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At least 127 records · Page 7

Active and Passive Storage Solutions for Low Temperature Lunar Sample Return

Active and passive thermal storage solutions to return lunar biological, physical science and/or geology samples to the Earth are under development. Temperature requirements for biological/physical science sample conditioning range from -100˚C to -153˚C and possibly down to -253˚C for geological samples. The lower limit for geological samples is derived from the temperature of permanently shadowed regions on the lunar surface and the sublimation temperature of specific volatiles of interest (i.e., H2O, NH3 and CH4). Individual samples for the lunar application are expected to be less than 50 kg with a 700 kg allowance for the container and refrigeration. Requiring no electrical power or heat rejection, passive approaches, comprised of high performance insulation and consumable Joule-Thompson cooling, may be preferred for shorter duration missions (< 30 days) to provide significant mass savings. Active storage approaches with cryogenic cooling may be necessary to preserve samples for longer periods of time. A notional passive storage concept with an internal vapor cooled shield is shown below. The vapor cooled shield contains the sample and is isolated from the outer container with concentric reflective rigid shields and conventional multi-layer and/or aerogel insulation on the outer layer. A special removable, insulating end cap to stow or retrieve the sample is included.

Lunar Sample Return↗

Companions to isolated elliptical galaxies: revisiting the Bothun-Sullivan (1977) sample using the NASA/IPAC extragalactic database

We investigate the number of physical companion galaxies for a sample of relatively isolated elliptical galaxies. The NASA/IPAC Extragalactic Database (NED) has been usedto reinvestigate the incidence of satellite galaxies for a sample of 34 elliptical galaxies, firstinvestigated by Bothun & Sullivan (1977) using a visual inspection of Palomar Sky Survey prints out to a projected search radius of 75 kpc. We have repeated their original investigation usingdata cataloged data in NED. Nine of these ellipticals appear to be members of galaxy clusters:the remaining sample of 25 galaxies reveals an average of +1.0 f 0.5 apparent companions per galaxy within a projected search radius of 75 kpc, in excess of two equal-area comparisonregions displaced by 150-300 kpc. This is nearly an order of magnitude larger than the +0.12+/- 0.42 companions/galaxy found by Bothun & Sullivan for the identical sample. Making use of published radial velocities, mostly available since the completion of the Bothun-Sullivan study,identifies the physical companions and gives a somewhat lower estimate of +0.4 companions per elliptical. This is still a factor of 3x larger than the original statistical study, but giventhe incomplete and heterogeneous nature of the survey redshifts in NED, it still yields a firmlower limit on the number (and identity) of physical companions. An expansion of the searchradius out to 300 kpc, again restricted to sampling only those objects with known redshifts in NED, gives another lower limit of 4.3 physical companions per galaxy. (Excluding fiveelliptical galaxies in the Fornax cluster this average drops to 3.5 companions per elliptical.)These physical companions are individually identified and listed, and the ensemble-averagedradial density distribution of these associated galaxies is presented. For the ensemble, the radial density distribution is found to have a fall-off consistent with p c( R^-0.5 out to approximately150 kpc. For non-Fornax cluster companions the fall-off continues out to the 300-kpc limit of thesurvey. The velocity dispersion of these companions is found to be constant with projected radial distance from the central elliptical, holding at a value of approximately +/- 300-350 km/sec overall.

NED NASA/IPAC Extragalactic Database↗

Materials processing in low gravity

Activities of the Materials Processing in Low Gravity Program in which the University of Alabama in Huntsville (UAH) designed, fabricated, and performed various low gravity experiments in materials processing between October 26, 1988 through October 25, 1989 are discussed. Details of low gravity experiments using the Drop Facilities at the Marshall Space Flight Center (MSFC) and the KC-135 aircraft at Ellington Field are discussed. This effort included the defining of experimental requirements and equipment, experiment-facility integration requirements, building/assembling the necessary experiment apparatus, and conducting experiments which will contribute to the knowledge base for commercialization of materials processing in low gravity. UAH also performed logistical support needed to execute the experimentation, the necessary sample preparation, metallography analysis, and physical properties measurements of the processed samples.

Workman, Gary L.↗

Analysis Methods and Results for Weak Gamma-Ray Bursts in the BATSE Data

We report initial results on the statistical properties of the dimmest gamma-ray bursts (GRBs) observed with the Burst and Transient Source Experiment (BATSE), using new ground-based methods to obtain a sample of GRBs from 502 days of BATSE data. Using the most sensitive ground-based detection of GRBs, the sample extends to GRBs much fainter than those detected by the on-board trigger, but because of the temporal resolution of the data, the sample is limited to GRBs of duration of at least 2(approx.)s. For each detected event, Bayesian probabilities are calculated for the event to belong to each of seven classes of differing physical origins. The sample of GRB candidates is defined by the requirement that the Bayesian probability for belonging to the GRB class is higher than 0.5. The intensity distribution of the GRB sample is corrected using a Monte Carlo simulation of the post-flight detection efficiency. The dimmest BATSE bursts of the sample continue the hardness-intensity trend seen in brighter GREs and are consistent with isotropy.

Mitrofanov, I. G.↗

Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO 2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO 2 leakage. We validate the performance of our methods using comprehensive numerical tests. Here via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.

58 GEOSCIENCES↗

Tidal distortions in pairs of early-type galaxies

The authors are conducting an imaging survey of pairs of elliptical galaxies which has already produced interesting results. Some pairs present a common pattern of distortion interpreted in terms of tidal effects (Davoust and Prugniel, 1988; Prugniel et al., 1989). Other examples drawn from the literature (Borne and Hoessel, 1988; Colina and Perez-Fournon, 1990) share the same morphology. New cases and lists of the characteristics of 24 such systems. The authors' pairs are drawn from a sample of binary and multiple galaxies which has in turn been extracted from the CGCG, UGC (Nilson, 1973) and VV (Vorontsov-Velyaminov, 1959) catalogues. This sample includes that of Karachentsev (1972). It contains 1800 pairs, among which 700 are S - S or mixed morphology pairs. The authors are working on the remainder to produce a sample of close physical pairs of elliptical galaxies (they also include bulge dominated SO's since the morphological discrimination from ellipticals is often ambiguous, in particular for interacting galaxies). One of the interests of this work is to provide a sample selected on purely optical criteria, at variance with other works (e.g., Valentijn and Casertano, 1988). This will allow statistical studies of non-optical properties of these pairs (in particular radio emission). The authors have so far obtained charge-coupled device (CCD) images of 125 pairs with a 2m telescope and velocities' differences of 78 pairs were obtained using the 1.93 meter telescope of Observatoire de Haute Provence and from the literature. One is an optical pair (VV 190). Eighteen of our pairs present the morphological effect described in Davoust and Prugniel (1988): the external parts of each member are stretched in opposite senses in a direction rougly perpendicular to the pair axis. The proportion of 15 plus or minus 4 percent distorted pairs confirms previous estimates. Except for a few cases involving flattened galaxies with nearly aligned major axes which deserve careful detailed analysis (Prugniel, 1989), the apparent distortions do correspond to physical distortions. We have searched the literature for isophote maps showing this effect. In the survey of radio galaxies by Colina and Perez-Fournon (1990), 7 out of 20 pairs show this characteristic distortion.

Prugniel, Philippe↗

Combining Data with Physical Knowledge for Uncertainty Quantification in Certification and Reliability Analysis

Unifying empirical data with predictive models can enable engineering cost-savings through certification by analysis and reliability-based design. Both concepts require rigorous uncertainty quantification (UQ) and robust understanding and treatment of relevant physics. Combining sampling-based UQ algorithms with high-fidelity simulations creates a computational bottleneck that is often alleviated through the use of machine learning (ML). ML can be used to create computationally efficient surrogates for simulations of complex or high-dimensional physical interactions (e.g., multi-phase interactions associated with melt pools in laser powder bed fusion or spatially-dependent material properties in functionally graded materials). However, negative side effects of ML may include a lack of interpretability and negative correlation between event rarity and simulation accuracy due to a lack of training data. As such, it is important to infuse ML algorithms with physics-based guardrails to provide confidence in their predictions. This talk will provide a brief review of recent NASA research at this intersection of physics-based simulation, ML, and UQ with a focus on certification and reliability analysis.

uncertainty quantification↗

Wide binaries in the direction of Andromeda

A statistically well-defined sample of candidate binary stars with separations that are expected to be mostly in the range 0.01-0.1 pc is presented. The 36 candidate pairs are all brighter than apparent visual magnitude 12; about half of the projected pairs are expected to be physically associated. After the candidates are studied spectroscopically and photometrically to establish which pairs are real binaries and to measure their physical characteristics, the sample can be used to help determine the dependence of number density on semimajor axis for wide binaries, a function that is of considerable theoretical interest.

Bahcall, J. N.↗

Aerosol Indirect Effect Studies at Southern Great Plains During the May 2003 Intensive Operations Period

During May 2003 the Department of Energy's Atmospheric Radiation Measurement Program conducted an Intensive Operations Period (IOP) to measure the radiative effects of aerosol and clouds. A suite of both in situ and remote sensing measurements were available to measure aerosol and cloud parameters. This paper has three main goals: First, it focuses on comparison between in situ retrievals of the radiatively important drop effective radius r(sub e) and various satellite, airborne, and surface remote sensing retrievals of the same parameter. On 17 May 2003, there was a fortuitous, near-simultaneous sampling of a stratus cloud by five different methods. The retrievals of r(sub e) agree with one another to within approx.20%, which is approximately the error estimate for most methods. Second, a methodology for deriving a best estimate of r(sub e) from these different instruments, with their different physical properties and sampling volumes, is proposed and applied to the 17 May event. Third, the paper examines the response of r(sub e) to changes in aerosol on 3 days during the experiment and examines the consistency of remote sensing and in situ measurements of the effect of aerosol on r(sub e). It is shown that in spite of the generally good agreement in derived r(sub e), the magnitude of the response of r(sub e), to changes in aerosol is quite sensitive to the method of retrieving r(sub e) and to the aerosol proxy for cloud condensation nuclei. Nonphysical responses are sometimes noted, and it is suggested that further work needs to be done to refine these techniques.

Feingold, Graham↗

Chemistry in Retrieved Ryugu Asteroid Samples Revealed by Non-Invasive X-ray Microanalyses: Pink-Beam Fluorescence CT and Tender-Energy Absorption Spectroscopy

The Hayabusa2 space mission recently retrieved 5.4 g of material from asteroid Ryugu, providing the first direct access to pristine material from a carbonaceous asteroid. This study employs a novel combination of non-invasive synchrotron X-ray techniques to examine microscale chemistry (elemental distributions and element-specific chemical speciation and local structure) inside Ryugu grains without physically cutting the samples. Manganese primarily occurs in carbonate: Mn-bearing dolomite with minor earlier ankerite. Iron sulfides present as large single grains and as smaller particles in the finer-grained matrix are both predominantly pyrrhotite. At the 5 μm scale, Fe sulfides do not show the mineralogical heterogeneity seen in many carbonaceous meteorites but exhibit some heterogeneous localized oxidation. Iron is present often as intergrowths of oxide and sulfide, indicating incomplete replacement. Trace selenium substitutes for S in pyrrhotite. Copper is present as Fe-poor Cu sulfide. These results demonstrate multiple episodes of fluid alteration on the parent body, including partial oxidation, and help constrain the sequence or evolution of fluids and processes that resulted in the current grain-scale mineralogical composition of Ryugu materials.

36 MATERIALS SCIENCE↗

Implementing Faraday effect measurement constraints into the Grad–Shafranov equilibrium fitting code EFIT

A new tool for the exploration and diagnosis of the internal magnetic field of plasmas in the DIII-D tokamak in the form of a constraint on the EFIT (Equilibrium Fitting) Grad–Shafranov code based on the Faraday-effect Radial Interferometer-Polarimeter (RIP) diagnostic is presented, including description, verification, and sample application. The physics underlying the diagnostic and its implementation into EFIT are discussed, and the results showing the verification of the model are given, and the model’s limitations are discussed. The influence of the diagnostic’s input on the resulting equilibrium parameters is characterized. Furthermore, the effect of electron density profile refinement is evaluated and found to be negligible. A sample application of the diagnostic is shown, indicating that the RIP constraint has similar effects on the equilibrium as motional Stark effect constraints do.

47 OTHER INSTRUMENTATION↗

X-ray properties of the X-CLASS-redMaPPer galaxy cluster sample: the luminosity–temperature relation

ABSTRACT This article presents the results of a spectroscopic analysis of the X-CLASS-redMaPPer (XC1-RM) galaxy cluster sample. X-CLASS is a serendipitous search for clusters in X-ray wavebands based on the XMM–Newton archive, whereas redMaPPer is an optical cluster catalogue derived from the Sloan Digital Sky Survey (SDSS). The present sample comprises 92 X-ray extended sources identified in optical images within 1 arcmin separation. The area covered by the cluster sample is ∼ 27 deg2. The clusters span a wide redshift range (0.05 < z < 0.6) and 88 clusters benefit from spectrosopically confirmed redshifts using data from SDSS Data Release 14. We present an automated pipeline to derive the X-ray properties of the clusters in three distinct apertures: R500 (at fixed mass overdensity), Rfit (at fixed signal-to-noise ratio) and ${R}_{300\, {\rm kpc}}$ (fixed physical radius). The sample extends over wide temperature and luminosity ranges: from 1–10 keV and from 6 × 1042 to 11 × 1044 erg s−1, respectively. We investigate the luminosity–temperature (L–T) relation of the XC1-RM sample and find a slope equal to 3.03 ± 0.26. It is steeper than predicted by self-similar assumptions, in agreement with independent studies. A simplified approach is developed to estimate the amount and impact of selection biases that might be affecting our recoveredL–Tparameters. The result of this simulation process suggests that the measuredL–Trelation is biased to a steeper slope and higher normalization.

Molham, Mona↗

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗

IRMA

IRMA (In)elastic Representation of Materials As S(α,β) evaluations IRMA turns one phonon model into three outputs that usually require three separate tool chains: an evaluated nuclear-data file, predicted neutron-scattering spectra, and scattering kernels for Monte Carlo transport. The three outputs draw on a single, consistent description of the material, so the evaluation, the spectroscopy that can validate it, and the transport that uses it always agree about the physics. Nuclear data. IRMA writes ENDF-6 File 7 thermal scattering evaluations on automatically constructed (α, β) grids. This part reimplements and generalizes NJOY's LEAPR: the classic kernels reproduce freshly generated NJOY2016 tapes digit for digit and published reference tapes to about 1e-4, and the generalized paths add the exact coherent one-phonon term, anisotropic Debye-Waller tensors, coherent elastic for arbitrary crystals, and a per-species partition for polyatomic materials. The tapes feed NJOY, AMPX, FUDGE, and every transport code downstream of them. Neutron spectroscopy. The irma.spectra forward model projects the same physics onto an instrument's kinematics and resolution: INS spectra for VISION and generic indirect geometries, and 2-D S(Q,E) powder maps for direct-geometry spectrometers, from a phonopy model or straight from a phonon DOS. It can be used to predict a proposed measurement before beam time; in analysis, it supplies the calculated single-scattering counterpart of a measured spectrum, from the same material description the evaluation was built from. Monte Carlo transport. The irma.ncrystal exporter writes per-temperature scattering kernels for the companion NCrystal plugin, so McStas, OpenMC, and other NCrystal-aware codes sample the same physics. The exported kernels carry the per-site anisotropic Debye-Waller tensors, keeping directional coherent-elastic physics that NCrystal's standard scalar treatment does not represent. With the same physics inside a transport code, an entire beamline becomes a virtual experiment: IRMA's end-to-end validation ran a custom McStas implementation of the ARCS spectrometer, assembled from the existing McVine and McStas models, against measured data. From a bare crystal structure. The irma mlip front end builds the phonon model itself: a structure file and a choice of potential are enough. Nine pretrained machine-learned interatomic potentials are supported, on a laptop CPU, with no first-principles calculation; an approximate phonon model for a new material costs minutes, not a DFT campaign, and the build emits prefilled inputs for all three outputs. The result is a good starting point rather than a finished evaluation: survey-quality physics with every parameter exposed for review. A converged atomistic calculation enters the same way, as a phonopy model, when higher fidelity is needed.

Ramic, Kemal [Oak Ridge National Laboratory (ORNL)↗

Investigating the mechanism of copper–carbon interactions in ultraconductor materials via in situ thermal X-ray and Raman spectroscopy

Improving energy transfer efficiency is critical to advancing technologies for a more sustainable future. Nanoscale materials, specifically metal–carbon composites such as ultraconductors, have shown promise in this field due to their enhanced electrical and thermal conductivities. However, the origin of the enhancement has yet to be determined. Prior research has primarily explored these materials at room temperature in an attempt to explain this phenomenon, but these materials have not yet been examined under enhanced thermal conditions. This study probes ultraconductor materials during the heating process to uncover the origins of their enhanced thermal conductivity. Understanding the mechanism underpinning the enhanced properties of the material could lead to increased property enhancement and therefore improved performance in energy transfer technologies. In this work we employ in situ thermal X-ray absorption near edge spectroscopy (XANES) and Raman spectroscopy to characterize copper-based covetic materials, revealing how thermal conditions influence the bonding environment and interaction between the copper and infused carbon. Our findings suggest that heating the materials does not result in the formation of chemical bonds between the carbon and copper framework of the material but rather points to a primarily physical interaction within the sample. Furthermore, we hypothesize possible mechanisms underlying the nature of the physical interaction leading to enhanced properties. These insights contribute to a deeper understanding of the material's behavior under relevant thermal conditions and highlight its potential for integration into next-generation energy systems.

36 MATERIALS SCIENCE↗

VIPIR: A High-Throughput Drop-Weight Impact Instrument for Imaging the Initiation and Propagation of Reactions in Energetic Materials

Characterizing the handling safety and sensitivity of explosives has been a challenging area of study for over 60 years. Historically one of the most accessible and widely utilized experiments has been the drop-weight impact test, which involves dropping a weight on a small sample sandwiched between two anvils. Because this experiment generally only utilizes sound thresholds to determine whether or not a sample reacted, the physical and chemical properties governing sensitivity remain convolved. Better understanding of chemical and material characteristics is needed to give the chemistry and engineering communities a predictive tool to determine the handling sensitivity of explosives prior to pursuing expensive and potentially hazardous synthesis and formulation operations. Here, we are developing a high throughput drop tower instrument capable of imaging the deformation and flow of energetic materials during impact and the resulting thermal ignition and propagation events. This instrument is based on previous designs but has been modified for higher throughput and tailorable modifications in the future. Herein, we present key design features that enable high-speed visible and thermal imaging of explosive initiation by sub-shock impacts, as well as preliminary results in which ignition sites were observed in an erythritol tetranitrate sample.

47 OTHER INSTRUMENTATION↗

Use of the sampling theorem to speed up near-field physical optics scattering calculations

Physical optics scattering calculations performed on the DSN 34-m beam-waveguide antennas at Ka-band (32 GHz) require approximately 12 hr of central processing unit time on a Cray Y-MP2 computer. This is excessive in terms of resource utilization and turnaround time. Typically, the calculations involve five surfaces, and the calculations are done two surfaces at a time. The sampling theorem is used to reduce the number of current values that must be calculated over the second surface by performing a physical optics integration over the first surface. The additional number of current values required on the second surface by subsequent physical optics integrations is obtained by interpolation over the original current values. Time improvements on the order of a factor of 2 to 4 were obtained for typical scattering pairs.

Cramer, P. W.↗

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗