anticipated performance in the cell tests.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Autonomous Hydrogen Fueling Station

This project “Autonomous Hydrogen Fueling Station” covered the autonomous refueling with both gaseous hydrogen and liquid hydrogen. The part on gaseous hydrogen focused on the development of an autonomous robotic fueling arm that would couple to a fuel cell engine for hydrogen refueling without guidance from the forklift operator and budget period. Research was also covered for the robotic fueling with a commercial vehicle. The second phase of the project created the baseline for an autonomous liquid hydrogen transfer system that would minimize boil off losses by operating at thermodynamically efficient state points. For the development of the robotic fueling arm, testing was conducted to establish a baseline measurement of the accuracy and repeatability of a human operator positioning a lift truck in front of a dispenser. The goal was to establish the range of motion required for an autonomous fueling mechanism to mate a hydrogen nozzle with a receptacle on a fuel cell system installed in a forklift. The final design comprised a selective compliance articulated robot arm (SCARA)-type mechanism with two arms for horizontal motion and a ball screw for vertical movement and color and LIDAR cameras were used for marker identification and proximity awareness to guide the robotic arm to its target receptacle. Initial tests resulted in 199 out of 200 successful attempts at autonomous coupling of the dispensing coupler and a fuel cell engine, without hydrogen. The dispenser prototype was modified to include tubing for both hydrogen fuel and air purge lines, but subsequent tests were confounded by the shoulder motor over current errors which limited the robot from getting to the fully inserted position to achieve a positive latch. Robotic hydrogen refueling was successfully demonstrated over 1.5 hours of testing, Plug completed 29 successful latches with an average number of 4 sequential latches before failure. However, a robot capable of placing the nozzle with more force is required for higher reliability. For budget period two, a small scale (10 kg / transfer) automated control system was designed that would operate valves to control pressure and flow of liquid nitrogen between a source and receiving tank with an aim to minimize boil off losses by operating at the most thermodynamically efficient state points. Control system logic flow and a P&ID were developed prior to system safety characterization via HAZOP. A control narrative and system state points were defined. Delays in approval for a change of project objective and procurement issues precluded the construction and test of the final prototype system.

08 HYDROGEN↗

NASA NTRSDate not supplied

Pilot Assessment of Immune Dysregulation, Stress and Latent Herpesvirus Reactivation at Palmer, Antarctica - Platform for validation of Immune Countermeasures?

Recent studieshave characterized adverse health events potentially relatedto immune system dysregulation, latent herpesvirus reactivation, and clinical incidence for crewmembers onboard ISS. Both areview article describing potential spaceflight countermeasures related to immunityand a specific countermeasures protocol for deep space missions were recently published. An appropriate ground analogto enable spaceflight countermeasures has yet to be validated, although winterover in Antarctica (AWO) seems a highly relevant mission parallel to spaceflight. AWO consists of prolonged deployment, extreme environment, circadian misalignment, personal isolation, station lifestyle (varies by base), and personal risk. Through several studies, AWO at several European bases has beencharacterized, and to date the data has revealed that (immunologically) deployment to interior bases at elevation and with persistent hypobaric hypoxia possesses certaindissimilarities to spaceflight. This proposal seeks to collect low cost pilot data assessing stress, immunity and viral reactivation during AWOat thecoastalU.S. Palmer Station. Even among coastal bases, lifestyle, available crew time/workload and logistical access can vary considerably. Considering all factors, if validated, Palmer may be the most feasible location to evaluate NASA countermeasures. The goal of this pilot study is to ascertain if Palmer may serve as a spaceflight analog option for ground validation of immune countermeasures.The study initiatedwith the crew deployed for winterover at Palmer in 2020.A second crew participated in the recently concluded WO2021 season.All participatingcrew havecollectedand preservedsaliva, plasma and hair samples. On location, the crewmembers alsoperformeda fingerstick blood collection for immediate analysis of basic peripheral leukocyte subsets. Preserved biosamples have beenreturned for analysis, with the WO2020 samples received, and the WO2021 samples currently en-route to Houston. It should be noted that COVID-19 had significant impacts on operations, including training and the collection of pre-mission baseline samples. Preliminary data from the 2020 crewmembers indicate that a relatively mild but detectable immune dysregulation persists at Palmer Station, including alterations in concentration of some plasma cytokines and consistent increases in the incidence of EBV reactivation. Final study data will be tabulated upon receipt of the 2021 crew samples.

Stephanie Krieger↗

NASA NTRS1999

Cardiovascular Deconditioning in Humans: Alteration in Cardiovascular Regulation and Function During Simulated Microgravity

Alterations in cardiovascular regulation and function that occur during and after space flight have been reported. These alterations are manifested, for example, by reduced orthostatic tolerance upon reentry to the earth's gravity from space. However, the precise physiologic mechanisms responsible for these alterations remain to be fully elucidated. Perhaps, as a result, effective countermeasures have yet to be developed. In this project we apply a powerful, new method - cardiovascular system identification (CSI) - for the study of the effects of space flight on the cardiovascular system so that effective countermeasures can be developed. CSI involves the mathematical analysis of second-to-second fluctuations in non-invasively measured heart rate, arterial blood pressure (ABP), and instantaneous lung volume (ILV - respiratory activity) in order to characterize quantitatively the physiologic mechanisms responsible for the couplings between these signals. Through the characterization of all the physiologic mechanisms coupling these signals, CSI provides a model of the closed-loop cardiovascular regulatory state in an individual subject. The model includes quantitative descriptions of the heart rate baroreflex, autonomic function, as well as other important physiologic mechanisms. We are in the process of incorporating beat-to-beat fluctuations of stroke volume into the CSI technique in order to quantify additional physiologic mechanisms such as those involved in control of peripheral vascular resistance and alterations in cardiac contractility. We apply CSI in conjunction with the two general protocols of the Human Studies Core project. The first protocol involves ground-based, human head down tilt bed rest to simulate microgravity and acute stressors - upright tilt, standing and bicycle exercise - to provide orthostatic and exercise challenges. The second protocol is intended to be the same as the first but with the addition of sleep deprivation to determine whether this contributes to cardiovascular alterations. In these studies, we focus on the basic physiologic mechanisms responsible for the alterations in cardiovascular regulation and function during the simulated microgravity in order to formulate hypotheses regarding what countermeasures are likely to be most effective. Compared to our original proposal, the protocol we are using has been slightly modified to lengthen the bed rest period to 16 days and streamline the data collection. These modifications provide us data on a longer bed rest period and have enabled us to increase our subject throughput. Based on review of our preliminary data we have decided to test a countermeasure which is applied the very end of the bed rest period. We will use the same bed rest protocol to test this countermeasure. We anticipate completing the baseline data collection in our first protocol plus testing of the countermeasure in an additional eight subjects, at which time we plan to initiate the second protocol which includes sleep deprivation. In future studies, we plan to apply CSI to test other potential countermeasures in conjunction with the same bed rest, sleep deprivation and acute stressor models. We also anticipate applying CSI for studying astronauts before and after space flight and ultimately, during space flight. The application of CSI is providing information relevant to the development and evaluation of effective countermeasures allowing humans to adapt appropriately upon re-exposure to a gravity field, and to live and work for longer periods of time in microgravity.

Cohen, Richard↗

NASA NTRSDate not supplied

Initial On-Orbit Spectral Calibration of the PACE Ocean Color Instrument

The NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission Project Science Team has used Ocean Color Instrument (OCI) measurements of Fraunhofer lines in spectra of sunlight reflected by the solar diffuser and measurements of atmospheric absorption bands in cloudtop and ocean spectra to characterize the spectral calibration of OCI on orbit. Multiple lines have been analyzed for both the ultraviolet to visible (UVVIS, 340−607 nm) and visible to near-infrared (VISNIR, 597−897 nm) grating spectrographs. The spectrographs yield hyperspectral observations with 5 nm bandwidths and 0.625 nm sampling intervals. The on-orbit observations have been compared with the prelaunch spectral calibration of OCI performed by the Goddard Laser for Absolute Measurement of Radiance (GLAMR) during thermal vacuum testing to track any changes in the calibration since launch. The calibration analyzed the line positions and strengths for the Fraunhofer lines for each spectrograph by comparing the solar spectra measured by OCI with predicted solar spectra derived from the solar reference spectrum and the BRDF of the solar diffuser, convolved with the OCI relative spectral responses. The calibration also compared the line positions of the atmospheric absorption bands with the model transmissions used by the PACE Project. The line position comparisons show that the root mean square (RMS) spectral difference between the measured and predicted spectra is 0.15 nm, the average spectral shift is 0.062 nm, and the residual spectral dispersion over the wavelength range of the Fraunhofer lines is 0.17 nm. All three estimates of the spectral accuracy of OCI meet the instrument functional requirement of a spectral accuracy of 0.5 nm and are well within the 0.625 nm sampling interval of the data. The line strength comparisons between measured and predicted spectra are essentially the same. These results show that the spectral calibration of OCI on orbit has not drifted since the prelaunch calibration of OCI by GLAMR and that the on-orbit spectral calibration of OCI is stable over time. These results also provide a baseline for monitoring the future spectral performance of OCI on orbit.

Radiometric Calibration↗

NASA NTRSDate not supplied

Investigation of Hardware and Instrumentation to Measure Hand Grasp Activity with the Spacesuit Gloves

Introduction: During the 2022 suited injury summit, it was hypothesized that there will be concerns for hand and glove injuries for future exploration space missions, especially given the fact that the “total number of Extravehicular activity (EVA) hours and frequency” for lunar surface missions is expected to vastly increase [1]. It has been reported that the hands experienced the greatest “absolute numbers” of reported injuries and far exceeds other injuries during EVA [1, 2]. It was reported that the most fatiguing part of the surface EVA was the repetitive gripping tasks [3]. It was recommended that a “glove sub-team” be created to look at possible injury mechanism and mitigation strategies. Some of the recommendations that were suggested [1] are as follows: examine hand fatigue, utilize motion capture, examine the duration and frequency of hand movements, and identify frequent hand motions. We started assessing hardware and instrumentation to measure hand grasp activity in the pressurized glove environment. The purpose of this test was to perform a hardware evaluation for motion capture (MoCap) gloves obtained from StretchSense (Auckland, New Zealand). The specific gloves used were the Pro Fidelity and SuperSplay to determine the repeatability, reliability, feasibility, and useability inside of a pressurized gloved environment. Methods: The MoCap gloves were customized (e.g., battery/Bluetooth pack relocated to upper arm) to better suit the pressurized testing environment and protect the subject from unintentional injury (Fig. 1). Fourteen total subjects from different demographics (i.e., gender and pressurized glove experience level) participated in this test series. Testing included one session each of a baseline data collection (NASA Johnson Space Center (JSC) building 21) and a spacesuit glove box (Fig. 2 at JSC building 7 room 2027) data collection (under vacuum down to 4.3 psid), where each session lasted 3-5 hours. Controlled and reproducible tasks to systematically evaluate the repeatability and reliability of the hardware were performed during baseline data collection. Additionally, subjects performed simulated EVA-like tasks in a pressurized gloved environment. For all sessions, MoCap gloves were placed on each of the subjects’ hands and the signal from it, or the raw capacitance (Fig. 3), was analysed. The raw capacitance was used to estimate the open and closed hand states between the testing conditions and allow us to provide an offset caused by the pressurized environment. Results & Discussion: Initial observation with the bare hands (baseline) condition showed that the MoCap gloves appeared to track grasping and releasing of the fingers (opening and closing fist) with both high- and low-speed conditions, while adduction and abduction of the fingers were not relatively tracked. A hardware evaluation was done outside of the glove box to assess the reliability and repeatability of the MoCap glove. In one task, a point force was applied to various locations on the back of the hand. When the point force was applied to the space between the 1st digit and the pointer finger, there was a noticeable distortion to the MoCap data. Another task examining an increasing force from a 10 lb. sandbag applied to the back of the hand while lying flat on a table, showed a constant flat line with only a distortion when the weight was increased or added to the back of the hand. Fig. 3 shows an object relocation task where you can see when each individual finger “opened” and “closed” (changed position) when picking up and setting down the dumbbell. When the fingers were stationary, the signal remained relatively flat compared to the peaks and valleys that can be observed in Fig. 3. This study showed promising results and imperative input into an attempt to discriminate between hand states across various functional tasks and should be evaluated with context to the repeatability and reliability outcomes. Depending on the task done inside of the pressurized glove box environment and outside, the results appear to be affected by many different factors (i.e., drift, pressure, hand size, etc.). Significance: If this hardware proves to be reliable and repeatable in determining the open and closed hand states then this may provide critical insight into assisting in the characterization of the pressurized gloved environment and the effect on crew member exertion level. Ultimately, this tool will provide useful data for quantifying the repetitive nature of EVA training and tasks. Acknowledgments: The authors would like to acknowledge the NASA Mars Campaign Office for providing funding for this research. Lastly, thanks to all the engineers and technicians at NASA JSC who helped with this data collection. References: [1] Reiber, et al. (2022), NASA/TM-20220007605; [2] Scheuring, et al. (2009), Av., Sp., and Envir. Med. 80(2). [3] Scheuring, et al. (2007), NASA/TM–2007–214755.

Rachel L Thompson↗