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NASA NTRS2021

Application of a Developmental Composite Material Model to Predict the Crush Response of Two Energy Absorbers

In 2012, a consortium was formed with the goal of creating a new composite material model capable of predicting the wide range of properties, accumulated damage behavior, and the many different types of failure in composites under impact loading. The material model was developed for execution in the commercially available nonlinear, explicit transient dynamic finite element code, LS-DYNA®. This material model incorporates three sub-models: deformation, damage, and failure. In addition, the model accounts for strain rate and temperature effects and relies heavily on the input of tabulated material response data. The model is designated *MAT_COMPOSITE_TABULATED_PLASTICITY_DAMAGE, or *MAT_213. Initially, *MAT_213 was developed for use with solid elements only; however, a thin shell element formulation for *MAT_213 has been adapted recently. The objective of this project was to find a suitable modeling example to investigate the capabilities and performance of *MAT_213. In 2012, two composite energy absorbers were designed and evaluated at NASA Langley Research Center through multi-level testing and simulation. The first was a conical-shaped energy absorber, designated the conusoid, which consisted of four layers of hybrid carbon-Kevlar® plain-weave fabric oriented at [+45°/-45°/-45°/+45°] with respect to the vertical direction. The second was a sinusoidal-shaped energy absorber, designated the sinusoid, which consisted of hybrid carbon-Kevlar® plain-weave fabric face sheets, two layers for each face sheet oriented at ±45° with respect to the vertical direction, and a closed-cell ELFOAM® P200 polyisocyanurate foam core. Finite element models were developed of the energy absorbers and simulations were performed using LS-DYNA®. In this paper, the development of a *MAT_213 model of a hybrid carbon-Kevlar® plain-weave fabric is presented. Next, comparisons with material characterization tests are presented. Then, test-analysis results are documented for each energy absorber as comparisons of time-history responses, as well as predicted and experimental structural deformations and progressive damage under impact loading using the *MAT_213 material model. Since a prior *MAT_58, or *MAT_LAMINATED_COMPOSITE_FABRIC material model was used in previous simulations of the energy absorbers, comparisons are made between *MAT_58 and *MAT_213 model predictions with test data. Finally, the paper includes a comprehensive list of “lessons learned,” in which a series of parametric studies are documented that were performed to investigate specific issues related to the material model. These “lessons learned” are included in hopes that they may help future *MAT_213 users.

crashworthiness, LS-DYNA transient dynamic simulat↗
NASA NTRSDate not supplied

A Combined Al-Mg/Pb-Pb Age of the Solar System

Astrophysical models of planet formation and protoplanetary disk evolution demand precise and accurate timing of the sequence of events in the solar nebula, relative to a time t=0, usually taken to be during the short epoch of CAI (Ca-rich, Al-rich inclusion) formation. Most CAIs formed withlive26Al (mean-life τ26= 1.034 Myr [1]), with an abundance 26Al/27Al ≈ (26Al/27Al)SS= 5.23 × 10-5[2]. We adopt this as the widespread level of 26Al in the solar nebula at t=0. Assuming spatial homogeneity of 26Al, an inclusion that had less 26Al, (26Al/27Al)0, formed a time Δt26= τ26ln[(26Al/27Al)SS/ (26Al/27Al)0] after t=0.These ages are typical precise to within ±0.1 Myr. Igneous bulk meteorites and inclusions can be relatively dated by the Al-Mg chronometer, but only ifΔt26<6 Myr. The Pb-Pb system is useful as a longer relative chronometer. It yields absolute ages tPb using 207Pb/206Pb, 206Pb/204Pb, and 238U/235U ratios measured indifferent portions of a sample, assuming certain half-lives [4]. These absolute ages are uncertain to within ±9 Myr due to uncertainties in the 235U half-life[3], but times of formation ΔtPb= tCAI–tPb relative to t=0, are more precise(±0.5Myr),iftCAI can be found. Here, tCAI means the Pb-Pb age that would be measured in CAIs using the half-lives the community typically uses, if they achieved isotopic closure at t=0. Unfortunately, direct Pb-Pb dating of CAIs has not definitively determined tCAI. Based on four CAIs with canonical (26Al/27Al)0,[5,6] found tPb= 4567.30 ± 0.16 Myr. No other CAI ages with measured 238U/235U have been reported in the refereed literature, but there are hints of other CAIs with ages tPb= 4568.0 ± 0.2 Myr [7] and tPb= 4568.3 ± 0.2 Myr [8].It is unclear whether anyof these igneous type B CAIs isotopically closed at t=0 or represents tCAI. Instead of measurements, we advocate finding tCAI by minimizing the discrepancies between the Al-Mg and Pb-Pb chronometers. Assuming Δt26=ΔtPb, we find the implied t’CAI= tPb+Δt26, then define t*CAIas the weighted mean of the t’CAI. t*CAIis the best guess for the Pb-Pb age of t=0; the assumption of homogeneity is justified if the t’CAI cluster within errors around t*CAI. This statistical approach is similar to, but improves on, that of[9]. We find t*CAI= 4568.73 ± 0.16 Myr. Below we discuss our methodology and the implications of this age for CAIs, 1.4 Myr older than the reported and typically used age 4567.30±0.16 Myr. Methods: We base our estimate of t*CAIon five achondrites for which published (26Al/27Al)0and Pb-Pb ages exist: the quenched angrites D’Orbigny, Sahara 99555 (SAH 99555), and Northwest Africa (NWA) 1670; the pseudo-eucrite Asuka 881394; and the inner disk achondrite. All are “NC” (non-carbonaceous) achondrites that likely cooled quickly enough that the Al-Mg and Pb-Pb systems achieved isotopic closure simultaneously. We also considered the “CC” (carbonaceous chondrite-like) achondrites NWA 2796 and NWA 6704, butdo not include them in our fit. Al-Mg and Pb-Pb seem not to have closed simultaneously, possibly because formation in the outer disk from volatile-rich composition led to slower cooling. Of the 8 chondrules from NWA 5697 measured by [20,21], we also consider the 4 for which 238U/235U was measured: 2-C1, 5-C2, 3-C5, 11-C1.Depending on their post-formation thermal histories, the Al-Mg and Pb-Pb systems in chondrules may or may not have closed simultaneously. Table 1: (26Al/27Al)0, Pb-Pb ages of selected samplesSample(26Al/27Al)0/ 10-6RefPb-PbRefD’Orbigny3.98±0.15104563.43±0.19♮10-12SAH 995553.64±0.18104563.88±0.2712NWA16705.92±0.59104564.39±0.24*10Asuka 88139413.1±0.5613-154564.98±0.1715NWA 73253.03±0.14164563.4±2.616NWA 27963.94±0.16174562.89±0.5917NWA 67043.03±0.14184562.76±0.26192-C17.56±1.53204567.57±0.56*215-C27.04±1.51204567.54±0.52*213-C58.85±1.83204566.20±0.63*2111-C15.55±1.84204565.84±0.72*21*regression based on one subset of data points ♮weighted mean of two datasets Pb-Pb ages are proportional to the intercept of the line formed by linear regression of 207Pb/206Pb vs. 206Pb/204Pb data from various washes, leachates and residues of acid dissolution of a sample. Because contamination by terrestrial or primordial Pb is pervasive, some fractions must be excluded from regressions to ensure a fit with acceptable mean squares weighted deviation (MSWD). Usually points are excluded based on low [Pb], or low 206Pb/204Pb ratio(low radiogenic component), with single outliers identified [11,12,15,16,17]. In the starred examples (Table 1)and the case of 3 CAI Pb-Pb ages [5], up to half the points were excluded solely because did not fit a pre-determined line. This approach is vulnerable to confirmation bias and produces fits with low MSWD and too-low Pb-Pb age uncertainty. Regressing the same data points as [10], were produce the Pb-Pb age of NWA 1670 of 4564.39±0.24 Myr. But selecting other combinations of data points, other, equally valid, isochrons yield ages from 4563.77±0.21 Myr to 4564.64±0.23 Myr. Similar arguments apply to the Pb-Pb isochrons built by [21] for chondrules 2-C1 (we find 4567.33±0.44 to 4567.85±0.46 Myr), 5-C2 (4566.84±0.53 to 4567.70±0.44 Myr), 3-C5 (4565.84±0.54to 4567.04±0.54)and 11-C1 (4565.36±0.51 to 4565.74±0.45 Myr). Our adopted ages for these and NWA 1670 are listed in Table 2.Table 2. tCAI estimated from various components, using our regressions for the chondrules & NWA 1670.SampleΔt26(Myr)tPb(Myr)t’CAI(Myr)D’Orbigny5.05±0.044563.43±0.194568.48±0.19SAH 995555.14±0.054563.88±0.274569.02±0.27NWA16704.64±0.104564.21±0.634568.85±0.67Asuka 8813943.81±0.044564.98±0.174568.79±0.17NWA 73255.33±0.054563.4±2.64568.7±2.6NWA 27965.06±0.044562.89±0.594567.95±0.59NWA 67045.29±0.134562.76±0.264568.05±0.292-C12.00±0.214567.59±0.704569.59±0.725-C22.07±0.224567.23±0.914569.30±0.933-C51.84±0.214566.44±1.124568.28±1.1411-C12.32±0.344565.52±0.664567.84±0.73achondrite4568.72±0.16chondrules4568.76±0.58combined4568.73±0.16A weighted average of the five NC achondrites(or just D’Orbigny, SAH 99555 and Asuka 881394)yields t*CAI= 4568.72 ± 0.16Myr. All are consistent with this value to within 1.8σ, and MSWD=1.5. Including the 4 U-corrected chondrules, t*CAI= 4568.73± 0.16Myrwith MSWD=1.66, which is statistically significant. All chondrules and NC achondrites are consistent with this to within 1.8σ, (Figure 1).Figure 1. Al-Mg formation times after t=0 vs. Pb-Pb ages. The five NC achondrites and four chondrules are consistent with a Pb-Pb age of t=0 of 4568.7 Myr. Discussion: The data from achondrites and chondrules are consistent with a single Pb-Pb age at t=0, justifying the assumption of 26Al homogeneity. The age, 4568.7 Myr, is ≈1.4 Myr older than the commonly accepted Pb-Pb age of CAIs that formed with canonical 26Al/27Al at t=0 [3]. Others have interpreted the discrepancy to signify 26Al heterogeneity in the CAI-forming region[5,21]. We suggest instead that CAIs were exposed to transient heating events that reset the Pb-Pb system without disturbing the Al-Mg system. Notably, chondrules typically experienced transient heating at these times in the nebula [22]. If so, direct measurements of CAIs will not yield as reliable a Pb-Pb age of t=0 as statistical approaches like this and that of [9].References:[1] Auer et al. 2009. [2] Jacobsen, B et al. 2008, EPSL 272, 353-364. [3] Tissot, Fet al. 2017, GCA 213, 593-617.[4] Villa, I et al. 2016, GCA 172, 387-392.[5] Amelin, Y et al. 2010, EPSL 300, 343-350.[6] Connelly, Jet al. 2012, Science 338, 651.[7] Bouvier, Aet al. 2011, LPICo 1639, 9054. [8] Bouvier, Aand Wadhwa, M2010, Nat Geosci 3, 637-641.[9] Nyquist, Let al. 2009, GCA 73, 5115-5136.[10] Schiller et al. 2015. [11] Wadhwa & Brennecka 2012. [12] Tissot et al. 2017. [13] Nyquist et al. 2003. [14] Wadhwa et al. 2009. [15] Wimpenny et al. 2019, GCA 244, 478-501.[16] Koefoed et al. 2016, GCA 183, 31-45. [17] Bouvier, A et al. 2011, GCA 75, 5310-5323. [18] Sanborn, Met al. 2019, GCA 245, 577-596. [19] Amelin, Yet al. 2019, GCA 245, 628-642.[20] Bollard, Jet al. 2017, Sci Adv 3 ,e1700407. [21] Bollard, Jet al. 2019, GCA 260, 62-83.[22] Villeneuve, J et al. 2009, Science 325, 985

S. J. Desch↗
NASA NTRSDate not supplied

In-Lab Rapid Analytical Detection of Lunar Volatiles By Universal Gas Analyzer With Comparison to GC-MS System

Introduction: The curation of permanently shadowed regions (PSRs) [1] on the lunar surface is centered around studies based upon the observed volatiles from the LCROSS mission [2]. The rapid detection of important volatile gases and vapors present in planetary bodies and Astromaterials by a standalone analytical device is an area of intense research interest in our group and Planetary Exploration & Astromaterials Research Laboratory (PEARL) facility and this work is relevant to the future preparation of viable lunar simulants for testing curation efforts down the road. The groundbreaking results obtained from the LCROSS Mission [2] open the requirements for the direct detection of volatiles present in regolith materials collected from the lunar surface. The mass spectrometry of volatile chemicals is a general technique that utilizes a set of instruments that creates charged ions from a gaseous chemical species and measures the intensities vs. mass-to-charge ratio (m/z) [3]. In this context, we discuss in-lab experimental results and procedures for rapid qualitative analysis of main LCROSS volatiles (water, H2S, NH3, CO2, and CH3OH) by a Universal Gas Analyzer (UGA) instrument. Additionally, the instrument performance was evaluated by measuring the isotopic abundance ratio of atmospheric Ar-40 to Ar-36 present in room air since, argon is a relevant gas in planetary studies as it can provide an insight and reference point to isotope studies [4]. Additional, cross comparisons were attempted and made between the two instruments to develop a robust analytical technique by comparing mass spectral data for H2S headspace samples with a Trace-1310/ ISQ 7000 (ThermoFisher Scientific.) GC-MS system. Background: The benchtop UGA System is equipped with an SRS UGA 300 quadrupole mass spectrometer designed and built by Stanford Research Systems [5]. This system can be configured for several types of gaseous chemical analysis. The inlet line continuously samples gases at low flow rates (several milliliters per minute) through a capillary limiting the intake pressure making the instrument ideal for online analysis of select gases and/or room atmosphere. Moreover, in our current UGA system, a change in composition at the inlet can be detected in about 200 milliseconds and a complete spectrum is acquired (for a range of 1-100 amu) in under 45 sec with masses measured at rates up to 25 msec per point [5]. This system provides a quick upstream analytical data that we can then compare to results obtained by our GC-MS system. Sample Preparation: Small volume (2-4 mL) of analyte sample was taken in a 10 mL glass vial and sealed with a crimped cap and purged with pure Ar or N2 gas to displace air from the top. The headspace sample was scanned by the UGA instrument at analog, histogram, and pressure vs. time modes. The isotopic abundance ratio for 40Ar-to-36Ar was estimated by measuring partial pressure vs. time scans and setting the mass at 40 and 36 respectively. Results and Discussions: In this work, we have investigated the applicability of the UGA system by qualitative analysis of a series of LCROSS volatiles measured individually. Fig. 1 demonstrates a set of vertically offset spectra for the partial pressures measured as a function of mass-to-charge (m/z) ratios. The average acquisition time for each spectrum was less than a minute suggesting that the UGA system is ideal for quick analysis of geochemical volatiles. For the cross-comparison, we analyzed an H2S headspace sample by a Trace-1310/ISQ-7000 system and compared mass spectral data with previously measured UGA histogram scan data (Fig. 2). In both cases, major peak positions are the same, however, the intensities of fragment ions ([1H132S]+ and [32S]+) are higher for UGA suggesting that the fragment ionization process is stronger in UGA compared to that of GC-MS. To investigate how the integrated area under each chromatogram varies with the headspace sample volume, a set of five H2S headspace samples with increasing volumes was analyzed by the GC-MS system (Fig. 3, inset). A small volume (e.g., 200 to 1000 µL) of H2S/H2O vapor was withdrawn from a 20 mL stock sample vial containing ~5 mL of 0.4% H2S in water by a gas-tight syringe and added to another 20 mL vial filled with argon and analyzed by the GC-MS system. Finally, the UGA detector sensitivity was evaluated by calculating the atmospheric 40Ar-to-36Ar isotopic abundance ratio in room air by running a partial pressure vs. time scan with setting the atomic mass at 40, and 36. Fig. 4(a) shows a ~25 min duration “P vs. time” scan for 40Ar (plot for 36Ar is not shown). The partial pressure values (~100 points) were corrected by subtracting the corresponding background pressure value for 37Ar and utilized to calculate 40Ar-to-36Ar isotopic abundance ratios as shown by Fig. 4b. The average isotopic abundance ratio is ~306 with a 2*STDEV ~13. This abundance ratio is significantly close to the previously reported value of 298.56 [6] and the ratio obtained by our GC-MS system (303 for a UHP grade Ar sample). Conclusions: Our study strongly evidenced that the benchtop UGA system is a valuable analytical tool for the detection of major LCROSS volatiles. The rapid scanning capability, the inexpensiveness of the whole system, and impressive detection sensitivity prove its worthiness as an essential device for advanced geochemical applications. Moreover, cross comparisons with the GC-MS provide important bridges into advanced curatorial efforts into the future. References: [1] Bickel, V.T., et al. (2021) Nat Commun 12, 5607. [2] Colaprete, A., et al. (2010) Science, 330, 463-468. [3] Glavin, D. P. et al. (2012) 2012 IEEE Aerospace Conference, 1-11. [4] Willett, C. D., et al. (2022) Geochimica et Cosmochimica Acta 329, 119-134. [5] Operation Manual and Programming Reference. (2018) Universal gas Analyzers, Stanford Research Systems. [6] Lee, J. Y., et al. (2006) Geochimica et Cosmochimica Acta 70, 4507–4512. Notes: (4 figures are attached with text as shown by the attached file)

Curation↗
NASA NTRSDate not supplied

Trace Elemental Abundances in Calcium-Aluminum-Rich Inclusions in CV Chondrites

Introduction: Calcium-aluminum-rich inclusions (CAIs), are the first formed solids that define the age of the Solar System [1,2]. CAIs are thought to have condensed from nebular gas [3,4] within the first <1 Ma of Solar System formation [5,6]. CAIs have experienced numerous early Solar System processes including condensation, evaporation, melting, recrystallization, and aqueous alteration [e.g., 7]. The chemical, mineralogical, and textural diversity among CAIs results from a range of chemical and physical processes recorded during nebular and parent body epoch. This study aims to explore the mineralogical, textural, and chemical compositions of CAIs including the trace elemental abundances in CAI phases to determine the early Solar System processes recorded in them. Samples and Analytical Methods: We analyzed one CAI each from CV3 chondrites Northwest Africa (NWA) 5508 designated as ‘Saguaro’, and Northwest Africa (NWA) 12772 designated as ‘Hoopoe’. Back-scatter electron (BSE) images were collected using a Phenom XL scanning electron microscope (SEM) at the Lunar and Planetary Institute (LPI) and the JEOL JXA-8530F electron probe microanalyzer (EPMA) at Johnson Space Center (JSC)-NASA. Additionally, energy dispersive X-ray spectrometry (EDS) elemental maps of select areas for these samples were collected using a 15.0kV beam energy and a 40µA emission current. Using the EPMA, wavelength-dispersive X-ray spectroscopy (WDS) quantitative data were collected. In-situ trace element measurements for both CAIs were determined at JSC-NASA using a Photon Machines 193nm laser ablation system and a Thermo-Scientific Element-XR inductively coupled plasma mass spectrometer (ICP-MS). Analyses consisted of 30s ablations at 10Hz, spot sizes of 20-25µm, and a fluence of 6.0 J/cm2 for anorthite and melilite, and a 3.5 J/cm2 fluence for all other phases. NIST612 was used to correct for instrument drift, while BHVO-2g was used as a primary calibration standard. BCR-2g and in-house mineral standards were regularly measured as unknowns to ensure accuracy. Results: Saguaro is a coarse-grained CAI, ~11 x 6 mm in dimensions. Saguaro contains spinel, Al-rich pyroxene, anorthite, Mg-rich melilite, and minor perovskite in its interior and is therefore classified as a Type B CAI. Individual melilite grains shows normal compositional zoning with an Ak content ranging from ~24 to 54 with no apparent trend from the core to the edge of the CAI. The spinel appears euhedral and occurs both as clusters and as spinel palisades [8]. Two rim sequences surround most of the sample: the inner rim being a Wark-Lovering (WL) rim (~10-35 µm) containing pyroxene, spinel, and melilite (or anorthite), and the outer rim is a finer-grained, thicker (~100 µm), accretionary rim (Fig. 1). The mineral phases in Saguaro record an overall flat REE pattern with an average negative Eu anomaly in pyroxene, and an average positive Eu anomaly in anorthite and melilite respectively. Anorthite, melilite, and pyroxene have a minor depletion in Tm (Fig. 2). The Hoopoe CAI is a compact, coarse-grained ~6 × 4 mm in size. The major mineralogy includes hibonite, spinel, melilite, anorthite, and perovskite. Therefore, it is classified as a compact transitional type A and B. (?)zoning was observed in some hibonites. Individual melilite grains show both reverse and normal zoning, where the Ak content ranges from ~6- to 28. Melilite shows two distinct textures. One texture consisted of smooth melilite that appeared homogenous, while the second appeared to consist of many fine fractures. The spinel also often appears clustered. The WL-rim sequence surrounding Hoopoe is ~25 µm thick and composed of spinel, perovskite, hibonite, and melilite/anorthite. It is then partially surrounded by an outer accretionary rim (~75µm). Like before, refractory metal nuggets appeared concentrated near the WL rims. Other metal assemblages rich in Fe and Ni were also observed. All major mineral phases in Hoopoe display relatively flat REE patterns, except for varying Eu and Tm between phases (Fig. 2). There is a prominent negative Eu anomaly in perovskite and an average positive Eu anomaly in spinel, anorthite, and hibonite respectively (Fig. 2, 3). The mixed phases along the rim of the CAI also display a negative Eu anomaly, and all phases the CAI were depleted in Pb. Discussion: The CV3 CAIs analyzed in this study were classified based on their mineralogy and textures into Type A versus Type B CAIs [10]. Hibonite appears to be pseudomorphically replacing the spinel, (i.e., is hibonite in composition, but appears in the shape of spinel). Spinel palisades. The presence of spinel palisades present in Saguaro are consistent with the melting and recrystallization experienced by this CAI. Trace elemental analyses. Saguaro and Hoopoe display similar trace element patterns to each other, with both appearing generally flat, with anomalies in Eu, and Tm. Melilite and anorthite display positive Eu anomalies in both CAIs, in addition to the hibonite in Hoopoe (Fig. 2). The phases that are depleted in Eu are pyroxene and perovskite in both Saguaro and Hoopoe, respectively (Fig. 3). Given that Eu is volatile in reducing environments [11], this could possibly indicate reducing conditions at the time anorthite and melilite crystallized, with the gas they formed from containing Eu. As these CAIs continued to form, this gas as a result would become depleted in Eu. This also could be supported by the propensity of anorthite and melilite to take up Eu from its surroundings and incorporate it into their structure [12]. In addition, analyzing the assemblage of the phases in the Saguaro, melilite and anorthite (Eu enriched) often surround the pyroxene (Eu depleted) as they are crystallized. This intergrowth of phases and the proximity of the phases would support that the Eu is being incorporated into some phases, preventing it from incorporating into other. Trace elemental analyses of the CAI rims will be evaluated in more detail, as they are complicated by the transient signal being composed of a mixture of mineral phases. Broadly, however, the patterns in the rims of both CAIs are comparable to each other, and for Hoopoe, to the mixed phase patterns in the core (Fig. 3). Other studies have found that CAI rims can be depleted in Ce and Yb [13], however we did not observe these anomalies in the two CAIs discussed here. Given their similarity, the trace elemental analyses of the mixed interior (i.e. core) and rim phases could be interpreted as forming from similar, if not the same, reservoirs. The REE abundance between the rim and core of Hoopoe are also similar, indicating they may have formed from a gas of the same or similar composition. Acknowledgments: We thank the ASU Center for Meteorite Studies for loaning the samples used in this work and Tabb Prissel for his assistance with the analysis. Mouti Al-Hashimi thanks Sam Crossley and Cyrena Goodrich for their help with the LPI SEM training. This work was supported by the LPI Summer Intern Program in Planetary Science and the LPI Cooperative Agreement. References: [1] Connelly J.N. (2012) Science, 338, 651-655. [2] MacPherson G. J. (2014) Treatise on Geochem., 2, 139-179. [3] Grossman L. (1972) GCA, 36, 597-619. [4] Ebel, D.S. (2006) Meteorites and the Early Solar System II (D. S. Lauretta & H. Y. McSween, Eds.) 253-277. [5] MacPherson G. J. (2012) Earth Planet. Sci. Lett., 331-332, 43-54. [6] MacPherson G.J. (2017) GCA, 201, 65-82. [7] Krot A.N. (1995) Meteoritics & Planet. Sci., 30, 748-775. [8] Wark and Lovering (1982) GCA, 46, 2595-2607. [9] Palme H. and Jones A. (2003) Treatise on Geochemistry (H. D. Holland and K. K. Turekian Eds.), 1, 41-61. [10] Grossman L. (1980) Ann. Rev. Earth Planet. Sci., 8, 559-608. [11] Floss C. et al. (1996) GCA, 60, 1975-1997. [12] Mason B. and Martin P. M. (1974) Earth Planet. Sci. Lett., 22, 141-144. [13] Wark B. and Boynton W. V. (2001) Meteoritics & Planet. Sci., 36, 1135-1166.

X Mouti↗
NASA NTRS2023

The Temporal Brightening of Uranus’ Northern Polar Hood from HST/WFC3 & HST/STIS Observations

Hubble Space Telescope Wide-Field Camera 3 (HST/WFC3) observations spanning 2015 to 2021 confirm a brightening of Uranus' north polar hood feature with time. The vertical aerosol model of Irwin et al. (2023, https://doi.org/10.1038/s41550-023-02047-0) (IRW23), consisting of a deep haze layer based at ∼5 bar, a 1–2 bar haze layer, and an extended haze rising up from the 1–2 bar layer, was applied to retrievals on HST Space Telescope Imaging Spectrograph (STIS) (HST/STIS) observations (Sromovsky et al., 2014, 2019, https://doi.org/10.1016/j.icarus.2014.05.016, https://doi.org/10.1016/j.icarus.2018.06.026) revealing a reduction in cloud-top CH4 volume mixing ratio (VMR) (i.e., above the deep ∼5 bar haze) by an average of 0.0019 ± 0.0003 between 40–80◦N (∼10% average reduction) from 2012 to 2015. A combination of latitudinal retrievals on the HST/WFC3 and HST/STIS data sets, again employing the IRW23 model, reveal a temporal thickening of the 1–2 bar haze layer to be the main cause of the polar hood brightening, finding an average increase in integrated opacity of 1.09 ± 0.08 (∼33% increase) at 0.8 µm north of ∼45°N, concurrent with a decrease in the imaginary refractive index spectrum of the 1–2 bar haze layer north of ∼40°N and longwards of ∼0.7 µm. Small contributions to the brightening were found from a thickening of the deep aerosol layer, with an average increase in integrated opacity of 0.6 ± 0.1 (58% increase) north of 45°N between 2012 and 2015, and from the aforementioned decrease in CH4 VMR. Our results are consistent with the slowing of a stratospheric meridional circulation, exhibiting subsidence at the poles.

Arjuna James↗
NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRS2021

Multi-Variate Factorisation of Numerical Simulations

Factorisation (also known as “factor separation”) is widely used in the analysis of numerical simulations. It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system. There are many possible factorisation methods; here we discuss three previously proposed factorisations that have been applied in the field of climate modelling: the linear factorisation, the Stein and Alpert (1993) factorisation, and the Lunt et al. (2012) factorisation. We show that, when more than two variables are being considered, none of these three methods possess all four properties of “uniqueness”, “symmetry”, “completeness”, and “purity”. Here, we extend each of these factorisations so that they do possess these properties for any number of variables, resulting in three factorisations – the “linear-sum” factorisation, the “shared-interaction” factorisation, and the “scaled-residual” factorisation. We show that the linear-sum factorisation and the shared-interaction factorisation reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables. We present the results of the factorisations in the context of three past studies that used the previously proposed factorisations.

Numerical simulations↗
NASA NTRS1973

Irradiation of three T-111 clad uranium nitride fuel pins for 8070 hours at 990 C (1815 F)

The design and successful operation of three tantalum alloy (Ta-8W-2Hf) clad uranium mononitride (UN) fuel pins irradiated for 8070 hr at 990 C (1815 F) is described. Two pin diameters having measured burnups of 0.47 and 0.90 uranium atom percent were tested. No clad failures or swelling was detected; however, postirradiation clad samples tested failed with 1 percent strain. The fuel density decrease was 2 percent, and the fission gas release was less than 0.05 percent. Isotropic fuel swelling, which averaged about 0.5 percent, was less than fuel pin assembly clearances. Thus the clad was not strained. Thermocouples with a modified hot zone operated at average temperatures to 1100 C (2012 F) without failure. Factors that influence the ability to maintain uniform clad temperature as well as the results of the heat transfer calculations are discussed.

Slaby, J. G.↗
NASA NTRS1988

Integrated strategies for the exploration of Mars

Two mission programs for the manned exploration of Mars are discussed. The first is a four-man, split/spring mission proposed for 2001-2003. It includes the manned exploration of Phobos and teleoperated exploration of the Martian surface using unmanned vehicles with sample return capabilities. The second program is a series of three split/sprint missions planned for between 2005 and 2012. These missions include the exploration of three different areas of the Martian surface by three different four-man crews. Each crew would spend 20 days on Mars with manned sorties to Phobos and Deimos.

Craig, Mark K.↗
NASA NTRS1991

STS-35 Space Shuttle mission report

The STS-35 Space Shuttle Program Mission Report contains a summary of the vehicle subsystem activities during this thirty-eighth flight of the Space Shuttle and the tenth flight of the Orbiter vehicle Columbia (OV-102). In addition to the Columbia vehicle, the flight vehicle consisted of an External Tank (ET) (designated as ET-35/LWT-28), three Space Shuttle main engines (SSME's) (serial numbers 2024, 2012, and 2028 in positions 1, 2, and 3, respectively), and two Solid Rocket Boosters (SRB's) designated as BI-038. The primary objectives of this flight were to successfully perform the planned operations of the Ultraviolet Astronomy (Astro-1) payload and the Broad-Band X-Ray Telescope (BBXRT) payload in a 190-nmi. circular orbit which had an inclination of 28.45 degrees. The sequence of events for this mission is shown in tablular form. Summarized are the significant problems that occurred in the Orbiter subsystems during the mission. The official problem tracking list is presented. In addition, each Orbiter subsystem problem is cited in the applicable subsystem discussion.

Camp, David W.↗
NASA NTRS1992

Advanced tracking and data relay satellite system

The purpose of this communication satellite system are as follows: to provide NASA needs for satellite tracking and communications through the year 2012; to maintain and augment the current TDRS system when available satellite resources are expended in the latter part of the decade; to provide the necessary ground upgrade to support the augmented services; and to introduce new technology to reduce the system life cycle cost. It is concluded that no ATDRS spacecraft requirement for new modulation techniques, that data rate of 650 MBps is required, and that Space Station Freedom requirement is for 650 MBps data some time after the year 2000.

Stern, Daniel↗
NASA NTRS1991

STS-43 Space Shuttle mission report

The STS-43 Space Shuttle Program Mission Report contains a summary of the vehicle subsystem operations during the forty-second flight of the Space Shuttle Program and the ninth flight of the Orbiter Vehicle Atlantis (OV-104). In addition to the Atlantis vehicle, the flight vehicle consisted of the following: an External Tank (ET) designated as ET-47 (LWT-40); three Space Shuttle main engines (SSME's) (serial numbers 2024, 2012, and 2028 in positions 1, 2, and 3, respectively); and two Solid Rocket Boosters (SRB's) designated as BI-045. The primary objective of the STS-43 mission was to successfully deploy the Tracking and Data Relay Satellite-E/Inertial Upper Stage (TDRS-E/IUS) satellite and to perform all operations necessary to support the requirements of the Shuttle Solar Backscatter Ultraviolet (SSBUV) payload and the Space Station Heat Pipe Advanced Radiator Element (SHARE-2).

Fricke, Robert W.↗
NASA NTRS1992

STS-45 Space Shuttle mission report

The STS-45 Space Shuttle Program Mission Report contains a summary of the vehicle subsystem operations during the forty-sixth flight of the Space Shuttle Program and the eleventh flight of the Orbiter Vehicle Atlantis (OV-104). In addition to the Atlantis vehicle, the flight vehicle consisted of the following: an External Tank (ET) designated as ET-44 (LWT-37); three Space Shuttle main engines (SSME's), which were serial numbers 2024, 2012, and 2028 in positions 1, 2, and 3, respectively; and two Solid Rocket Boosters (SRB's) designated as BI-049. The lightweight redesigned Solid Rocket Motors (RSRM's) installed in each of the SRB's were designated as 360L021A for the left SRM and 360W021B for the right SRM. The primary objective of this mission was to successfully perform the planned operations of the Atmospheric Laboratory for Applications and Science-1 (ATLAS-1) and the Shuttle Solar Backscatter Ultraviolet Instrument (SSBUV) payloads. The secondary objectives were to successfully perform all operations necessary to support the requirements of the following: the Space Tissue Loss-01 (STL-01) experiment; the Radiation Monitoring Equipment-3 (RME-3) experiment; the Visual Function Tester-2 (VFT-2) experiment; the Cloud Logic to Optimize use of Defense System (CLOUDS-1A) experiment; the Shuttle Amateur Radio Experiment 2 (SAREX-2) Configuration B; the Investigation into Polymer Membranes Processing experiment; and the Get-Away Special (GAS) payload G-229. The Ultraviolet Plume Instrument (UVPI) was a payload of opportunity that required no special maneuvers. In addition to the primary and secondary objectives, the crew was tasked to perform as many as 10 Development Test Objectives (DTO'S) and 14 Detailed Supplementary Objectives (DSO's).

Fricke, Robert W.↗
NASA NTRS1993

STS-53 Space Shuttle mission report

The STS-53 Space Shuttle Program Mission Report provides a summary of the Orbiter, External Tank (ET), Solid Rocket Booster/Redesigned Solid Rocket Motor (SRB/RSRM), and the Space Shuttle Main Engine (SSME) subsystems performance during the fifty-second flight of the Space Shuttle Program, and the fifteenth flight of the Orbiter vehicle Discovery (OV-103). In addition to the Orbiter, the flight vehicle consisted of an ET, which was designated as ET-49/LWT-42; three SSME's, which were serial numbers 2024, 2012, and 2017 in positions 1, 2, and 3, respectively; and two SRB's, which were designated BI-055. The lightweight RSRM's that were installed in each SRB were designated 360L028A for the left SRB, and 360L028B for the right SRB. The primary objective of this flight was to successfully deploy the Department of Defense 1 (DOD-1) payload. The secondary objectives of this flight were to perform the operations required by the Glow Experiment/Cryogenic Heat Pipe Experiment Payload (GCP); the Hand-Held, Earth-Oriented, Real-Time, Cooperative, User-Friendly, Location-Targeting and Environmental System (HERCULES); the Space Tissue Loss (STL); the Battlefield Laser Acquisition Sensor Test (BLAST); the Radiation Monitoring Equipment-III (RME-III); the Microcapsules in Space-1 (MIS-1); the Visual Function Tester-2 (VFT-2); the Cosmic Radiation Effects and Activation Monitor (CREAM); the Clouds Logic to Optimize Use of Defense Systems-1A (CLOUDS-1A); the Fluids Acquisition and Resupply Experiment (FARE); and the Orbital Debris Radar Calibration Spheres (ODERACS). In addition to presenting a summary of subsystem performance, this report also discusses each Orbiter, ET, SSME, SRB, and RSRM in-flight anomaly in the applicable section of the report. Listed in the discussion of each anomaly is the officially assigned tracking number as published by each Project Office in their respective Problem Tracking List. All times given in this report are in Greenwich mean time (G.m.t.) as well as mission elapsed time (MET).

Fricke, Robert W., Jr.↗
NASA NTRS1994

STS-60 Space Shuttle mission report

The STS-60 Space Shuttle Program Mission Report summarizes the Payload activities as well as the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Redesigned Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance during the sixtieth flight of the Space Shuttle Program and eighteenth flight of the Orbiter vehicle Discovery (OV-103). In addition to the Orbiter, the flight vehicle consisted of an ET designated at ET-61 (Block 10); three SSME's which were designated as serial numbers 2012, 2034, and 2032 in positions 1, 2, and 3, respectively; and two SRB's which were designated BI-062. The RSRM's that were installed in each SRB were designated as 360L035A (lightweight) for the left SRB, and 360Q035B (quarterweight) for the right SRB. This STS-60 Space Shuttle Program Mission Report fulfills the Space Shuttle Program requirement as documented in NSTS 07700, Volume VIII, Appendix E. That document requires that each major organizational element supporting the Program report the results of its hardware evaluation and mission performance plus identify all related in-flight anomalies. The primary objectives of the STS-60 mission were to deploy and retrieve the Wake Shield Facility-1 (WSF-1), and to activate the Spacehab-2 payload and perform on-orbit experiments. Secondary objectives of this flight were to activate and command the Capillary Pumped Loop/Orbital Debris Radar Calibration Spheres/Breman Satellite Experiment/Getaway Special (GAS) Bridge Assembly (CAPL/ODERACS/BREMSAT/GBA) payload, the Auroral Photography Experiment-B (APE-B), and the Shuttle Amateur Radio Experiment-II (SAREX-II).

Fricke, Robert W., Jr.↗
NASA NTRS1995

STS-67 Space Shuttle mission report

The STS-67 Space Shuttle Program Mission Report provides the results of the orbiter vehicle performance evaluation during this sixty-eighth flight of the Shuttle Program, the forty-third flight since the return to flight, and the eighth flight of the Orbiter vehicle Endeavour (OV-105). In addition, the report summarizes the payload activities and the performance of the External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engines (SSME). The serial numbers of the other elements of the flight vehicle were ET-69 for the ET; 2012, 2033, and 2031 for SSME's 1, 2, and 3, respectively; and Bl-071 for the SRB's. The left-hand RSRM was designated 360W043A, and the right-hand RSRM was designated 360L043B. The primary objective of this flight was to successfully perform the operations of the ultraviolet astronomy (ASTRO-2) payload. Secondary objectives of this flight were to complete the operations of the Protein Crystal Growth - Thermal Enclosure System (PCG-TES), the Protein Crystal Growth - Single Locker Thermal Enclosure System (PCG-STES), the Commercial Materials Dispersion Apparatus ITA Experiments (CMIX), the Shuttle Amateur Radio Experiment-2 (SAREX-2), the Middeck Active Control Experiment (MACE), and two Get-Away Special (GAS) payloads.

Fricke, Robert W., Jr.↗
NASA NTRS1996

STS-74 Space Shuttle Mission Report

The STS-74 Space Shuttle Program Mission Report summarizes the Payload activities as well as the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance during the seventy-third flight of the Space Shuttle Program, the forty-eighth flight since the return-to-flight, and the fifteenth flight of the Orbiter Atlantis (OV-104). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-74; three Phase 11 SSME's that were designated as serial numbers 2012, 2026, and 2032 in positions 1, 2, and 3, respectively; and two SRB's that were designated BI-076. The RSRM's, designated RSRM-51, were installed in each SRB and the individual RSRM's were designated as 360TO51 A for the left SRB, and 360TO51 B for the right SRB. The primary objectives of this flight were to rendezvous and dock with the Mir Space Station and perform life sciences investigations. The Russian Docking Module (DM) was berthed onto the Orbiter Docking System (ODS) using the Remote Manipulator System (RMS), and the Orbiter docked to the Mir with the DM. When separating from the Mir, the Orbiter undocked, leaving the DM attached to the Mir. The two solar arrays, mounted on the DM, were delivered for future Russian installation to the Mir. The secondary objectives of the flight were to perform the operations necessary to fulfill the requirements of the GLO experiment (GLO-4)/Photogrammetric Appendage Structural Dynamics Experiment Payload (PASDE) (GPP), the IMAX Cargo Bay Camera (ICBC), and the Shuttle Amateur Radio Experiment-2 (SAREX-2). Appendix A lists the sources of data, both formal and informal, that were used to prepare this report. Appendix B provides the definition of acronyms and abbreviations used throughout the report. All times during the flight are given in Greenwich mean time (GMT)) and mission elapsed time (MET).

Fricke, Robert W., Jr.↗
NASA NTRS1998

Application of an Electrodynamic Tether System to Reboost the International Space Station

The results of a study on the application of an electrodynamic tether system to reboost the International Space Station are presented. A new and novel method is recommended using a partially bare tether. Locations are suggested as to where the tether system is to be attached at the Space Station. The effects of the tether system on the microgravity environment have been characterized and actually may be beneficial in that the system could be operated during the quiescent period. Alternative approaches to tether deployment and retrieval are discussed. It is shown that a relatively short tether system, 7 km long, operating at a power level of 5 kw could provide a cumulative savings of over a billion dollars during a ten-year period ending 2012. This savings is a direct result from a reduction in the number of flights normally required to deliver propellant for reboost.

Irwin E. Vas↗