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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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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Investigations Using Laboratory Testbeds to Interpret Flight Instrument Datasets from Mars Robotic Missions

The Astromaterials Research and Exploration Science Directorate at the NASA Johnson Space Center (JSC) has laboratory instrumentation that mimic the capabilities of corresponding flight instruments to enable interpretation of datasets returned from Mars robotic missions. The lab instruments have been and continue to be applied to datasets for the Moessbauer Spectrometer (MB) on the Mars Exploration Rovers (MER), the Thermal & Evolved Gas Analyzer (TEGA) on the Mars Phoenix Scout, the CRISM instrument on the Mars Reconnaissance Orbiter Missions and will be applied to datasets for the Sample Analysis at Mars (SAM), Chemistry and Mineralogy (CheMin) and Chemistry & Camera (ChemCam) instruments onboard the Mars Science Laboratory (MSL). The laboratory instruments can analyze analog samples at costs that are substantially lower than engineering models of flight instruments, but their success to enable interpretation of flight data depends on how closely their capabilities mimic those of the flight instrument. The JSC lab MB instruments are equivalent to the MER instruments except without flight qualified components and no reference channel Co-57 source. Data from analog samples were critical for identification of Mg-Fe carbonate at Gusev crater. Fiber-optic VNIR spectrometers are used to obtain CRISM-like spectral data over the range 350-2500 nm, and data for Fephyllosilicates show irreversible behavior in the electronic transition region upon dessication. The MB and VNIR instruments can be operated within chambers where, for example, the absolute H2O concentration can be measured and controlled. Phoenix's TEGA consisted of a calorimeter coupled to a mass spectrometer (MS). The JSC laboratory testbed instrument consisted of a differential scanning calorimeter (DSC) coupled to a MS configured to operate under total pressure (12 mbar), heating rate (20 C/min), and purge gas composition (N2) analogous to the flight TEGA. TEGA detected CO2 release at both low (400-680 C) and high (725-820 C) temperature and an endothermic reaction in concert with the high temperature release. The high-temperature thermal decomposition is consistent with calcite, dolomite, or ankerite, (3-6 wt.%) or any combination of these phase based upon laboratory testbed experiments. Recent laboratory experiments suggest that the low temperature CO2 release was caused by a reaction between calcium carbonate and hydrated magnesium perchlorate; although, CO2 release by the oxidation of organic materials and Fe-/Mg-rich carbonates cannot be ruled out. MSL landed in Gale crater on August 5, 2012. Although numerous analog samples have been analyzed on the JSC laboratory testbeds, no SAM, CheMin, or ChemCam analyses have been acquired by MSL to date. The JSC SAM laboratory testbed consists of a thermal analyzer coupled with a MS configured to operate under total pressure (30 mbar), heating rate (35 C/min), and purge gas composition (He) analogous to the flight SAM. The CheMin and ChemCam laboratory testbeds were developed and built by inXitu, Inc. and Los Alamos National Laboratory, respectively, to acquire datasets relevant to the MSL CheMin and ChemCam flight instruments.

Ming, D. W.↗
NASA NTRS2016

Interfacial Structure and Capacitance of Li-doped Ionic Liquid Electrolytes from Molecular Simulations

Ionic liquids have been proposed as candidate electrolytes for high-energy density, rechargeable batteries, supercapacitors, and hybrid energy storage devices. Though Li-salt is often present in these systems, its influence on interfacial properties is largely uncharacterized. We, thereby, present an extensive computational analysis, supported by experimental comparisons, of the properties of a representative set of these electrolytesat an ideal carbon interface as a function of Li-salt doping and voltage. We have performed polarizable molecular (MD) dynamics simulations, using the APPLEP force field, to evaluate electric double layer (EDL) capacitance and distribution of Li+ in the EDL. Differential capacitance exhibits the characteristic camel profile and is insensitive to Li-doping. Li+ localizes in the second molecular layer of the EDL, which is a result of confinement from free energy barriers associated with ion layering. Joint MDelectronic structure computations show the electrochemical window of the electrolytes to be a weak function of Li-doping. Estimates of supercapacitor specific energy are made using the computed window and capacitance. The magnitude and trends in specific energy are in good agreement with experiment.

batteries↗
NASA NTRSDate not supplied

Reconfiguring COSSIR for the Next Generation of Cloud and Precipitation Science

NASA originally developed the Compact Scanning Sub-millimeter Imaging Radiometer (CoSSIR) in the early 2000s, where it was own in the CRYSTAL-FACE and TC4 field experiments. With renewed interest in sub-millimeter remote sensing motivated by the upcoming launch of the EUMETSAT-sponsored Ice Cloud Imager (ICI), along with its potential for inclusion as part of the NASA Decadal Survey Aerosols, CLouds, Convection, and Precipitation (ACCP) observing architecture, CoSSIR is being refurbished to prepare for routine suborbital submillimeter measurements this decade. Upgrades include hardware (procurement of shared parts so that CoSMIR and CoSSIR can fly concurrently) and a change to the channels, with a focus on differential scattering and polarization at eight frequencies ranging from 170 to 640 GHz. As part of the refurbishment, the instrument name has been changed to Configurable Scanning Sub-millimeter Instrument/Radiometer, retaining the same acronym, in order to emphasize the science objectives that are enabled by CoSSIR's unique scanning geometry and channel set. These anticipated capabilities include two-dimensional pro ling of clouds and precipitation and characterization of the geometry (shape and orientation) of large cloud and precipitating ice particles.

Radiometry↗
DOE OSTI2022

Measurement of Charmonium Production in $p + p$ and $p + d$ Interactions in the Fermilab SeaQuest Experiment

The Fermilab SeaQuest experiment has measured dimuon events from the interactions of 120GeV proton beam on liquid hydrogen and deuterium targets with dimuon mass between 2 and 9GeV. These dimuon events contain both the Drell-Yan process and the charmonium ($J/\psi$ and $\psi^\prime$) production. Unlike the Drell-Yan process which probes the antiquark distributions in the nucleons, the charmonium production is sensitive to both quark and gluon distributions. SeaQuest has extracted the $\sigma^{pd}/2\sigma^{pp}$ ratio as well as the differential cross sections for charmonium production in the kinematic region of $0.4 < x_F < 0.9$. The $\sigma^{pd}/2\sigma^{pp}$ ratio for charmonium production are found to be significantly different from that of the Drell-Yan process. The measured differential cross sections for charmonium production are compared with theoretical calculations using Color Evaporation Model and Non-Relativistic QCD model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Predicting Fracture Network Development in Crystalline Rocks

The geometric properties of fractures influence whether they propagate, arrest, or coalesce with other fractures. Thus, quantifying the relationship between fracture network characteristics may help predict fracture network development, and perhaps precursors to catastrophic failure. To constrain the relationship and predictability of fracture characteristics, we deform eight one centimeter tall rock cores under triaxial compression while acquiring in situ X-ray tomograms. The tomograms reveal precise measurements of the fracture network characteristics above the spatial resolution of 6.5 µm. We develop machine learning models to predict the value of each characteristic using the other characteristics, and excluding the macroscopic stress or strain imposed on the rock. The models predict fracture development more accurately in the experiments performed on granite and monzonite, than the experiments on marble. Fracture network development may be more predictable in these igneous rocks because their microstructure is more mechanically homogeneous than the marble, producing more systematic fracture development that is not strongly impeded by grain contacts and cleavage planes. The varying performance of the models suggest that fracture volume, length, and aperture are the most predictable of the characteristics, while fracture orientation is the least predictable. Orientation does not correlate with length, as suggested by the idea that the orientation evolves with increasing differential stress and thus fracture length. This difference between the observed and expected relationship between orientation and length highlights the influence of mechanical heterogeneities and local stress perturbations on fracture growth as fractures propagate, link, and coalesce.

58 GEOSCIENCES↗
DOE OSTI2022

Differentiation of static and dynamic interfacial area in the structured packed column

Effective mass-transfer area plays a key role for post-combustion carbon capture. The static area, an inactive part relative to mass transfer, needs to be differentiated in the interfacial area. In the present study, CFD method is employed to investigate static and dynamic interfacial areas in Mellapak 500.Y structured packing. Here, a wide range of physical properties and loading conditions are included to understand their effects on both areas. The result shows that the relationship between interfacial area and interfacial velocity at the local scale can be described by gamma distribution with good accuracy. The influences of various physical properties derived from simulation are compared with existing experiment-based correlations for both static and interfacial areas. The result shows the dominant influence of viscosity on the static area, and the most importance of the influence of contact angle on the interfacial area. This study also highlights the importance of viscosity, which is usually ignored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantitative phase retrieval and characterization of magnetic nanostructures via Lorentz (scanning) transmission electron microscopy

Magnetic materials phase reconstruction using Lorentz transmission electron microscopy (LTEM) measurements have traditionally been achieved using longstanding methods such as off-axis holography (OAH) fast-Fourier transform technique and the transport-of-intensity equation (TIE). The increase in access to processing power alongside the development of advanced algorithms have allowed for phase retrieval of nanoscale magnetic materials with greater efficacy and resolution. Specifically, reverse-mode automatic differentiation (RMAD) and the extended electron ptychography iterative engine (ePIE) are two recent developments of phase retrieval that can be applied to analyzing micro-to-nano- scale magnetic materials. This work evaluates phase retrieval using TIE, RMAD, and ePIE in simulations of Permalloy (Ni 80 Fe 20 ) nanoscale islands, or nanomagnets. Extending beyond simulations, we demonstrate total phase retrieval and image reconstructions of a NiFe nanowire using OAH and RMAD in LTEM and ePIE in Lorentz-mode-4D scanning transmission electron microscopy experiments and determine the saturation magnetization through corroborations with micromagnetic modeling. Finally, we demonstrate the efficacy of these methods in retrieving the total phase and highlight its use in characterizing and analyzing the proximity effect of the magnetic nanostructures.

Lorentz transmission electron microscopy↗
DOE OSTI2022

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability. However, the robustness of kNN-based deep classification models has not been thoroughly explored and kNN attack strategies are underdeveloped. In this paper, we first propose an Adversarial Soft kNN (ASK) loss for developing more effective kNN-based deep neural network attack strategies and designing better defense methods against them. Our ASK loss provides a differentiable surrogate of the expected kNN classification error. It is also interpretable as it preserves the mutual information between the perturbed input and the in-class-reference data. We use the ASK loss to design a novel attack method called the ASK-Attack (ASK-Atk), which shows superior attack efficiency and accuracy degradation relative to previous kNN attacks on hidden layers. We then derive an ASK-Defense (ASK-Def) method that optimizes the worst-case ASK training loss. Experiments on CIFAR-10 (ImageNet) show that (i) ASK-Atk achieves ≥13% (≥ 13% ) improvement in attack success rate over previous kNN attacks, and (ii) ASK-Def outperforms the conventional adversarial training method by ≥ 6.9% (≥ 3.5% ) in terms of robustness improvement. Relevant codes are available at https://github.com/wangren09/ASK .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

Tkachenko, Alexei V. (ORCID:000000031291243X)↗
NASA NTRS1974

Quantum mechanical streamlines. I - Square potential barrier

Exact numerical calculations are made for scattering of quantum mechanical particles hitting a square two-dimensional potential barrier (an exact analog of the Goos-Haenchen optical experiments). Quantum mechanical streamlines are plotted and found to be smooth and continuous, to have continuous first derivatives even through the classical forbidden region, and to form quantized vortices around each of the nodal points. A comparison is made between the present numerical calculations and the stationary wave approximation, and good agreement is found between both the Goos-Haenchen shifts and the reflection coefficients. The time-independent Schroedinger equation for real wavefunctions is reduced to solving a nonlinear first-order partial differential equation, leading to a generalization of the Prager-Hirschfelder perturbation scheme. Implications of the hydrodynamical formulation of quantum mechanics are discussed, and cases are cited where quantum and classical mechanical motions are identical.

Hirschfelder, J. O.↗
NASA NTRS2000

Thermal Stability of Otto Fuel Prepolymer

Otto Fuel II contains a nitrate ester, plasticizer, and 2-NPDA as a stabilizer. Otto Fuel with stabilizers from three vendors was investigated by dynamic and isothermal differential scanning calorimetry (DSC) using samples sealed in a glass ampoule and by Isothermal Microcalorimetry (IMC) using 10 gram samples aged at 75 C for 35 days. DSC kinetics did not show differences between the stabilizer; the samples had an activation energy of 36.7 +/- 0.6 kcal/mol. However, IMC analysis was sensitive enough to detect small differences between the stabilizer, namely energy of interaction values of 7 to 14 Joules. DSC controlled cooling and heating at 5 C/min from 30 to -60 to 40 C experiments were similar and showed a crystallization peak at -48 +/- 1 C during cooling, and upon heating there was a glass transition temperature step at approx. -54 +/- 0.5 C and a melting peak at -28 +/- 0.4 C.

Tompa, Albert S.↗
NASA NTRS2002

Microgravity modifies protein kinase C isoform translocation in the human monocytic cell line U937 and human peripheral blood T-cells

Individual protein kinase C (PKC) isoforms fulfill distinct roles in the regulation of the commitment to differentiation, cell cycle arrest, and apoptosis in both monocytes and T-cells. The human monocyte like cell line U937 and T-cells were exposed to microgravity, during spaceflight and the translocation (a critical step in PKC signaling) of individual isoforms to cell particulate fraction examined. PKC activating phorbol esters induced a rapid translocation of several PKC isoforms to the particulate fraction of U937 monocytes under terrestrial gravity (1 g) conditions in the laboratory. In microgravity, the translocation of PKC beta II, delta, and epsilon in response to phorbol esters was reduced in microgravity compared to 1 g, but was enhanced in weak hypergravity (1.4 g). All isoforms showed a net increase in particulate PKC following phorbol ester stimulation, except PKC delta which showed a net decrease in microgravity. In T-cells, phorbol ester induced translocation of PKC delta was reduced in microgravity, compared to 1 g, while PKC beta II translocation was not significantly different at the two g-levels. These data show that microgravity differentially alters the translocation of individual PKC isoforms in monocytes and T-cells, thus providing a partial explanation for the modifications previously observed in the activation of these cell types under microgravity.

STS-81 Shuttle Project↗
NASA NTRS2004

Soluble Protein Analysis using a Compact Bench-top Flow Cytometer

Future space exploration missions will require analytical technology capable of providing both autonomous medical care to the crew and investigative capabilities to researchers. While several promising candidate technologies exist for further development, flow cytometry is an attractive technology as it offers both crew health (blood cell count, leukocyte differential, etc.) and a wide array of biochemistry and immunology assays. research settings, the application of this technique to soluble protein analysis is also possible. Proteomic beads using fluorescent dyes for optical encoding were used to monitor six cytokines simultaneously in cell medium of cell cultures in stationary and rotating cell culture systems. The results of this work demonstrate that a compact flow cytometer, such as a system proposed for space flight, can detect a variety of soluble proteins for crew health and biotechnology experiments during long-term missions.

Pappas, Dimitri↗
NASA NTRS2016

Sex-Specific Effects of Unpredictable Variable Prenatal Stress: Implications for Mammalian Developmental Programming During Spaceflight

During adaptation to the microgravity environment, adult mammals experience stress mediated by the Hypothalamic-Pituitary-Adrenal axis. In our previous studies of pregnant rats exposed to 2-g hypergravity via centrifugation, we reported decreased corticosterone and increased body mass and leptin in adult male, but not female, offspring. In this study, we utilized Unpredictable Variable Prenatal Stress to simulate the stressors of spaceflight by exposing dams to different stressors. Stress response modulation occurs via both positive and negative feedback in the hypothalamus, anterior pituitary gland, and adrenal cortex resulting in the differential release of corticosterone (CORT), a murine analog to human cortisol.

Prenatal Stress↗
NASA NTRS2019

LongRunMIP - Motivation and Design for a Large Collection of Millennial-Length AO-GCM Simulations

LongRunMIP is the first collection of millennial-length simulations of complex coupled climate models and enables investigations of how these models equilibrate in response to radiative perturbations.We present a model intercomparison project, LongRunMIP, the first collection of millennial-length (1000+ year) simulations of complex coupled climate models with a representation of ocean, atmosphere, sea ice, and land surface, and their interactions. Standard model simulations are generally only a few hundred years long. However, modeling the long-term equilibration in response to radiative forcing perturbation is important for understanding many climate phenomena, such as the evolution of ocean circulation, time-and temperature-dependent feedbacks, and the differentiation of forced signal and internal variability. The aim of LongRunMIP is to facilitate research into these questions by serving as an archive for simulations that capture as much of this equilibration as possible. The only requirement to participate in LongRunMIP is to contribute a simulation with elevated, constant CO2 forcing that lasts at least 1000 years. LongRunMIP is a MIP of opportunity in that the simulations were mostly performed prior to the conception of the archive without an agreed-upon set of experiments. For most models, the archive contains a preindustrial control simulation and simulations with an idealized (typically abrupt) CO2 forcing. We collect 2D surface and top-of-atmosphere fields, and 3D ocean temperature and salinity fields. Here, we document the collection of simulations and discuss initial results, including the evolution of surface and deep ocean temperature and cloud radiative effects. As of summer 2019, the collection includes 50 simulations of 15 models by 10 modeling centers. The data of LongRunMIP are publicly available. We encourage submission of more simulations in the future.

Maria Rugenstein↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗