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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The activities at Penn State University are described. During the period Oct. 1990 to Dec. 1991 work on HEAO-1 analysis of the Low Energy Detectors (LED) concentrated on using the improved detector spectral simulation model and fitting diffuse x-ray background spectral data. Spectral fitting results, x-ray point sources, and diffuse x-ray sources are described.
STS-93 was launched on July 23, 1999. There was an anomaly at the end of the launch in that the main engines shut down 0.16 second early because sensors detected a low level of oxidizer in the LOX tank (actually the duct from the tank to the vehicle). This resulted in a cutoff velocity for the vehicle that was 16 ft/sec low. It should have been 25872 ft/sec. The OMS engines were subsequently used to achieve the proper orbit. An investigation was immediately initiated into the cause of this LOX tank low level cutoff. It was noticed during the launch that the turbine temperatures for Main Engine 3 (E2019) were approximately 100 F higher than the preflight prediction. Linear Engine Model matching of the data indicated that a nozzle leak best fit the data. Post launch review of the data showed, that at approximately five seconds into the start, numerous parameters indicated small anomalous shifts. These shifts were all consistent with a rupture of nozzle tubes. Post launch review of the films showed that just after Space Shuttle Main Engine (SSME) ignition and just prior to liftoff a streak is seen in the exhaust plume of E2019. Just after liftoff the streak can be seen emanating from the nozzle wall. This photo confirmed that a leak was coming from the nozzle tubes. Based on the photo, the axial location of the leak was estimated to be 28 in. from the aft end of the nozzle and in line with nozzle coolant feed line #1. The streak continued to be visible during the launch.
Atmospheric temperature retrievals from TES observed radiances make possible the most complete separation of the constituent wave modes evident in Mars atmosphere to date. We use all of the data from the pre-mapping mission phase, which affords good sampling of the diurnal tides and stationary waves. TES retrievals of atmospheric temperature on a grid of pressure levels are the fundamental data set in this study. We then fit this data to selected fourier modes in longitude and time for latitude and L(sub s) bins. From this we have identified the amplitudes and phases of the diurnal and semi-diurnal tides, the first few (gravest) stationary waves and standing waves, as well as an estimate of the zonal and time mean temperature meridional cross sections. These results will be compared with existing models and theory. A possible critical layer for the sun-synchronous diurnal tide may indicate 40 m/s surface zonal wind near 50S for L(sub s) =255-285. Additional information is contained in the original extended abstract.
This quarter was largely devoted to a detailed study of temperature data acquired by the Cryogenic Limb Array Etalon Spectrometer (CLAES) on UARS. Our analysis used the same sequence of methods that have been developed, tested and refined on a more limited subset of temperature data acquired by the CRISTA instrument. We focused on a limited subset of our reasoning that geographical and vertical trends in the small-scale temperature variability could be compared with similar trends observed in November 1994 by the CRISTA-SPAS satellite. Results, backed up with hindcasts from the Mountain Wave Forecast Model (MWFM), reveal strong evidence of mountain waves, most persuasively in the Himalayas on 16-17 November, 1992. These CLAES results are coherent over the 30-50 km range and compare well with MWFM hindcasts for the same period. This constitutes, we believe, the first clear evidence that CLAES explicitly resolved long wavelength gravity waves in its CO2 temperature channel. A series of other tasks, related to mesoscale modeling of mountain waves in CRISTA data and fitting of ground-based and HRDI data on global scales, were seen through to publication stage in peer-reviewed journals.
The Multi-Mission Maximum Likelihood framework (threeML)is a flexi-ble python-based framework for multiwavelength data analysis in astronomy. ThreeMLallows joint likelihood fits of data recorded by many different instruments, from radio to gamma rays. This is achieved by encapsulating data access into instrument-specific plugins, leaving the rest of the analysis agnostic of the data format. In this paper, I out-line threeML’s design and major components, with a focus on the modeling language(astromodels) and the data-access plugins
X-ray absorption near edge structure (XANES) spectroscopy is widely used for operando catalyst characterization. We show that, for highly fluxional supported nanoclusters, the customary extraction of the oxidation state of the metal from the XANES data by fitting to the bulk standards is highly questionable. The XANES signatures as well as the apparent oxidation state for such clusters arise from a complex combination of many factors, and not only from the chemical composition in reaction conditions (e.g., oxygen content in oxidizing atmosphere). The thermally accessible isomerization and population of several structurally distinct cluster forms, cluster-support interaction, and intrinsic size effects all impact the metal oxidation state and XANES signal. We demonstrate this on copper oxide clusters with different compositions, Cu4Ox(x = 2-5) and Cu5Oy (y = 3, 5), deposited on amorphous alumina and ultrananocrystalline diamond, for which we computed the XANES spectra and compare the results to the experiment. We show in addition that fitting the experimental spectrum to calculated spectra of supported clusters can, in contrast, provide good agreement and insight into the spectrum-composition-structure relation. Experimental XANES interpreted using the proposed fitting scheme shows the partial reduction of Cu oxide clusters at rising temperatures, and pinpoints the specific stoichiometries that dominate in the ensemble of cluster states as the temperature changes.
Baryon acoustic oscillation data from the first year of the Dark Energy Spectroscopic Instrument (DESI) provide near percent-level precision of cosmic distances in seven bins over the redshift range z=0.1–4.2. Here, this paper is the follow-up to the original DESI BAO cosmology paper [A. G. Adame et al. (DESI Collaboration), arXiv:2404.03002], which considered the conventional w 0 w a cold dark matter (CDM) model. We use the novel DESI data, together with other cosmic probes, to constrain the background expansion history using some well-motivated physical classes of dark energy. In particular, we explore three physics-focused behaviors of dark energy from the equation of state and energy density perspectives: the thawing class (matching many simple quintessence potentials), emergent class (where dark energy comes into being recently, as in phase transition models), and mirage class [where phenomenologically the distance to cosmic microwave background (CMB) last scattering is close to that from a cosmological constant Λ despite dark energy dynamics]. All three classes fit the data at least as well as Λ CDM, and indeed can improve on it by Δχ 2 ≈ –5 to –17 for the combination of DESI BAO with CMB and supernova data while having one more parameter. The mirage class does essentially as well as w 0 w a CDM and exhibits moderate to strong Bayesian evidence preference with respect to Λ CDM. These classes of dynamical behaviors highlight worthwhile avenues for further exploration into the nature of dark energy.
The Galactic Center excess (GCE) remains an enduring mystery, with leading explanations being annihilating dark matter or an unresolved population of millisecond pulsars. Analyzing the morphology of the GCE provides critical clues to identify its exact origin. We investigate the robustness of the inferred GCE morphology against the effects of masking, an important step in the analysis where the gamma-ray emission from point sources and the galactic disk are excluded. Using different masks constructed from Fermi point source catalogs and a wavelet method, we find that the GCE morphology, particularly its ellipticity and cuspiness, is relatively independent of the choice of mask for energies above 2–3 GeV. The GCE morphology systematically favors an approximately spherical shape, as expected for dark matter annihilation. Compared to various stellar bulge profiles, a spherical dark matter annihilation profile better fits the data across different masks and galactic diffuse emission backgrounds, except for the stellar bulge profile which provides a similar fit to the data. Modeling the GCE with two components, one from dark matter annihilation and one tracing the Coleman bulge, we find this two-component model outperforms any single component or combinations of dark matter annihilation and other stellar bulge profiles. Uncertainty remains about the exact fraction contributed by each component across different background models and masks. Furthermore, when the Coleman bulge dominates, its corresponding spectrum lacks characteristics typically associated with millisecond pulsars, suggesting that it mostly models the emission from other sources instead of the GCE that is still present and spherically symmetric.
The equation of state and stability of topaz at high-pressure/high-temperature conditions have been investigated by in situ synchrotron X-ray diffraction (XRD) and Raman spectroscopy in this study. No phase transition occurs on topaz over the experimental pressure–temperature ($P-T$) range. The pressure–volume data were fitted by the third-order Birch–Murnaghan equation of state (EoS) with the zero-pressure unit–cell volume V 0 = 343.86 (9) Å 3 , the zero-pressure bulk modulus K 0 = 172 (3) GPa, and its pressure derivative K’ 0 = 1.3 (4), while the obtained K 0 = 155 (2) GPa when fixed K’ 0 = 4. In the pressure range of 0–24.4 GPa, the vibration modes of in-plane bending OH-groups for topaz show non-linear changes with the increase in pressure, while the other vibration modes show linear changes. Moreover, the temperature–volume data were fitted by Fei’s thermal equation with the thermal expansion coefficient α 300 = 1.9 (1) × 10 –5 K –1 at 300 K. Finally, the $P-T$ stability of topaz was studied by a synchrotron-based single-crystal XRD at simultaneously high $P-T$ conditions up to ~10.9 GPa and 700 K, which shows that topaz may maintain a metastable state at depths above 370 km in the upper mantle along the coldest subducting slab geotherm. Thus, topaz may be a potential volatile-carrier in the cold subduction zone. It can carry hydrogen and fluorine elements into the deep upper mantle and further affect the geochemical behavior of the upper mantle.
The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.
Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.
X-ray photoelectron spectroscopy (XPS) is one of the most widely used techniques for surface characterization. Analysis of XPS data is challenging and requires the analyst to fit the data with synthetic line shapes to reach physically meaningful interpretations. Experimental spectral envelopes, however, are complex and display asymmetric features that are often ignored or attributed to additional chemical components. The high-resolution XPS spectra of [C 4 C 1 Pyrr][NTf 2 ] and [C 8 C 1 Im][NTf 2 ] all exhibit a degree of asymmetry which is systematically observed at the higher binding energy side of photoemission envelopes. Here, we present the development of a refined fitting procedure for XPS spectra of these ionic liquid-based systems which include (a) Shirley background offset necessary to account for the insulator-like region and (b) spectral asymmetry in C 1s and N 1s regions. Further, Shirley and trapezoid components are applied to compensate for inelastic scattering taking place during electron transitions as high as 7.8 eV above the start of the fitting region in C 1s high-resolution spectrum. To demonstrate the fitness of this model, we present an analysis of a 2:1 mixture of [C 4 C 1 Pyrr][NTf 2 ]: [C 8 C 1 Im][NTf 2 ].
We explore the hypothesis that the unexplained data from LSND and MiniBooNE are evidence for a new, heavy neutrino mass-eigenstate that mixes with the muon-type neutrino and decays into an electron-type neutrino and a new, very light scalar particle. We consider two different decay scenarios, one with Majorana neutrinos, one with Dirac neutrinos; both fit the data equally well. We find a reasonable, albeit not excellent, fit to the data of MiniBooNE and LSND. The decaying-sterile-neutrino hypothesis, however, cleanly evades constraints from disappearance searches and precision measurements of leptonic meson decays, as long as $1~{\rm MeV}\gtrsim m_4\gtrsim 10$~keV. The SBN program at Fermilab should be able to definitively test the decaying-sterile-neutrino hypothesis.
The goal of the BioSentinel small satellite payload is to measure the effect of deep space radiation on the growth and metabolic activity of yeast cells. Raw test data is generated by fluidics cards containing yeast cells rehydrated at different periods, with metabolic activity measured by the reduction of alamarBlue. Each card well has a sensor array that measures the amount of red, green, and infrared light transmitted through the yeast culture. This illumination data is then converted to absorbance values, which are further converted into concentrations. The ultimate objective is to convert these concentrations into biologically-relevant metrics that can be compared against one another to determine changes due to differential radiation exposure. Beginning with IR absorbance data (corresponding to cell density) from ground studies, three parameters from a sigmoidal growth curve were extracted and analyzed: 𝜆 (lag phase), 𝜇 (max growth rate), and A (max cell growth). The data was fit to the Gompertz model of microbial growth using non-linear regression (Minitab), as the fit error was reduced compared to the simpler logistic growth curve. Graphs showed that the data contained a discrepancy (drift) in the lag phase that is attributable to a slow, constant loss of moisture. Correcting this discrepancy by fitting the first 25 hours of the data to a power function and subtracting these values from the absorbance readings obtained a better statistical fit to the growth curve in the lag phase. A power fit was selected over a linear fit because it reflected the effects of constant volume loss. This correction to the BioSentinel data analysis pipeline will enable quantitative statistical analysis of the effect of different levels of deep space radiation on yeast cells. Future work includes automation of drift correction and curve modeling to extract these parameters directly from data.
Abstract not provided.
The author has identified the following significant results. Three small scales of CIR photography were interpreted to determine the number of bark beetle-killed trees detected in each of six spot size categories. A procedure was developed to predict the probability of detecting spots in each spot size category and in turn to estimate the number of infestations and dead trees even on the smallest scale. Statistical tests of the data indicated that the linear model did not fit the data and that other models should be tested. As a result of daily monitoring of Black Hills radiometric instruments it was possible to show the spectral energy relationships in the ponderosa pine ecosystems over time. These data have been helpful for comparison with radiance signatures extracted from ERTS-1 bulk 70mm using precision microdensitometry. Effects of atmospheric interference were shown by a 30 percent increase in scene radiance on channel 4 of the satellite imagery. A calibration and scaling technique was developed and tested to enable interpretation of ERTS-1 bulk and precision data for the Atlanta test site. The technique includes calibration of a photographic copy system for the I2S image combiner and the production of scaled overlays of grid coordinate systems, study area locations, and outline maps of county boundaries.
A criterion for characterizing an iteratively trained classifier is presented. The criterion is based on an information theoretic measure that is developed from modeling classifier training iterations as a set of cascaded channels. The criterion is formulated as a figure of merit and as a performance index to check the appropriateness of application of the characterized classifier to an unknown data base and for implementing classifier updates and data selection respectively.
A criterion for characterizing an iteratively trained classifier is presented. The criterion is based on an information theoretic measure that is developed from modeling classifier training iterations as a set of cascaded channels. The criterion is formulated as a figure of merit and as a performance index to check the appropriateness of application of the characterized classifier to an unknown data base and for implementing classifier updates and data selection, respectively.