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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Situating the Vector Density Approach Among Contemporary Continuum Theories of Dislocation Dynamics

Abstract For the past century, dislocations have been understood to be the carriers of plastic deformation in crystalline solids. However, their collective behavior is still poorly understood. Progress in understanding the collective behavior of dislocations has primarily come in one of two modes: the simulation of systems of interacting discrete dislocations and the treatment of density measures of varying complexity that are considered as continuum fields. A summary of contemporary models of continuum dislocation dynamics is presented. These include, in order of complexity, the two-dimensional statistical theory of dislocations, the field dislocation mechanics treating the total Kröner–Nye tensor, vector density approaches that treat geometrically necessary dislocations on each slip system of a crystal, and high-order theories that examine the effect of dislocation curvature and distribution over orientation. Each of theories contain common themes, including statistical closure of the kinetic dislocation transport equations and treatment of dislocation reactions such as junction formation. An emphasis is placed on how these common themes rely on closure relations obtained by analysis of discrete dislocation dynamics experiments. The outlook of these various continuum theories of dislocation motion is then discussed.

Engineering↗
NASA NTRS1962

A STATISTICAL OPTIMIZING NAVIGATION PROCEDURE FOR SPACE FLIGHT

In a typical self-contained space navigation system, celestial observation data are gathered and processed to produce estimated velocity corrections. The results of this paper provide a basis for determining the best celestial measurements and the proper time s to implement velocity corrections. Fundamental to the navigation system is a procedure for processing celestial measurement data which permits incorporation of each individual measurement as it is made in order to provide an improved estimate of position and velocity. In order to "optimize" the navigation, a statistical evaluation of a number of alternative courses of action is made. The various alternatives, which form the basis of a decision process, concern the following: 1) which star and planet combination provides the "best" available observation; 2) whether the best observation gives a sufficient reduction in the predicted target error to warrant making the measurement; and 3) whether the uncertainty in the indicated velocity correction is a small enough percentage of the correction itself to justify an engine re-start and propellant expenditure. Numerical results are presented which illustrate the effectiveness of this approach to the space navigation problem.

NAVIGATION↗
DOE OSTI2020

Phonon spectrum of underdoped HgBa 2 CuO 4+δ investigated by neutron scattering

The cuprates exhibit a prominent charge-density-wave (CDW) instability with a wave vector along [100], i.e., the Cu-O bond direction. Whereas CDW order is most prominent at moderate doping and low temperature, there exists increasing evidence for dynamic charge correlations throughout a large portion of the temperature-doping phase diagram. In particular, the signatures of incipient charge order have been observed as phonon softening and/or broadening near the CDW wave vector approximately halfway through the Brillouin zone. Most of this work is focused on moderately doped cuprates, for which the CDW order is robust, or on optimally doped samples, for which the superconducting transition temperature (T c ) attains its maximum. Here we present a time-of-flight neutron scattering study of phonons in simple-tetragonal HgBa 2 CuO 4+δ (T c = 55 K) at a low doping level where prior work showed the CDW order to be weak. We employ and showcase a new software-based technique that mines a large number of measured Brillouin zones for useful data in order to improve accuracy and counting statistics. Density-functional theory has not provided an accurate description of phonons in HgBa2CuO4+δ, yet we find the right set of parameters to qualitatively reproduce the data. The notable exception is a dispersion minimum in the longitudinal Cu-O bond-stretching branch along [100]. Here, this discrepancy suggests that, while CDW order is weak, there exist significant dynamic charge correlations in the optic phonon range at low doping, near the edge of the superconducting dome.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

Statistical fitting accuracy in photon correlation spectroscopy

Continuing our experimental investigation of the fitting accuracy associated with photon correlation spectroscopy, we collect 150 correlograms of light scattered at 90 deg from a thermostated sample of 91-nm-diameter, polystyrene latex spheres in water. The correlograms are taken with two correlators: one with linearly spaced channels and one with geometrically spaced channels. Decay rates are extracted from the single-exponential correlograms with both nonlinear least-squares fits and second-order cumulant fits. We make several statistical comparisons between the two fitting techniques and verify an earlier result that there is no sample-time dependence in the decay rate errors. We find, however, that the two fitting techniques give decay rates that differ by 1 percent.

Shaumeyer, J. N.↗
DOE OSTI2024

The Aemulus Project. VI. Emulation of Beyond-standard Galaxy Clustering Statistics to Improve Cosmological Constraints

Abstract There is untapped cosmological information in galaxy redshift surveys in the nonlinear regime. In this work, we use the Aemulus suite of cosmological N -body simulations to construct Gaussian process emulators of galaxy clustering statistics at small scales (0.1–50 h −1 Mpc) in order to constrain cosmological and galaxy bias parameters. In addition to standard statistics—the projected correlation function w p ( r p ), the redshift-space monopole of the correlation function ξ 0 ( s ), and the quadrupole ξ 2 ( s )—we emulate statistics that include information about the local environment, namely the underdensity probability function P U ( s ) and the density-marked correlation function M ( s ). This extends the model of Aemulus III for redshift-space distortions by including new statistics sensitive to galaxy assembly bias. In recovery tests, we find that the beyond-standard statistics significantly increase the constraining power on cosmological parameters of interest: including P U ( s ) and M ( s ) improves the precision of our constraints on Ω m by 27%, σ 8 by 19%, and the growth of structure parameter, f σ 8 , by 12% compared to standard statistics. We additionally find that scales below ∼6 h −1 Mpc contain as much information as larger scales. The density-sensitive statistics also contribute to constraining halo occupation distribution parameters and a flexible environment-dependent assembly bias model, which is important for extracting the small-scale cosmological information as well as understanding the galaxy–halo connection. This analysis demonstrates the potential of emulating beyond-standard clustering statistics at small scales to constrain the growth of structure as a test of cosmic acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

NASA Engineering and Safety Center Technical Bulletin No. 22-04: Uncertainty Quantification of Reduced Order Structural Dynamic Models

Uncertainty quantification (UQ) provides statistical bounds on prediction accuracy based on finite element model (FEM) uncertainty. An alternate method for UQ, called the Hybrid Parametric Variation (HPV) combines a parametric variation of the Hurty/Craig-Bampton (HCB) fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method. This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available.

Uncertainty Quantification↗
NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, A.↗
NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, Alinda↗
NASA NTRS1974

An optimized buffer controlled data compression system

The digital data compression system considered uses a buffer controlled aperture algorithm which minimizes the mean-squared error between the reconstructed receiver output and transmitter input. The data compression technique selected is based on the zero-order floating aperture prediction rule. It is assumed that the statistics of the input data are initially uniformly distributed, stationary, and first-order Markov. The problem is solved for stationary data. An approach is presented for extending the results to slowly varying uniformly distributed nonstationary Markov data.

Dosik, P. H.↗
DOE OSTI2023

Morphological analysis of the polarized synchrotron emission with WMAP and Planck

The bright polarized synchrotron emission, away from the Galactic plane, originates mostly from filamentary structures. We implement a filament finder algorithm which allows the detection of bright elongated structures in polarized intensity maps. We analyse the sky at 23 and 30 GHz as observed respectively by WMAP and Planck. We identify 19 filaments, 13 of which have been previously observed. For each filament, we study the polarization fraction, finding values typically larger than for the areas outside the filaments, excluding the Galactic plane, and a fraction of about 30% is reached in two filaments. We study the polarization spectral indices of the filaments, and find a spectral index consistent with the values found in previous analysis (about -3.1) for more diffuse regions. Decomposing the polarization signals into the E and B families, we find that most of the filaments are detected in P E , but not in P B . We then focus on understanding the statistical properties of the diffuse regions of the synchrotron emission at 23 GHz. Using Minkowski functionals and tensors, we analyse the non-Gaussianity and statistical isotropy of the polarized intensity maps. For a sky coverage corresponding to 80% of the fainter emission, and on scales smaller than 6 degrees (ℓ > 30), the deviations from Gaussianity and isotropy are significantly higher than 3σ. The level of deviation decreases for smaller scales, however, it remains significantly high for the lowest analised scale (~ 1.5°). When 60% sky coverage is analysed, we find that the deviations never exceed 3σ. Finally, we present a simple data-driven model to generate non-Gaussian and anisotropic simulations of the synchrotron polarized emission. The simulations are fitted in order to match the spectral and statistical properties of the faintest 80% sky coverage of the data maps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Phase Diagram of a Deep Potential Water Model

Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region. The computational efficiency of the model makes it possible to predict its phase diagram using molecular dynamics. Satisfactory overall agreement with experimental results is obtained. Here, the fluid phases, molecular and ionic, and all the stable ice polymorphs, ordered and disordered, are predicted correctly, with the exception of ice III and XV that are stable in experiments, but metastable in the model. The evolution of the atomic dynamics upon heating, as ice VII transforms first into ice VII" and then into an ionic fluid, reveals that molecular dissociation and breaking of the ice rules coexist with strong covalent fluctuations, explaining why only partial ionization was inferred in experiments.

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

Entropic order parameters in weakly coupled gauge theories

The entropic order parameters measure in a universal geometric way the statistics of non-local operators responsible for generalized symmetries. In this article, we compute entropic order parameters in weakly coupled gauge theories. To perform this computation, the natural route of evaluating expectation values of physical (smeared) non-local operators is prevented by known difficulties in constructing suitable smeared Wilson loops. We circumvent this problem by studying the smeared non-local class operators in the enlarged non-gauge invariant Hilbert space. This provides a generic approach for smeared operators in gauge theories and explicit formulas at weak coupling. In this approach, the Wilson and ’t Hooft loops are labeled by the full weight and co-weight lattices respectively. We study generic Lie groups and discuss couplings with matter fields. Smeared magnetic operators, as opposed to the usual infinitely thin ones, have expectation values that approach one at weak coupling. The corresponding entropic order parameter saturates to its maximum topological value, except for an exponentially small correction, which we compute. On the other hand, smeared ’t Hooft loops and their entropic disorder parameter are exponentially small. We verify that both behaviors match the certainty relation for the relative entropies. In particular, we find upper and lower bounds (that differ by a factor of 2) for the exact coefficient of the linear perimeter law for thin loops at weak coupling. This coefficient is unphysical/non-universal for line operators. We end with some comments regarding the RG flows of entropic parameters through perturbative beta functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2011

Ten Years of Cloud Properties from MODIS: Global Statistics and Use in Climate Model Evaluation

The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24,2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements between for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial . and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. In addition to these statistics, we will show trends and statistical significance in annual and seasonal means for a variety of the MODIS cloud properties, as well as the time required for detection given assumed trends. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivity; initial results will be discussed.

Platnick, Steven E.↗
NASA NTRS2003

Single-Vector Calibration of Wind-Tunnel Force Balances

An improved method of calibrating a wind-tunnel force balance involves the use of a unique load application system integrated with formal experimental design methodology. The Single-Vector Force Balance Calibration System (SVS) overcomes the productivity and accuracy limitations of prior calibration methods. A force balance is a complex structural spring element instrumented with strain gauges for measuring three orthogonal components of aerodynamic force (normal, axial, and side force) and three orthogonal components of aerodynamic torque (rolling, pitching, and yawing moments). Force balances remain as the state-of-the-art instrument that provide these measurements on a scale model of an aircraft during wind tunnel testing. Ideally, each electrical channel of the balance would respond only to its respective component of load, and it would have no response to other components of load. This is not entirely possible even though balance designs are optimized to minimize these undesirable interaction effects. Ultimately, a calibration experiment is performed to obtain the necessary data to generate a mathematical model and determine the force measurement accuracy. In order to set the independent variables of applied load for the calibration 24 NASA Tech Briefs, October 2003 experiment, a high-precision mechanical system is required. Manual deadweight systems have been in use at Langley Research Center (LaRC) since the 1940s. These simple methodologies produce high confidence results, but the process is mechanically complex and labor-intensive, requiring three to four weeks to complete. Over the past decade, automated balance calibration systems have been developed. In general, these systems were designed to automate the tedious manual calibration process resulting in an even more complex system which deteriorates load application quality. The current calibration approach relies on a one-factor-at-a-time (OFAT) methodology, where each independent variable is incremented individually throughout its full-scale range, while all other variables are held at a constant magnitude. This OFAT approach has been widely accepted because of its inherent simplicity and intuitive appeal to the balance engineer. LaRC has been conducting research in a "modern design of experiments" (MDOE) approach to force balance calibration. Formal experimental design techniques provide an integrated view to the entire calibration process covering all three major aspects of an experiment; the design of the experiment, the execution of the experiment, and the statistical analyses of the data. In order to overcome the weaknesses in the available mechanical systems and to apply formal experimental techniques, a new mechanical system was required. The SVS enables the complete calibration of a six-component force balance with a series of single force vectors.

Parker, P. A.↗
DOE OSTI2020

Uncertainties in Density and Simulation Parameters for Radiographic Reconstructions Using Machine Learning

We will develop new and improved methods that will enable a fuller and more extensive use of experimental data, such as from radiographic imaging, towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in the areas of computational imaging, statistical and machine learning, and reduced order modeling. Key new aspects of our approach include (a) a coupling of deep learning based density reconstruction with fast hydrodynamics simulators that will permit the development of certain guarantees on extrapolatability and bounds on certain other hydrodynamics-related para- metric uncertainties and (b) a dual approach towards better characterizing uncertainties in density reconstruction using deep learning based surrogates to accelerate Bayesian reconstructions on the one hand and using variational inference and Bayesian machine learning on the other hand.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Hydrodynamic and Radiographic Toolbox (HART)

With a multi-lab and university team, we propose to develop new methods for a Hydrodynamic and Radiographic Toolbox (HART) that will enable a fuller and more extensive use of experimental radiographic data towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in areas of computational imaging, statistical and machine learning, and reduced order modeling of hydrodynamics. In terms of software practices, by partnering with XCP (RISTRA Project) we will conform to recent XCP standards that are inline with modern software practices and standards and ensure compatibility and ease of inter-operability with existing codes. Three new activities under this proposal include (a) the development and use of deep learning-based surrogates to accelerate reconstruction and variational inference of density fields from radiographs of hydrotests, (b) a model-data fusion strategy that couples deep learning-based density reconstructions with fast hydrodynamics simulators to better constrain the reconstruction, and (c) a method for treating asymmetries using techniques adopted from limited view tomography. All three new activities will be based on improved treatment of scatter, noise, beam spot movement, detector blur, and flat fielding in the forward model, and a use of sophisticated priors to aid in the re construction. The improvements to the forward model and improved algorithmic design of the reconstruction when complete will be contained in the iterative reconstruction code SHIVA—a code project that we have recently initiated. The many ways in which machine learning can be used in the reconstruction work will be contained in a code HERMES that has been initiated with DTRA support. For example, the significant levels of acceleration that will likely be achieved by the use of machine learning techniques will permit us (and are required) to quantify uncertainties in density retrievals. Next, the two-way coupling between density reconstruction and model-based simulation of the hydrodynamics will be contained in code EREBUS, and will permit a fuller realization of the potential of the data to constrain the hydrodynamic model and better address issues related to asymmetries in the problem. Finally, we anticipate that the better consistency with physics achieved in our reconstructions will allow them to be used by X-Division more so than today.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Quantifying Uncertainty in PV Energy Estimates Final Report

Uncertainty in PV energy estimates is "one of the most critical areas of lack of understanding" according to independent engineers, financiers, PV model developers, and other industry stakeholders. The primary problem is a lack of rigorous, transparent, widely accepted methods for quantifying uncertainty in energy production estimates. Uncertainty in energy production estimates arises from variability of the solar resource, inexact PV performance models and their parameters, and system reliability considerations. Uncertainty in annual energy production is frequently calculated for larger projects in order to quantify financial risk. Key statistics for energy, such as the P-values "P50" and "P90" (the annual energy values that are exceeded in future years with 50\% and 90\% probability, respectively) are used by financing institutions to calculate the repayment risk for the project. The current methods to estimate these statistics are typically proprietary, specialized, and involve significant post-processing of commercial performance model results. This black-box approach leads to inconsistent P-value estimates from different parties, which reduces investors' confidence in the results. Since the financial community bases its risk assessment on these estimates, reduced confidence increases perceived project risk, and consequently financing costs. The goal of this project was to establish a set of best practices for quantifying uncertainty in energy production estimates, including identifying what sources of uncertainty must be considered with clear definitions and metrics, determining which sources are the biggest drivers of uncertainty, and providing a computationally efficient framework for combining different sources of uncertainty that is flexible enough to accommodate substitutions of data or methods when better information is available. We engaged a wide set of stakeholders to ensure industry endorsement and adoption, and leveraged complementary projects investigating individual sources of uncertainty in great detail, as well as others' work that started down this path.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗