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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High speed PLIF study of the Richtmyer–Meshkov instability upon re-shock

The Richtmyer–Meshkov instability (RMI) of a twice-shocked gas interface is researched using high-speed planar-laser induced fluorescence (PLIF) in the Wisconsin Shock Tube Laboratory’s vertical shock tube. The initial condition (IC) is a shear layer with broadband diffuse perturbations at the interface between a helium-acetone mixture and argon. This IC is accelerated by a shock of nominal strength M = 1.8, and then accelerated again by the transmitted shock that reflects off the end wall of the tube. An estimate of the light gas mole fraction is extracted from high-speed imaging using an iterative process that accounts for the nonlinear temperature dependence of the acetone’s fluorescence quantum yield (FQY) and absorption cross-section. A vorticity deposition model for the initial growth rate after reshock is compared with the Mikaelian model for re-shock. Previously used in literature, the number of generations is shown to naturally arise from a normalisation of the scalar transport equation. A self-similar analysis is then further performed using the mole fraction data to explore the evolution of the RMI after reshock and the higher order moments of the light gas mole fraction are compared with a proposed model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Non-Boltzmann Effects in Chain Branching and Pathway Branching for Diethyl Ether Oxidation

Low-temperature (LT) engine applications have several potential benefits, including reduced emissions and increased efficiency. Attaining these benefits requires accurate kinetic modeling of LT chain branching, which depends heavily on ketohydroperoxide (KHP) decomposition. For diethyl ether (DEE), a promising biofuel, current estimates of the KHP decomposition rate constant are largely based on empirical fits to data. In this study, we investigate the most important KHP isomer in DEE LT oxidation by applying variable reaction coordinate transition state theory to the main pathway for KHP decomposition: OO bond fission to produce •OH and a keto-alkoxy radical, •OQ'O. We also use ab initio kinetics methods to investigate the decomposition of •OQ'O, where we find dominant branching to acetic acid, with the remaining flux going to CH 3 C(O)OCHO. Additionally, new time-resolved measurements of DEE and acetic acid concentrations during LT (450–600 K) DEE oxidation are obtained in a laser photolysis flow reactor coupled with multiplexed photoionization mass spectrometry. These new experimental data, along with jet-stirred reactor data in the literature, are compared with the predictions of a recent DEE mechanism (Tran et al. Proc. Comb. Inst. 2019, 37, 511-519) that was modified with the newly calculated ab initio rate constants for KHP and •OQ'O decomposition. The predictions of the modified mechanism are quite poor when compared to the experimental data; this is primarily due to the new KHP ⇌ •OQ'O + •OH rate constant, which is 1–2 orders of magnitude slower than empirical values employed in recent mechanisms. To reconcile the new KHP rate constant and the experimental data, we explore and quantify the possible role of non-Boltzmann (nB) reaction sequences. The nB reactions have a substantial effect on both the overall mechanism reactivity and the •OQ'O branching to acetic acid. We also provide guidance on the proper implementation of nB reactions in kinetic mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Post-acquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: 1) mapping the IM axis to the LC axis for IM-MS data, 2) converting the drift time vs. m/z space to CCS/z vs m/z space, and 3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. Finally, these new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Detecting macroevolutionary genotype–phenotype associations using error-corrected rates of protein convergence

On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype–phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of non-synonymous to synonymous substitution rate ratios and developed the novel metric ω C , which measures the error-corrected convergence rate of protein evolution. While ω C distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally non-trivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype–phenotype associations, even in lineages that diverged for hundreds of millions of years.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

The turbulent cascade and proton heating in the solar wind during solar minimum

Solar wind measurements at 1 AU during the recent solar minimum and previous studies of solar maximum provide an opportunity to study the effects of the changing solar cycle on in situ heating. Our interest is to compare the levels of activity associated with turbulence and proton heating. Large-scale shears in the flow caused by transient activity are a source that drives turbulence that heats the solar wind, but as the solar cycle progresses the dynamics that drive the turbulence and heat the medium are likely to change. The application of third-moment theory to Advanced Composition Explorer (ACE) data gives the turbulent energy cascade rate which is not seen to vary with the solar cycle. Likewise, an empirical heating rate shows no significan changes in proton heating over the cycle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ''rapid-prototyping'' of new materials in silico , and provide researchers with new avenues for cost-effective, data-driven materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Asymptotic solution of light transport problems in optically thick luminescent media

We study light transport in optically thick luminescent random media. Using radiative transport theory for luminescent media and applying asymptotic and computational methods, a corrected diffusion approximation is derived with the associated boundary conditions and boundary layer solution. The accuracy of this approach is verified for a plane-parallel slab problem. In particular, the reduced system models accurately the effect of reabsorption. The impacts of varying the Stokes shift and using experimentally measured luminescence data are explored in detail. The results of this study have application to the design of luminescent solar concentrators, fluorescence medical imaging, and optical cooling using anti-Stokes fluorescence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Polarization sensitive anisotropic structuring of silicon by ultrashort light pulses

Imprinting of anisotropic structures on the silicon surface by double pulse femtosecond laser irradiation is demonstrated. The origin of the polarization-induced anisotropy is explained in terms of interaction of linearly polarized second pulse with the wavelength-sized symmetric crater-shaped structure generated by the linearly polarized first pulse. A wavefront sensor is fabricated by imprinting an array of micro-craters. Polarization controlled anisotropy of the structures can be also explored for data storage applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Revisiting galactic black hole binary GX 339-4 by using 2007 – 2014 Swift XRT observations

This work aims to study the X-ray properties of the galactic black hole binary GX 339-4. Focus of the study is on exploration of data from Swift-XRT in exclusively photon-counting mode. We use data from 2007 up to August 2014, which contain about 40 pointing observations with level 1 data. The flux of GX 339-4 varies in a factor of 100 during this period of observations. For the purpose of this work, we also try to develop a system to conduct standard SWIFT XRT data reduction automatically, in order to greatly reduce time when working with data bulk, which produces images, lightcurves as well as spectra. We also develop another system to conduct fitting of bulk spectral data with a two-component model, disk blackbody and power-law. The fitting results show that no data have a reduced chi-squared > 2. The fraction of the disk to total flux and the power-law to total flux range from 0.00389 – 0.994 and 0.00605 – 0.996, respectively. From the analysis of the disk component, we obtain the value of the innermost disk radius that does not show any large scale truncation which is in a good agreement with a previous study that used 2007 – 2011 Swift-XRT data, indicating that the systems we developed work properly.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Synergy of semiempirical models and machine learning in computational chemistry

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An experimental and computational study of thin-layer Rayleigh–Taylor instability development during deceleration with and without an externally applied magnetic field

The importance of mitigating the Rayleigh–Taylor instability (RTI) in inertial confinement fusion (ICF) is critical to successfully achieve high gain fusion yield. Consequently, understanding the seed mechanisms of RTI and the potential evolution of RTI in ICF relevant conditions is crucial. Single feature perturbations consistently demonstrate non-linear RTI evolution, for which an experimental platform on OMEGA-EP is developed. Manufacturing defects introduced into the target design require exploration of unanticipated changes to RTI development and an identification of targets that will still render quantifiable physics results. Consequently, it is presented that the inherent 3D nature of experimental targets necessitates 3D modeling for accurate design work and predictive modeling of experimental targets, especially when high resolution imaging diagnostics, like Fresnel Zone Plates, are utilized. A study of the morphology of the RTI evolution due to changing initial conditions and the presence of an externally applied magnetic field are also explored. Experimental data show thin-layer RTI morphology comparable to resistive magneto hydrodynamic 3D results. A discussion on the impacts of an externally applied magnetic field makes the case for continued efforts to observe a magnetic field's impact on RTI morphology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

We present a general method for detecting and correcting biases in the outputs of particle-tracking experiments. Our approach is based on the histogram of estimated positions within pixels, which we term the single-pixel interior filling function (SPIFF). We use the deviation of the SPIFF from a uniform distribution to test the veracity of tracking analyses from different algorithms. Unbiased SPIFFs correspond to uniform pixel filling, whereas biased ones exhibit pixel locking, in which the estimated particle positions concentrate toward the centers of pixels. Although pixel locking is a well-known phenomenon, we go beyond existing methods to show how the SPIFF can be used to correct errors. Here, the key is that the SPIFF aggregates statistical information from many single-particle images and localizations that are gathered over time or across an ensemble, and this information augments the single-particle data. We explicitly consider two cases that give rise to significant errors in estimated particle locations: undersampling the point spread function due to small emitter size and intensity overlap of proximal objects. In these situations, we show how errors in positions can be corrected essentially completely with little added computational cost. Additional situations and applications to experimental data are explored in SI Appendix. In the presence of experimental-like shot noise, the precision of the SPIFF-based correction achieves (and can even exceed) the unbiased Cramér–Rao lower bound. We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor

This study explores data-driven anomaly detection methods to analyze sensor fail- ures in the Advanced Gas Reactor (AGR) nuclear fuel irradiation experiments. Specifically, we examine failures of thermocouples (TCs), which are critical for mon- itoring and controlling in-reactor temperatures during operation. Failures were pri- marily observed during abrupt power transitions and manifested as sensor drop-outs, drifts, or unexplained behavior. We applied three time-series analysis techniques— rolling mean smoothing, matrix profile, and vector auto-regression (VAR)—to de- tect anomalies in TC data prior to failure events. The rolling mean method effec- tively highlighted deviations aligned with reported failures, while the matrix profile provided partial early warning but sometimes flagged normal fluctuations during power-down periods. VAR shows potential in capturing multivariate dependencies but requires further calibration. A rare case of TC drift was also documented, which did not result in failure, underscoring the challenge of building predictive models with sparse positive examples. Our findings demonstrate that traditional statistical tools can aid anomaly detection but have limited predictive power without richer training data. We propose future directions including synthetic data generation, real- time surrogate modeling, and multi-modal feature integration. This work provides a foundation for applying robust anomaly detection frameworks to mission-critical sensor systems in experimental settings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

ULTRAVIOLET PROPERTIES OF GALACTIC GLOBULAR CLUSTERS WITH GALEX. I. THE COLOR-MAGNITUDE DIAGRAMS

We present Galaxy Evolution Explorer (GALEX) data for 44 Galactic globular clusters (GCs) obtained during three GALEX observing cycles between 2004 and 2008. This is the largest homogeneous data set on the UV photometric properties of Galactic GCs ever collected. The sample selection and photometric analysis are discussed, and color-magnitude diagrams (CMDs) are presented. The blue and intermediate-blue horizontal branch is the dominant feature of the UV CMDs of old Galactic GCs. Our sample is large enough to display the remarkable variety of horizontal branch shapes found in old stellar populations. Other stellar types that are obviously detected are blue stragglers and post-core-He burning stars. The main features of UV CMDs of Galactic GCs are briefly discussed. We establish the locus of post-core-He burning stars in the UV CMD and present a catalog of candidate asymptotic giant branch (AGB), AGB-manque, post early-AGB, and post-AGB stars within our cluster sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The first allwise proper motion discovery: Wisea J070720.50+170532.7

While quality checking a new motion-aware co-addition of all 12.5 months of Wide-field Infrared Survey Explorer (WISE) data, we found that the source WISE J070720.48+170533.0 moved 0.''9 in six months. Backtracking this motion allowed us to identify this source as 2MASS J07071961+1705464, with several entries in the USNO B catalog. An astrometric fit to these archival data gives a proper motion of μ = 1793 ± 2 mas yr{sup –1} and a parallax of piv = 35 ± 42 mas. Photometry from WISE, 2MASS, and the POSS can be fit reasonably well by a blackbody with T = 3658 K and an angular radius of 4.36 × 10{sup –11} radians. No clear evidence of H{sub 2} collision-induced absorption is seen in the near-infrared. An optical spectrum shows broad deep CaH bands at 638 and 690 nm, broad deep Na D at 598.2 nm, and weak or absent TiO, indicating that this source is an ultra-subdwarf M star with a radial velocity v {sub rad} ≈ –21 ± 18 km s{sup –1} relative to the Sun. Given its apparent magnitude, the distance is about 39 ± 9 pc and the tangential velocity is probably ≈330 km s{sup –1}, but a more precise parallax is needed to be certain.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Three new cool brown dwarfs discovered with the wide-field infrared survey explorer (WISE) and an improved spectrum of the Y0 dwarf wise J041022.71+150248.4

As part of a larger search of Wide-field Infrared Survey Explorer (WISE) data for cool brown dwarfs with effective temperatures less than 1000 K, we present the discovery of three new cool brown dwarfs with spectral types later than T7. Using low-resolution, near-infrared spectra obtained with the NASA Infrared Telescope Facility and the Hubble Space Telescope, we derive spectral types of T9.5 for WISE J094305.98+360723.5, T8 for WISE J200050.19+362950.1, and Y0: for WISE J220905.73+271143.9. The identification of WISE J220905.73+271143.9 as a Y dwarf brings the total number of spectroscopically confirmed Y dwarfs to 17. In addition, we present an improved spectrum (i.e., higher signal-to-noise ratio) of the Y0 dwarf WISE J041022.71+150248.4 that confirms the Cushing et al. classification of Y0. Spectrophotometric distance estimates place all three new brown dwarfs at distances less than 12 pc, with WISE J200050.19+362950.1 lying at a distance of only 3.9-8.0 pc. Finally, we note that brown dwarfs like WISE J200050.19+362950.1 that lie in or near the Galactic plane offer an exciting opportunity to directly measure the mass of a brown dwarf via astrometric microlensing.

79 ASTRONOMY AND ASTROPHYSICS↗