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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evolution of in-medium baryon-baryon scattering cross sections and stiffness of dense nuclear matter from Bayesian analyses of FOPI proton-flow excitation functions

Within a Bayesian statistical framework using a Gaussian process (GP) emulator for an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions with momentum-independent Skyrme interactions, we infer from the proton directed and elliptical flow in mid-central Au + Au reactions, measured at beam energies from 150 to 1200 MeV/nucleon by the FOPI Collaboration, the posterior probability distribution functions (PDFs) of the in-medium baryon-baryon scattering cross section (BBSCS) modification factor 𝑋 (with respect to their free-space values) and the stiffness parameter 𝐾 of dense nuclear matter. Here, we find that the most probable value of 𝑋 evolves from around 0.7 to 1.0 as the beam energy 𝐸 beam /𝐴 increases. On the other hand, the posterior PDF⁡(𝐾) may have dual peaks having roughly the same height or extended shoulders at high 𝐾 values. More quantitatively, the posterior PDF⁡(𝐾) changes from having a major peak around 220 MeV, characterizing a soft EOS in the reaction at 𝐸 beam /𝐴 = 150 MeV, to one that peaks around 320 MeV, indicating a stiff EOS in the reactions at 𝐸 beam /𝐴 higher than about 600 MeV. The transition from soft to stiff happens in mid-central Au + Au reactions at beam energies around 250 MeV/nucleon in which 𝐾 = 220 MeV and 𝐾 = 320 MeV are approximately equally probable. Altogether, the FOPI proton-flow excitation function data indicate a gradual hardening of hot and dense nuclear matter as its density and temperature increase in reactions with higher beam energies.

nuclear matter↗
DOE OSTI2020

Dealing with Measurement Uncertainties as Nuisance Parameters in Bayesian Model Calibration

In the presence of model discrepancy, the calibration of physics-based models for physical parameter inference is a challenging problem. Lack of identifiability between calibration parameters and model discrepancy requires additional identifiability constraints to be placed on the model discrepancy to obtain unique physical parameter estimates. If these assumptions are violated, the inference for the calibration parameters can be systematically biased. In many applications, such as in dynamic material property experiments, many of the calibration inputs refer to measurement uncertainties. Here, we develop a metric for identifying overfitting of these measurement uncertainties, propose a prior capable of reducing this overfitting, and show how this leads to a diagnostic tool for validation of physical parameter inference. The approach is demonstrated for a benchmark example and applied for a material property application to perform inference on the equation of state parameters of tantalum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Low-redshift Lyman Continuum Survey. II. New Insights into LyC Diagnostics

The Lyman continuum (LyC) cannot be observed at the epoch of reionization (z ≳ 6) owing to intergalactic H I absorption. To identify LyC emitters (LCEs) and infer the fraction of escaping LyC, astronomers have developed various indirect diagnostics of LyC escape. Using measurements of the LyC from the Low-redshift Lyman Continuum Survey (LzLCS), we present the first statistical test of these diagnostics. While optical depth indicators based on Lyα, such as peak velocity separation and equivalent width, perform well, we also find that other diagnostics, such as the [O III ]/[O II ] flux ratio and star formation rate surface density, predict whether a galaxy is an LCE. The relationship between these galaxy properties and the fraction of escaping LyC flux suggests that LyC escape depends strongly on H I column density, ionization parameter, and stellar feedback. We find that LCEs occupy a range of stellar masses, metallicities, star formation histories, and ionization parameters, which may indicate episodic and/or different physical causes of LyC escape.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1976

Coincident observations of ionospheric troughs and the equatorial plasmapause

Electron-density observations made in the topside ionosphere by the Ariel 4 and Isis 2 satellites are examined in conjunction with results obtained by Explorer 45 when it traversed the near-equatorial plasmapause with one hour (both UT and MLT) of the Ariel and Isis traversals of the same L coordinate. Both dusk and night observations are analyzed, and an attempt is made to show that depressions in ionospheric electron density occur in the vicinity of the plasmapause field line. It is concluded that the electron distributions observed in the electron-density troughs at 550 km near dusk by Ariel and at 1400 km near midnight by Isis do not always parallel variations in the light-ion distribution inferred from the Explorer plasmapause traversals and that there appears to be no specific feature of the main ionospheric trough which can be used to identify the plasmapause field line except in a statistical sense.

Grebowsky, J. M.↗
DOE OSTI2020

Inference offers a metric to constrain dynamical models of neutrino flavor transformation

The multimessenger astrophysics of compact objects presents a vast range of environments where neutrino flavor transformation may occur and may be important for nucleosynthesis, dynamics, and a detected neutrino signal. Development of efficient techniques for surveying flavor evolution solution spaces in these diverse environments, which augment and complement existing sophisticated computational tools, could leverage progress in this field. To this end we continue our exploration of statistical data assimilation (SDA) to identify solutions to a small-scale model of neutrino flavor transformation. SDA is a machine learning formula wherein a dynamical model is assumed to generate any measured quantities. Specifically, we use an optimization formulation of SDA wherein a cost function is extremized via the variational method. Regions of state space in which the extremization identifies the global minimum of the cost function will correspond to parameter regimes in which a model solution can exist. Our example study seeks to infer the flavor transformation histories of two monoenergetic neutrino beams coherently interacting with each other and with a matter background. We require that the solution be consistent with measured neutrino flavor fluxes at the point of detection, and with constraints placed upon the flavor content at various locations along their trajectories, such as the point of emission, and the locations of the Mikheyev-Smirnov-Wolfenstein resonances. We show how the procedure efficiently identifies solution regimes and rules out regimes where solutions are infeasible. Overall, results in this work intimate the promise of this “variational annealing” methodology to efficiently probe an array of fundamental questions that traditional numerical simulation codes render difficult to access.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Estimation of process steady state with autoregressive models and Bayesian inference

To improve efficiency, separations engineers will typically design process circuits containing recirculating streams, which mix one or more of the process outputs with the feed material. Doing so can improve efficiency, but will cause a delay in the system reaching steady state conditions until the recirculating load mass flows stabilize. In testing separation circuits, engineers will often test a variety of factors and complete an analysis from sample results. Knowledge of if a process is at steady state, as well as the steady state conditions of a process, is essential for a valid techno-economic analysis. However, the definition of process steady state is often poorly defined, or does not include uncertainty quantification. If the performance of a process operating under two different sets of conditions are compared, an engineer who does not test for steady state or quantify steady state conditions risks producing a faulty analysis. In this work, a Bayesian statistical method for testing if all streams are at steady state is further motivated and then derived. Then after testing for steady state, the same model is used with a prior distribution that enforces a steady state assumption to estimate steady state conditions. Further, these methods were validated in a solvent extraction pilot plant where steady state conditions for all outflows were inferred with uncertainty quantification. Analysis is completed with functions available to the reader as part of the BayesMassBal (V 1.1.0) software package written in R.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Further adoption of conservation tillage can increase maize yields in the western US Corn Belt

Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Does the emission measure decrease during the start of a soft X-ray flare

It is suggested that the apparent emission dip found in soft X-ray flare observations, interpreted as the emission from a single temperature plasma, is an artifact. According to this hypothesis, the flare phenomenon represents a combination of flare plus a stable active region plasma. A small amount of hot flare plasma will dominate the total emission, and the calculated isothermal plasma parameters will trend towards those of the hot flare plasma, so that a decrease in emission measure will be inferred. It is claimed that observed data fit the model of a stable plasma plus a flare plasma better than it does the model of a single plasma, in accordance with a chi square statistic. The model of stable plasma plus flare plasma is also supported by the observation that the plasma parameters in the postflare condition are indistinguishable from those during preflare.

Wolfson, C. J.↗
NASA NTRS1991

An Exploratory Exercise in Taguchi Analysis of Design Parameters: Application to a Shuttle-to-space Station Automated Approach Control System

The chief goals of the summer project have been twofold - first, for my host group and myself to learn as much of the working details of Taguchi analysis as possible in the time allotted, and, secondly, to apply the methodology to a design problem with the intention of establishing a preliminary set of near-optimal (in the sense of producing a desired response) design parameter values from among a large number of candidate factor combinations. The selected problem is concerned with determining design factor settings for an automated approach program which is to have the capability of guiding the Shuttle into the docking port of the Space Station under controlled conditions so as to meet and/or optimize certain target criteria. The candidate design parameters under study were glide path (i.e., approach) angle, path intercept and approach gains, and minimum impulse bit mode (a parameter which defines how Shuttle jets shall be fired). Several performance criteria were of concern: terminal relative velocity at the instant the two spacecraft are mated; docking offset; number of Shuttle jet firings in certain specified directions (of interest due to possible plume impingement on the Station's solar arrays), and total RCS (a measure of the energy expended in performing the approach/docking maneuver). In the material discussed here, we have focused on single performance criteria - total RCS. An analysis of the possibility of employing a multiobjective function composed of a weighted sum of the various individual criteria has been undertaken, but is, at this writing, incomplete. Results from the Taguchi statistical analysis indicate that only three of the original four posited factors are significant in affecting RCS response. A comparison of model simulation output (via Monte Carlo) with predictions based on estimated factor effects inferred through the Taguchi experiment array data suggested acceptable or close agreement between the two except at the predicted optimum point, where a difference outside a rule-of-thumb bound was observed. We have concluded that there is most likely an interaction effect not provided for in the original orthogonal array selected as the basis for our experimental design. However, we feel that the data indicates that this interaction is a mild one and that inclusion of its effect will not alter the location of the optimum.

Deal, Don E.↗
NASA NTRS1994

The Galactic far-ultraviolet sky as seen by Faust: Modeling and observations

We test the model of the Galactic point source sky recently extended to the far-ultraviolet by Cohen (1994) against the set of Galactic far-ultraviolet point sources detected by the Far Ultraviolet Space Telescope (FAUST) experiment. We find excellent agreement between the predicted (log N, log S) and the observed source counts for a range of Galactic directions observed by Faust. At low galactic latitudes, we can directly infer the amount of interstellar extinction in a given direction associated with local molecular complexes, based on comparison of model calculations and FAUST star counts. The model can also be used to predict the statistical stellar content of any field. We compare the model predcitions with the observations of Brosch et al. (1994), who have spectroscopically studied an optically identified complete sample of FAUST objects in a region near the north Galactic pole. We have used the model to determine that the contribution of far-ultraviolet stars fainter than 6 x 10(exp -14) ergs/s/sq cm/A (the FAUST detection limit) is less than 1% of the diffuse sky brightness, and less than about 4% of the total point source flux.

Cohen, Martin↗
DOE OSTI2022

Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems

Abstract Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform physics-based models for systems with few observable degrees of freedom; e.g. hydrological systems. Here, we provide an operator-theoretic explanation for this empirical success. To predict a partially-observed system’s future behavior with physics-based models, the missing degrees of freedom must be explicitly accounted for using data assimilation and model parametrization. Data-driven models, in contrast, employ delay-coordinate embeddings and their evolution under the Koopman operator to implicitly model the effects of the missing degrees of freedom. We describe in detail the statistical physics of partial observations underlying data-driven models using novel maximum entropy and maximum caliber measures. The resulting nonequilibrium Wiener projections applied to the Mori–Zwanzig formalism reveal how data-driven models may converge to the true dynamics of the observable degrees of freedom. Additionally, this framework shows how data-driven models infer the effects of unobserved degrees of freedom implicitly, in much the same way that physics models infer the effects explicitly. This provides a unified implicit-explicit modeling framework for predicting partially-observed systems, with hybrid physics-informed machine learning methods combining both implicit and explicit aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bayesian model-data comparison incorporating theoretical uncertainties

Accurate comparisons between theoretical models and experimental data are critical for scientific progress. However, inferred physical model parameters can vary significantly with the chosen physics model, highlighting the importance of properly accounting for theoretical uncertainties. In this Letter, we present a Bayesian framework that explicitly quantifies these uncertainties by statistically modeling theory errors, guided by qualitative knowledge of a theory’s varying reliability across the input domain. We demonstrate the effectiveness of this approach using two systems: a simple ball drop experiment and multi-stage heavy-ion simulations. In both cases incorporating model discrepancy leads to improved parameter estimates, with systematic improvements observed as additional experimental observables are integrated.

Bayesian methods↗
DOE OSTI2024

Cosmology with persistent homology: a Fisher forecast

Abstract Persistent homology naturally addresses the multi-scale topological characteristics of the large-scale structure as a distribution of clusters, loops, and voids. We apply this tool to the dark matter halo catalogs from theQuijotesimulations, and build a summary statistic for comparison with the joint power spectrum and bispectrum statistic regarding their information content on cosmological parameters and primordial non-Gaussianity. Through a Fisher analysis, we find that constraints from persistent homology are tighter for 8 out of the 10 parameters by margins of 13–50%. The complementarity of the two statistics breaks parameter degeneracies, allowing for a further gain in constraining power when combined. We run a series of consistency checks to consolidate our results, and conclude that our findings motivate incorporating persistent homology into inference pipelines for cosmological survey data.

Astronomy & Astrophysics↗
NASA NTRS1976

The development of a model to infer precipitation from microwave measurements

To permit the inference of precipitation amounts from radiometric measurements, a radiative interaction model was developed. This model uses a simple computational scheme to determine the effects of rain upon brightness temperatures and can be used with a statistical inversion procedure to invert for rain rate. Precipitating cloud models was also developed and used with the microwave model for frequencies of 19.35 and 37 GHz to determine the variability of the microwave-rain rate relationship on a global and seasonal basis.

Fowler, M. G.↗
NASA NTRS1996

On the Relationship Between Transit Velocity of Interplanetary Shocks and Solar Active Processes

Recently, it was reported that preferential relationships exist between the transit velocity V(sub T) of earthward-directed interplanetary shocks and solar active processes, in particular, eruptive filaments outside active regions (the size of the erupting filament L(sub f)) and solar flares (the value of the X-ray characteristic J). Unfortunately, statistical testing of the proposed associations was not accomplished, nor was the 'geo-effectiveness' of the events adequately described. Reported here are the results of a re-examination of the 21 eruptive filaments (SSC-EF events) and 26 X-ray flares (SSC-F events) that have been associated with storm sudden commencements (SSCs) at Earth. Simple statistical testing refutes the claim that a preferential relationship exists between V(sub T) and L(sub F), while it supports the claim that one exists between V(sub T) and J. More importantly, the inferred relationship between V(sub T) and J is found to be more complicated than previously thought. In particular, it now appears that SSC-F events may be separable into two groups, based on the value of J: a low-J group (J less than 56), in which V(sub T) varies directly with J, and a high-J group (J greater than 56), in which V(sub T) varies inversely with J. As a whole, high-J events are associated with shocks of higher average transit velocity than those of low-J events, and SSC-F events with shocks of higher average transit velocity than those of SSC-EF events. Further, high-J events tend to be of greater X-ray class ( greater than M3), longer duration (greater then 80 min), and are more likely to be associated with type II/IV radio emission (9 of 12) than low-J events. They also tend to occur in magnetically complex (gamma/delta configuration) active regions (10 of 12) that are large in area extent (area greater than 445 millionths of a solar hemisphere) on the day of flaring (9 of 12). Of the 9 solar proton events that affected the Earth's environment that were found to be associated with SSC-F events, six were high-J events. Concerning 'geo-effectiveness', there appears to be no preferential relationship between the value of the J-parameter and the most negative value of the Dst geomagnetic index Dst(min) following the SSC, which is found to usually occur at 6-14 h after SSC onset (18 of 26) and which ranged in value from -1 to -249 (having a median value of about -75). Of the 26 SSC-F events, only 14 can be associated with a Dst(min) less than or equal to -75, and of these only 7 were high-J events. Of the 14 storm-related events (i.e. Dst(min) less than or equal to -75), three have previously been identified as being either 'magnetic clouds' or 'bidirectional flows', both manifestations of earthward-directed coronal mass ejections (CMEs). Superposed epoch analyses of selected solar wind parameters and Dst during the interval of storm-related SSC-F events demonstrate that geoeffective SSC-F events tend to be associated with solar wind flows that are faster, greater in magnetic field strength, and have a rotating field which has a strong southward component shortly after SSC onset, in comparison to SSC-F events that do not have Dst(min) less than or equal to 75. Therefore, it is inferred that geoeffective SSC-F events are probably fast earthward-directed CMEs. Although no single parameter is found that can serve as a predictor of high-skill level for determining the geoeffectiveness of an SSC-F event prior to its occurrence at Earth, one finds that knowledge of the flare's hemispheric location and appearance or lack of appearance of a two-ribbon structure is sufficient to correctly predict the geoeffectiveness of 20 out of 25 of the SSC-F events (80%). Surprisingly, the association or lack of association of metric type II/IV radio emission as a characteristic for determining the geoeffectiveness of the SSC-F events proved unfruitful, as did, to a lesser extent, the duration of the X-ray emission.

Wilson, Robert M.↗
NASA NTRS2007

Toward the characterization of upper tropospheric clouds using Atmospheric Infrared Sounder and Microwave Limb Sounder observations

We estimate the accuracy of cloud top altitude (Z) retrievals from the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) observing suite (ZA) on board the Earth Observing System Aqua platform. We compare ZA with coincident measurements of Z derived from the micropulse lidar and millimeter wave cloud radar at the Atmospheric Radiation Measurement (ARM) program sites of Nauru and Manus islands (ZARM) and the inferred Z from vertically resolved Microwave Limb Sounder (MLS) ice water content (IWC) retrievals. The mean difference in ZA minus ZARM plus or minus one standard deviation ranges from −2.2 to 1.6 km ± 1.0 to 4.2 km for all cases of AIRS effective cloud fraction (f A) > 0.15 at Manus Island using the cloud radar only. The range of mean values results from using different approaches to determine ZARM, day/night differences, and the magnitude of f A; the variation about the mean decreases for increasing values of f A. Analysis of ZARM from the micropulse lidar at Nauru Island for cases restricted to 0.05 ≤ f A ≤ 0.15 indicates a statistically significant improvement in ZA − ZARM over the cloud radar-derived values at Manus Island. In these cases the ZA − ZARM difference is −1.1 to 2.1 km ± 3.0 to 4.5 km. These results imply that the operational ZA is quantitatively useful for constraining cirrus altitude despite the nominal 45 km horizontal resolution. Mean differences of cloud top pressure (PCLD) inferred from coincident AIRS and MLS ice water content (IWC) retrievals depend upon the method of defining AIRS PCLD (as with the ARM comparisons) over the MLS spatial scale, the peak altitude and maximum value of MLS IWC, and f A. AIRS and MLS yield similar vertical frequency distributions when comparisons are limited to f A > 0.1 and IWC > 1.0 mg m−3. Therefore the agreement depends upon the opacity of the cloud, with decreased agreement for optically tenuous clouds. Further, the mean difference and standard deviation of AIRS and MLS PCLD are highly dependent on the MLS tangent altitude. For MLS tangent altitudes greater than 146 hPa, the strength of the limb technique, the disagreement becomes statistically significant. This implies that AIRS and MLS “agree” in a statistical sense at lower tangent altitudes and “disagree” at higher tangent altitudes. These results provide important insights on upper tropospheric cloudiness as observed by nadir-viewing AIRS and limb-viewing MLS.

Wu, Dong L.↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗