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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
NASA NTRS1989

Hypersonic rarefied-flow aerodynamics inferred from Shuttle Orbiter acceleration measurements

Data obtained from multiple flights of sensitive accelerometers on the Space Shuttle Orbiter during reentry have been used to develop an improved aerodynamic model for the Orbiter normal- and axial-force coefficients in hypersonic rarefied flow. The lack of simultaneous atmospheric density measurements was overcome in part by using the ratio of normal-to-axial acceleration, in which density cancels, as a constraint. Differences between the preflight model and the flight-acceleration-derived model in the continuum regime are attributed primarily to real gas effects. New insights are gained into the variation of the force coefficients in the transition between the continuum regime and free molecule flow.

Blanchard, R. C.↗
NASA NTRS1991

Complex magmatic processes on Mars - Inferences from the SNC meteorites

Published data on the elemental and isotopic abundances in the shergottites-nakhlites-Chassigny (SNC) meteorites, considered to be of Martian origin, are compared with those for eucritic, lunar, and terrestrial basalt samples, with a focus on their implications for magmatic processes in the parent bodies. The major elements, the REEs and isotopes, and the other lithophile incompatible elements (such as high-field-strength elements, HFSEs) are discussed separately, and it is concluded that Mars had a magmatic history significantly different from that of the other bodies. The Martian pattern of HFSE and REE anomalies suggests extraction of carbonatic melts and remelting of the depleted source material, while the Nd isotopic constraints on the melting of Nakhla indicate very high fractionation of REEs, requiring exceedingly efficient porous flow down to depths of over 350 km.

Longhi, J.↗
NASA NTRS1997

Europa's differentiated internal structure: inferences from two Galileo encounters

Doppler data generated with the Galileo spacecraft's radio carrier wave during two Europa encounters on 19 December 1996 (E4) and 20 February 1997 (E6) were used to measure Europa's external gravitational field. The measurements indicate that Europa has a predominantly water ice-liquid outer shell about 100 to 200 kilometers thick and a deep interior with a density in excess of about 4000 kilograms per cubic meter. The deep interior could be a mixture of metal and rock or it could consist of a metal core with a radius about 40 percent of Europa's radius surrounded by a rock mantle with a density of 3000 to 3500 kilograms per cubic meter. The metallic core is favored if Europa has a magnetic field.

Flight Experiment↗
NASA NTRS1997

Europa's Differentiated Internal Structure: Inferences from Two Galileo Encounters

Doppler data generated with the Galileo spacecraft's radio carrier wave during two Europa encounters on 19 December 1996 (E4) and 20 February 1997 (E6) were used to measure Euroapa's external gravitational field. The measurements indicate that Europa has a predominantly water ice-liquid outer shell about 100 to 200 klilometers thick and a deep interior with a density in excess of about 4000 kilograms per cubic meter.

Galileo↗
DOE OSTI2024

Applications of emulation and Bayesian methods in heavy-ion physics

Abstract Heavy-ion collisions provide a window into the properties of many-body systems of deconfined quarks and gluons. Understanding the collective properties of quarks and gluons is possible by comparing models of heavy-ion collisions to measurements of the distribution of particles produced at the end of the collisions. These model-to-data comparisons are extremely challenging, however, because of the complexity of the models, the large amount of experimental data, and their uncertainties. Bayesian inference provides a rigorous statistical framework to constrain the properties of nuclear matter by systematically comparing models and measurements. This review covers model emulation and Bayesian methods as applied to model-to-data comparisons in heavy-ion collisions. Replacing the model outputs (observables) with Gaussian process emulators is key to the Bayesian approach currently used in the field, and both current uses of emulators and related recent developments are reviewed. The general principles of Bayesian inference are then discussed along with other Bayesian methods, followed by a systematic comparison of seven recent Bayesian analyses that studied quark-gluon plasma properties, such as the shear and bulk viscosities. The latter comparison is used to illustrate sources of differences in analyses, and what it can teach us for future studies.

Paquet, Jean-François (ORCID:0000000187368171)↗
DOE OSTI2023

Quintessential cosmological tensions

Several cosmological tensions have emerged in light of recent data, most notably in the inferences of the parameters and . We explore the possibility of alleviating both these tensions simultaneously by means of the Albrecht-Skordis “quintessence” potential. The field can reduce the size of the sound horizon while concurrently suppressing the power in matter density fluctuations before it comes to dominate the energy density budget today. Interestingly, this rich set of dynamics is governed entirely by one free parameter that is of in Planck units. We find that the inferred value of can be increased, while that of can be decreased, both by compared to the case. However, ultimately the model is disfavored by Planck and BAO data alone, compared to the standard model, with a . When including large scale structure and supernova data we find . We note that historically much attention has been focused on preserving the three angular scales , , and to their values. Our work presents an example of how, while doing so indeed maintains a relatively good fit to the cosmic microwave background data for an increased number of ultrarelativistic species, it is a priori insufficient in maintaining such a fit in more general model spaces.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Workshop on Dust on Mars

A workshop was organized to stimulate and coordinate research on the properties of Martian dust, its distribution on the planet, and the processes affecting its yearly deposition, erosion, and transport. A great deal of research was carried out to understand local, regional, and global aspects of the Martian dust transport cycle, with the goal being definition of the currently active processes and their relation to those operating over the long term. To date, however, most individual research projects have centered around interpretation of single data sets (visual images, IRTM, radar, etc.), and these many efforts were not coordinated. Many of the details of short and long term dust-related processes, as inferred from the various single data sets, are still not agreed upon. It was the intent of this workshop to encourage the informal exchange of ideas among several researchers currently investigating various aspects of the problem and to investigate cooperative efforts to synthesize the individual approaches into a unified understanding of the present seasonal and yearly Martian sediment transport cycles.

Lee, S.↗
DOE OSTI2020

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗
DOE OSTI2020

Framework For Spatial Agricultural Crop Yield Prediction Model Development

This framework was developed to provide data preprocessing for spatiotemporal agricultural yield data and remote sensing data for modelling using artificial neural networks (ANNs) to predict subfield crop yield estimates. The software includes methods to train, validate, and test ANN models. It also include methods to infer on new remote sensing data.

Griffel, LloydM.↗
NASA NTRS2014

Comparison of the Sensitivity of Surface Downward Longwave Radiation to Changes in Water Vapor at Two High Elevation Sites

Among the potential reasons for enhanced warming rates in many high elevation regions is the nonlinear relationship between surface downward longwave radiation (DLR) and specific humidity (q). In this study we use ground-based observations at two neighboring high elevation sites in Southwestern Colorado that have different local topography and are 1.3 kilometers apart horizontally and 348 meters vertically. We examine the spatial consistency of the sensitivities (partial derivatives) of DLR with respect to changes in q, and the sensitivities are obtained from the Jacobian matrix of a neural network analysis. Although the relationship between DLR and q is the same at both sites, the sensitivities are higher when q is smaller, which occurs more frequently at the higher elevation site. There is a distinct hourly distribution in the sensitivities at both sites especially for high sensitivity cases, although the range is greater at the lower elevation site. The hourly distribution of the sensitivities relates to that of q. Under clear skies during daytime, q is similar between the two sites, however under cloudy skies or at night, it is not. This means that the DLR-q sensitivities are similar at the two sites during daytime but not at night, and care must be exercised when using data from one site to infer the impact of water vapor feedbacks at another site, particularly at night. Our analysis suggests that care should be exercised when using the lapse rate adjustment to infill high frequency data in a complex topographical region, particularly when one of the stations is subject to cold air pooling as found here.

elevation↗
DOE OSTI2023

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Abstract Connecting observations of core-collapse supernova explosions to the properties of their massive star progenitors is a long-sought, and challenging, goal of supernova science. Recently, Barker et al. presented bolometric light curves for a landscape of progenitors from spherically symmetric neutrino-driven core-collapse supernova (CCSN) simulations using an effective model. They find a tight relationship between the plateau luminosity of the Type II-P CCSN light curve and the terminal iron-core mass of the progenitor. Remarkably, this allows us to constrain progenitor properties with photometry alone. We analyze a large observational sample of Type II-P CCSN light curves and estimate a distribution of iron-core masses using the relationship of Barker et al. The inferred distribution matches extremely well with the distribution of iron-core masses from stellar evolutionary models and namely, contains high-mass iron cores that suggest contributions from very massive progenitors in the observational data. We use this distribution of iron-core masses to infer minimum and maximum masses of progenitors in the observational data. Using Bayesian inference methods to locate optimal initial mass function parameters, we find M min = 9.8 − 0.27 + 0.37 and M max = 24.0 − 1.9 + 3.9 solar masses for the observational data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Scaling Inference Using Triton to Accelerate Particle Physics at the LHC and DUNE

DUNE and the LHC experiments consist of unique and cutting-edge particle detectors that create massive, complex, and rich datasets with billions of events. They require sophisticated algorithms to reconstruct and interpret the data. Modern machine learning algorithms provide a powerful toolset to detect and classify particles, from familiar image processing convolutional neural networks to newer graph neural network architectures. A full reconstruction of these particle collisions requires novel approaches to handle the computing challenge of processing so much raw data. In a series of studies, physicists from Fermilab, CERN, and university groups explored how to accelerate their data processing using the Triton Inference Server.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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↗
NASA NTRS1993

An expert system shell for inferring vegetation characteristics

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this contract, an interface to a file of unknown cover type data was constructed. An interface that allowed the results of VEG to be written to a file was also implemented. A learning system that learned class descriptions from a data base of historical cover type data and then used the learned class descriptions to classify an unknown sample was built. This system had an interface that integrated it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER was completed and a number of additional techniques that inferred the proportion ground cover of a sample were implemented. This work was previously described. The work carried out in the second year of the contract is described. The historical cover type database was removed from VEG and stored as a series of flat files that are external to VEG. An interface to the files was provided. The framework and interface for two new VEG subgoals that estimate the atmospheric effect on reflectance data were built. A new interface that allows the scientist to add techniques to VEG without assistance from the developer was designed and implemented. A prototype Help System that allows the user to get more information about each screen in the VEG interface was also added to VEG.

Harrison, P. Ann↗
NASA NTRS1983

Inference of the aerosol Angstrom coefficient from SAGE short-wavelength data

SAGE four-channel transmission profiles are inverted to retrieve the extinction profiles from which the aerosol Angstrom coefficient alpha is obtained. The procedure allows one to check the influence of the NO2 absorption profile, which is small below 25 km. The results compare well with those obtained by a completely different procedure at NASA Langley Research Center, and the main features of the alpha profiles seem to be significant, even considering the rather large error bars. The relation between the retrieved Angstrom coefficient, the particle effective radius and the asymmetry factor is considered.

Lenoble, J.↗
NASA NTRS1991

Inference of surface power spectra from inversion of multifrequency polarimetric radar data

During the summer of 1988 an intensive field experiment was conducted in the vicinity of Pisgah lava field in the Mojave Desert. As part of the experiment, physical properties such as microtopography, composition, soil moisture, and dielectric constant at five different sites representing surfaces with rms heights varying from less than one centimeter to tens of centimeters were measured. In addition, polarimetric radar images at P-band, L-band and C-band were acquired at three different incidence angles with the NASA/JPL airborne imaging radar polarimeter. Using trihedral corner reflectors deployed in the area prior to imaging, the radar images were calibrated to provide values for each resolution element in each scene. This paper reports on the derivation of the power spectrum of surface microtopography by solution of the small perturbation model for multiple incidence angle and multiple frequency radar data. Power-law fits to the power spectra have exponents that are nearly the same for all surfaces. These values are close to those from measured microtopography profiles.

Van Zyl, Jacob J.↗