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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

A Quantitative Analysis of Systematic Differences in the Positions and Proper Motions of Gaia DR2 with Respect to VLBI

We have analysed the differences in positions of 9081 matched sources between the GaiaData Release 2 (DR2) and very long baseline interferometry (VLBI) catalogues. The medianposition uncertainty of matched sources in the VLBI catalogue is a factor of two larger thanthe median position uncertainty in Gaia DR2. There are 9 per cent matched sources withstatistically significant offsets between both catalogues. We found that the reported positionalerrors should be rescaled by a factor of 1.3 for VLBI and 1.06 for Gaia and, in addition, the Gaiaerrors should be multiplied by the square root of chi squared per degree of freedom in order tobest fit the normalized position differences to the Rayleigh distribution. We have establishedthat the major contributor to statistically significant position offsets is the presence of opticaljets. Among the sources for which the jet direction was determined, the position offsets areparallel to the jet directions for 62 per cent of the outliers. Among the matched sources withsignificant proper motion, the fraction of objects with proper motion directions parallel tojets is a factor of three greater than on average. Such sources have systematically higher chisquared per degree of freedom. We explain these proper motions as a manifestation of thesource position jitter caused by flares, which we predicted earlier. Therefore, the assumptionthat quasars are fixed points, and thus that differential proper motions determined with respectto quasar photocentres can be regarded as absolute proper motions, should be treated withgreat caution.

Petrov, L.↗
DOE OSTI2023

An anomalous structure disordering in Zr–Cu–Ag supercooled glass-forming liquids

An anomalous ordering-to-disordering transition in a Zr–Cu–Ag supercooled glass-forming liquid has been revealed by in situ high-energy x-ray diffraction. Upon heating, the supercooled liquid exhibits a structural crossover at T c = 716 K, evident by the change in statistical characteristics of the first sharp diffraction peak. When annealed in the vicinity of T c , the supercooled liquid first relaxes from its initial state to a denser and more ordered state. Subsequently, it becomes disordered at the medium-range scale with a slowing down of densification, as demonstrated by the broadening of the first sharp diffraction peak and a decrease in the peak intensities of the higher atomic shells in the reduced pair distribution function. Finally, a scenario for the evolution of less correlated locally favored clusters in the liquid–liquid phase transition is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Correlations in cosmic density fields

A method is proposed to place constraints on the functional form of the high-order correlation functions zeta(sub n) that arise in cosmic density fields at large scales. This technique is based on a mass-in-cell statistic and a difference of mass in partitions of a cell. The relationship between these measures is sensitive to the formal structure of the zeta(sub n) as well as their amplitudes. This relationship is quantified in several theoretical models of structure, based on the hierarchical clustering paradigm. The results lead to a test for specific types of hierarchical clustering that is sensitive to correlations of all orders. The method is applied to examples of simulated large-scaled structure dominated by cold dark matter. In the preliminary study, the hierarchical paradigm appears to be a realistic approximation over a broad range of the scales. Furthermore, there is evidence that graphs of low-order vertices are dominant. On the basis of simulated data a phenomological model is specified that gives a good representation of clustering from linear scales to the strongly clustered regime (zeta(sub 2) approximately 500).

Bromley, B. C.↗
NASA NTRS1988

Radiometric performance of AVIRIS: Assessment for an arid region geologic target

Data from several AVIRIS flight lines were examined to assess instrument stability and response. Both scene and in-flight calibration data were analyzed statistically. The data clearly indicates that, although the instrument output was noisy and unstable at the time of the data acquisition, valuable spectral signatures can still be extracted and analyzed. Some first order calibration corrections can be performed by forcing internal consistency within the data. AVIRIS data are delivered in band-interleaved-by-line format, but high efficiency routines were developed which access the data as either image or spectral planes and enable effective statistical and visual examination of both AVIRIS scenes and ancillary files. Two methods were used to extract spectral information from segment 4 of the Kelso Dunes flight. Both successfully identified at least three distinct spectral signatures, but neither has positively identified a specific material.

Kieffer, Hugh H.↗
NASA NTRS2018

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a piloting an aircraft, where activities with distinct visual signatures might be things like communicating, navigating, and monitoring. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. The method is in some respects similar to recurrence analysis, but here we compare not individual fixations, but groups of fixations aggregated over a fixed time interval. The duration of this interval is a parameter that we will refer to as delta. We assume that the environment has been divided into a set of N different areas-of-interest (AOIs). For a given interval of time of duration delta, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector. These proportions can be converted to integer counts by multiplying by delta divided by the average fixation duration (another parameter that we fix at 280 milliseconds). We compare different intervals by computing the chi-square statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. The method has been applied to approximately 100 hours of eye movement data collected from pilots in a high-fidelity B747 flight simulator, and the results have been compared to synthetic data in which the each activity is represented as first-order Markov process with random probabilities assigned to the AOIs. Randomly-generated synthetic activities can require thousands of fixations to be discriminated with statistical significance, while the human data can be clustered using averaging windows of some 10's of seconds, suggesting that the actual activities are much more narrowly focused than random Markov models.

activity analysis↗
NASA NTRS2010

Sparse Superpixel Unmixing for Hyperspectral Image Analysis

Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.

Castano, Rebecca↗
DOE OSTI2021

Quantitative and interpretable order parameters for phase transitions from persistent homology

Here, we apply modern methods in computational topology to the task of discovering and characterizing phase transitions. As illustrations, we apply our method to four two-dimensional lattice spin models: the Ising, square ice, XY, and fully frustrated XY models. In particular, we use persistent homology, which computes the births and deaths of individual topological features as a coarse-graining scale or sublevel threshold is increased, to summarize multiscale and high-point correlations in a spin configuration. We employ vector representations of this information called persistence images to formulate and perform the statistical task of distinguishing phases. For the models we consider, a simple logistic regression on these images is sufficient to identify the phase transition. Interpretable order parameters are then read from the weights of the regression. This method suffices to identify magnetization, frustration, and vortex-antivortex structure as relevant features for phase transitions in our models. We also define “persistence” critical exponents and study how they are related to those critical exponents usually considered.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Leveraging observed soil heterotrophic respiration fluxes as a novel constraint on global-scale models

Microbially-explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global-scale soil heterotrophic respiration (RH), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global RH products, as well as 7,821 daily site-scale RH measurements, to evaluate the spatio-temporal performance of one first-order decay model (CASA-CNP) and two microbially-explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different climate datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the meteorological input data used to drive them. Spatial RH variability was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of RH well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise, but also needs to be improved for realistic spatio-temporal variability of RH. Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.

Jian, Jinshi↗
DOE OSTI2022

Monitoring the propagation of mechanical discontinuity using data-driven causal discovery and supervised learning

Mechanical wave transmission through a material is influenced by the mechanical discontinuity in the material. The propagation of embedded discontinuities can be monitored by analyzing the wave-transmission measurements recorded by a multipoint sensor system placed on the surface of the material. The proposed workflow monitors the propagation of mechanical discontinuity through three stages, namely initial, intermediate, and final stages, by using supervised learning followed by data-driven causal discovery. To the end, the workflow processes the multipoint waveform measurements resulting from a single impulse source, while considering the effects of wave attenuation, dispersion and multiple wave-propagation modes due to the discontinuity and material boundaries. Among various feature reduction techniques ranging from decomposition methods to manifold approximation methods, the features derived based on statistical parameterizations of the measured waveforms lead to reliable monitoring that is robust to changes in precision, resolution, and signal-to-noise ratio of the multipoint sensor measurements. The numbers of zero-crossing, negative-turning, and positive turning in the waveforms are the strongest causal signatures of the propagation of mechanical discontinuity. Higher order moments of the waveforms, such as variance, skewness and kurtosis, are also strong causal signatures of the propagation. Finally, the newly discovered causal signatures confirm that the statistical correlations and conventional feature rankings are not always statistically significant indicators of causality.

42 ENGINEERING↗
DOE OSTI2025

Verification of the Uniformly-Ordered Binary Decision Algorithm in Correlated-Benchmark Whisper Calculations

Whisper is a nuclear criticality safety code package that aids analysts in validation exercises by computing upper subcritical limits (USL) for applications of interest. To obtain statistically meaningful, significant, and conservative USLs, the analyst must ensure that Whisper selects a sufficient number of benchmarks that are neutronically similar to the application. Many of the available benchmarks are correlated but are currently treated as independent, leading to an artificially small sample size, as their individual information contributions will be overestimated. To aid the analyst in obtaining a sufficient sample size, prior work [2] demonstrated application of the Uniformly-Ordered Binary Decision (UOBD) algorithm in adjusting benchmark weights to account for benchmark correlations. This work provides verification of the Whisper implementation and considers the impact of updated benchmark correlations compared to those available previously. We demonstrate that the UOBD algorithm performs as expected with an analytic example. With HEU-SOL-THERM-001 cases 1 through 10 as the applications, we compare the USLs computed with benchmark correlations available in the Whisper 1.1 release only to those computed with additional benchmark correlations from DICE 2023 and demonstrate substantive differences.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2021

Flow and interferometry results from Au + Au collisions at $\sqrt{s_{NN}}$ = 4.5 GeV

In this study, the beam energy scan (BES) program at the Relativistic Heavy Ion Collider (RHIC) was extended to energies below $\sqrt{s_{NN}}$ = 7.7 GeV in 2015 by successful implementation of the fixed-target mode of operation in the STAR (Solenoidal Tracker At RHIC) experiment. In this mode, ions circulate in one ring of the collider and interact with a stationary target at the entrance of the STAR Time Projection Chamber. The first results for Au + Au collisions at $\sqrt{s_{NN}}$ = 4.5 GeV are presented, demonstrating good performance of all the relevant detector subsystems in fixed-target mode. Results presented here include directed and elliptic flow of identified hadrons, and radii from pion femtoscopy. The latter, together with recent HADES results, reveal a long-sought peak structure that may be caused by the system evolving through a first-order phase transition from quark-gluon plasma to the hadronic phase. Directed and elliptic flow for pions are presented for the first time at this beam energy. Pion and proton elliptic flow show behavior which hints at constituent quark scaling, and demonstrate that a definitive conclusion will be achievable using the full statistics of the on-going second phase of BES (BES-II). In particular, BES-II to date has recorded fixed-target data sets with two orders of magnitude more events at each of nine energies between $\sqrt{s_{NN}}$ = 3.0 and 7.7 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Characterization and differentiation of aluminum powders used in improvised explosive devices. Part 2: Micromorphometric method refinement and preliminary statistical analysis

Abstract Aluminum (Al) powder is commonly encountered in improvised explosive devices (IEDs) as a metallic fuel due to its availability and low cost. Although available commercially in powder form, amateur bomb‐makers also produce their own Al powder via simple methods found online. In order to provide investigative leads and forensic intelligence, it is important to evaluate not only the composition of homemade devices, but also to distinguish between the various forms of Al powder they contain. To achieve this goal, a method using automated microscopy in combination with statistical techniques has been demonstrated to have the potential to provide source discrimination and investigative leads in source attribution of Al powders in IEDs. The present research refined this method and investigated 59 industrially and amateurly produced Al powder sources with seven subsamples per source using two traditional linear discriminant analyses (LDA), one with a standard data split for training and testing, and another using leave‐one‐out cross‐validation. Averaging the classification accuracies for the two LDA‐based analyses, LDA has the ability to correctly classify 59.26%, 83.35%, and 80.69% of the samples based on their powder source, type, and production method, respectively. This classification accuracy represents a 3407%, 317%, and 61.38% increase in accuracy from random class assignment, respectively. Further, in most instances of incorrect data attribution to a particular source, the subsample has been misidentified with another sample of the same powder type or production method.

Ommen, Danica M.↗
NASA NTRS2013

Application of Statistically Derived CPAS Parachute Parameters

The Capsule Parachute Assembly System (CPAS) Analysis Team is responsible for determining parachute inflation parameters and dispersions that are ultimately used in verifying system requirements. A model memo is internally released semi-annually documenting parachute inflation and other key parameters reconstructed from flight test data. Dispersion probability distributions published in previous versions of the model memo were uniform because insufficient data were available for determination of statistical based distributions. Uniform distributions do not accurately represent the expected distributions since extreme parameter values are just as likely to occur as the nominal value. CPAS has taken incremental steps to move away from uniform distributions. Model Memo version 9 (MMv9) made the first use of non-uniform dispersions, but only for the reefing cutter timing, for which a large number of sample was available. In order to maximize the utility of the available flight test data, clusters of parachutes were reconstructed individually starting with Model Memo version 10. This allowed for statistical assessment for steady-state drag area (CDS) and parachute inflation parameters such as the canopy fill distance (n), profile shape exponent (expopen), over-inflation factor (C(sub k)), and ramp-down time (t(sub k)) distributions. Built-in MATLAB distributions were applied to the histograms, and parameters such as scale (sigma) and location (mu) were output. Engineering judgment was used to determine the "best fit" distribution based on the test data. Results include normal, log normal, and uniform (where available data remains insufficient) fits of nominal and failure (loss of parachute and skipped stage) cases for all CPAS parachutes. This paper discusses the uniform methodology that was previously used, the process and result of the statistical assessment, how the dispersions were incorporated into Monte Carlo analyses, and the application of the distributions in trajectory benchmark testing assessments with parachute inflation parameters, drag area, and reefing cutter timing used by CPAS.

Romero, Leah M.↗
DOE OSTI2025

Probing the limits of statistical neutron capture for the r process: Experimental constraints on 141 Cs nuclear level densities

The r-process abundance peaks, particularly near mass number A ∼ 130, reflect underlying nuclear structure effects such as closed neutron shells, yet modeling the nucleosynthesis in this region remains hindered by uncertain neutron-capture rates. These rates are especially sensitive to nuclear level densities (NLDs) and γ-ray strength functions of neutron-rich nuclei, where experimental data are scarce. We present the first experimental constraint on the NLD of 141 Cs using the β-Oslo method, extending sensitivity to the neutron-rich regime near the N = 82 closed shell. Our data allow for critical calibration of microscopic NLD models and reveal that 141 Cs lies near the limit of statistical model applicability. Using this experimental input, we evaluate radiative neutron-capture rates across neighboring isotones using both Hauser–Feshbach (HF) and High Fidelity Resonance (HFR) models. Our results show order-of-magnitude rate increases for nuclei along the N = 86 line, signaling a transition to resonance-dominated capture in this region. These findings underscore the importance of constraining NLDs to improve r-process reaction network predictions, particularly in environments where the validity of statistical models breaks down.

Nuclear level density↗
NASA NTRS1983

Higher-order derivative correlations and the alignment of small-scale structures in isotropic numerical turbulence

In a three dimensional simulation higher order derivative correlations, including skewness and flatness factors, are calculated for velocity and passive scalar fields and are compared with structures in the flow. The equations are forced to maintain steady state turbulence and collect statistics. It is found that the scalar derivative flatness increases much faster with Reynolds number than the velocity derivative flatness, and the velocity and mixed derivative skewness do not increase with Reynolds number. Separate exponents are found for the various fourth order velocity derivative correlations, with the vorticity flatness exponent the largest. Three dimensional graphics show strong alignment between the vorticity, rate of strain, and scalar-gradient fields. The vorticity is concentrated in tubes with the scalar gradient and the largest principal rate of strain aligned perpendicular to the tubes. Velocity spectra, in Kolmogorov variables, collapse to a single curve and a short minus 5/3 spectral regime is observed.

Kerr, R. A.↗
DOE OSTI2023

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Cobalt Carbonyl Clusters Enable Independent Control of Two Proton Transfer Rates in the Mechanism for Hydrogen Evolution

Abstract Metal carbonyl clusters (MCC's) are atomically defined nanomaterials which can be characterized using the precise tools of molecular (electro)chemistry. HER mechanisms involve two proton transfer (PT) steps in the catalytic cycle. For HER catalyzed by [Co 11 C 2 (CO) 23 ] 3− ( 1 3− ), cyclic voltammetry measurements were used to determine the rate for PT1 as k PT1 =3×10 8 M −1 s −1 , whereas the rate for PT2 is k PT2 =3.7×10 3 M −1 s −1 . The fast, diffusion‐limited rate for PT1 is consistent with a previous report describing [Co 13 C 2 (CO) 24 ] 4− ( 2 4− ), with PT=2.9×10 9 M −1 s −1 . In both cases rate enhancement in PT1 is promoted by the many Co−Co bonds on the surface of the MCC that serve as PT sites: a statistical enhancement of rate akin to the effects of proton relays. In contrast, k PT2 varies and is five orders of magnitude slower for 1 2− compared with 2 4− . Thus, MCC's and nanomaterials offer an opportunity to enhance the rate for PT1 while maintaining thermochemical or kinetic control of PT2.

Pattanayak, Santanu↗