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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
NASA NTRS1972

Signal sequence detection given noisy, common background image sets.

The optimum processing (likelihood functional) is found for a set of M images, each the sum of a member of a signal sequence due to an object to be detected and its parameters estimated, a sample function of a noise field, and a sample function of a common background field. The noise fields are independent, zero mean, white Gaussian fields, all independent of the background field. The latter is assumed to be either (1) completely unknown or of known mean and covariance functions with (2) a certain fluctuation property or (3) Gaussian. Three equivalent forms of the optimum processing are found: (1) a summation of generalized matched filterings of the images, (2) a summation of matched filtering of certain generalized differences of the images, and (3) a summation of 'estimator-correlator' type filterings. The detection performance and optimum signal/image selection under the Neyman-Pearson criterion is given, and is shown that optimum processor and signal design can completely eliminate any effect of the background on detectability.

Harger, R. O.↗
NASA NTRS2007

Propagation of Computational Uncertainty Using the Modern Design of Experiments

This paper describes the use of formally designed experiments to aid in the error analysis of a computational experiment. A method is described by which the underlying code is approximated with relatively low-order polynomial graduating functions represented by truncated Taylor series approximations to the true underlying response function. A resource-minimal approach is outlined by which such graduating functions can be estimated from a minimum number of case runs of the underlying computational code. Certain practical considerations are discussed, including ways and means of coping with high-order response functions. The distributional properties of prediction residuals are presented and discussed. A practical method is presented for quantifying that component of the prediction uncertainty of a computational code that can be attributed to imperfect knowledge of independent variable levels. This method is illustrated with a recent assessment of uncertainty in computational estimates of Space Shuttle thermal and structural reentry loads attributable to ice and foam debris impact on ascent.

DeLoach, Richard↗
NASA NTRS1994

Generation of electrostatic waves as a consequence of chaotic ion acceleration in the neutral sheet

The stability of ion distributions with velocity space holes caused by chaotic ion acceleration in the vicinity of the field reversal region in the Earth's magnetotail is investigated. It is shown that such distributions are unstable to electrostatic waves with wave vector directed along the Sun-Earth line. The solution of the quasi-linear equation shows that the saturation of the instability occurs due to velocity space trapping of the low-energy background ions and results in a flattening of the reduced ion distribution function in the region of the original velocity space hole. The energy level of the excited waves in the vicinity of the Earth's midplane is estimated as a function of the initial size of the empty velocity space region.

Burinskaya, T.↗
NASA NTRS1995

On treatments of fetch and stability sensitivity in large-area estimates of sensible heat flux over sea ice

Bulk transfer coefficients estimated as a function of atmospheric stability and sea-ice lead width are combined with Arctic meteorological observations and ice thickness data to calculate the sensitivity of turbulent flux estimates to changes in lead width, wind speed, air temperature, and ice thickness for a high-concentration ice pack. These results are considered in terms of bulk transfer parameterizations that use a fixed transfer coefficient r that address atmospheric stability only. On the basis of the fetch-sensitive parameterizations considered here, differences in lead width for widths up to about 200 m can exert a substantial influence on sensible heat transfer coefficients and heat flux from leads under typical Arctic conditions. Fluxes from an open water lead decrease by 34% if fetch increases from 10 m to 100 m. This effect is greatest for open water leads, decreases considerably as leads refreeze, and is negligible for ice thicker than about 0.3 m. If open or newly refrozen leads make up 2% of the ice cover, than an increase in mean fetch from 10 m to 100 m yields a decrease of about 2 W/sq m in areally averaged flux from the ice pack. Calculations using observed and theoretical lead width distributions suggest that parameterizing lead widths in a sea ice model can be done effectively using a single, representative lead width rather than requiring a full distribution of widths. When coupled to the lowest atmospheric boundary layer using a bulk similarity theory model, this sensitivity of heat transfer to fetch results in substantially higher near-surface air temperatures over narrow leads, with equilibrium air temperatures decreasing by about 50% as fetch increases from 10 to 100 m.

Maslanik, J. A.↗
DOE OSTI2025

Electronic structure and exchange interactions in altermagnetic MnGeP 2 in the quasiparticle self-consistent G W approach

The quasiparticle self-consistent 𝐺⁡𝑊 method is used to study the electronic band structure, optical dielectric function, and exchange interactions in chalcopyrite, 𝐼⁢ ‾ 4 ⁢2⁢𝑑, structure MnGeP 2 . The material is found to be an antiferromagnetic semiconductor with a lowest direct gap of 2.44 eV at the Γ point and a lower indirect gap of 1.87 eV from Γ to 𝑀. The material is an altermagnet because the two magnetic atoms of opposite spin are related by a twofold rotation operation perpendicular to the main fourfold rotation inversion axis. The spin splittings along a low symmetry line like 𝑃⁢𝑁 is sizable while at k points on the diagonal mirror planes or on the twofold symmetry axes the spin splitting is zero. The exchange interactions are calculated using a linear response approach. The antiferromagnetic exchange interaction between nearest neighbors in the primitive unit cell is dominating and found to be slightly decreasing upon carrier doping but not sufficiently to change the interaction to become ferromagnetic. The bare (noninteracting) and interacting transverse spin susceptibilities, which provide interatomic site exchange interactions after averaging over the muffin-tin spheres, are calculated from the 𝐺⁡𝑊 band structure and wave functions. From these exchange interactions, the spin wave spectra are obtained along the high symmetry lines and the Néel temperature is calculated using the mean-field and Tyablikov estimations. The dielectric function and the optical absorption spectra are calculated including excitonic effects using the Bethe Salpeter equation. The exchange interactions around Mn Ge defect sites is also studied. While we find it can generate ferromagnetic interactions with neighboring spins, we did not find direct evidence of producing an overall ferromagnetic phase. First, if Mn antisites are introduced by exchanging Mn with a nearby Ge, the interactions stay largely antiferromagnetic. Second, when we add additional Mn, in other words in Mn-rich stoichiometry, the Mn Ge antisites produce a strong ferromagnetic interaction primarily with the Mn in the same basal plane but weaker ferromagnetic interaction with adjacent plane Mn. The interactions between regular lattice Mn stay antiferromagnetic as before and thus favor keeping the antiferromagnetic order along the [001] direction. Adding Mn antisites, however, does lead to a metallic band structure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Anisotropy of the Surface Energy and Work Function of IIB Metals

On the basis of electron-statistical method of calculation of the surface energy of metals, a technique for estimating the electron work function of hexagonal and rhombohedral metallic crystals has been developed. This technique relates surface energy to work function and can thus be applied to estimate the surface energy of crystal faces from experimental data for a work function. Computations have been made for cadmium, zinc, and mercury macrocrystals. The temperature and orientation dependences of both quantities have been constructed. Our results are in good agreement with literature experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Thermosteam: BioSTEAM's Premier Thermodynamic Engine

Thermosteam is a thermodynamic engine capable of solving mass and energy balances, estimating mixture properties, solving thermodynamic phase equilibria, and modeling stoichiometric reactions. All chemical data in Thermosteam is imported from the chemicals library, an open-source compilation of data and functions for the estimation of pure component chemical and mixture properties. Thermosteam’s fast and flexible platform has enabled the evaluation of conceptual and emerging biochemical production processes. The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) — capable of modeling reactors, distillation columns, heat exchangers, and other unit operations — has adopted Thermosteam as its premier thermodynamic engine. Published biorefinery designs modeled in BioSTEAM implement thermodynamic property packages created with Thermosteam, including a cornstover biorefinery for the production of cellulosic ethanol, a lipid-cane biorefinery for the co-production of ethanol and biodiesel, and a wheatstraw biorefinery for the production of cellulosic ethanol.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

The augmented Lagrangian method for parameter estimation in elliptic systems

In this paper a new technique for the estimation of parameters in elliptic partial differential equations is developed. It is a hybrid method combining the output-least-squares and the equation error method. The new method is realized by an augmented Lagrangian formulation, and convergence as well as rate of convergence proofs are provided. Technically the critical step is the verification of a coercivity estimate of an appropriately defined Lagrangian functional. To obtain this coercivity estimate a seminorm regularization technique is used.

Ito, Kazufumi↗
NASA NTRS2010

Model Effects on GLAS-Based Regional Estimates of Forest Biomass and Carbon

Ice, Cloud, and land Elevation Satellite (ICESat) / Geosciences Laser Altimeter System (GLAS) waveform data are used to estimate biomass and carbon on a 1.27 X 10(exp 6) square km study area in the Province of Quebec, Canada, below the tree line. The same input datasets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include non-stratified and stratified versions of a multiple linear model where either biomass or (biomass)(exp 0.5) serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial dry biomass estimates of up to 0.35 G, with a range of 4.94 +/- 0.28 Gt to 5.29 +/-0.36 Gt. The differences among model estimates are statistically non-significant, however, and the results demonstrate the degree to which carbon estimates vary strictly as a function of the model used to estimate regional biomass. Results also indicate that GLAS measurements become problematic with respect to height and biomass retrievals in the boreal forest when biomass values fall below 20 t/ha and when GLAS 75th percentile heights fall below 7 m.

Nelson, Ross F.↗
DOE OSTI2014

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Contributions of Shear and Turbulence to Cloud Overlap for Cumulus Clouds

The causes of vertical cloud overlap, the ratio between the cloud fraction by area and the cloud fraction by volume, are studied for fields of cumulus convection. Using large-eddy simulations, the structure of individual clouds within the cloud field is inspected and connected to the overall cloud cover of the entire cloud field. First, it is shown that the cloud overlap of the entire field can be well approximated by the total cloud overlap of the individual clouds. It is then shown that a simple maximum overlap assumption, which is commonly used for cloud fields with small layer depth, can only account for roughly half of the overlap inefficiency. This is due to the fact that maximum overlap only represents variations in cloud width as a function of depth but does not take other factors into account, most notably shear or turbulence. The effects of turbulence and wind shear on the overlap of individual clouds are first estimated as a function of cloud depth, which closes the discrepancy between observed cloud overlap and the maximum overlap assumption. Then, an empirical model is developed for the shear and turbulence contributions on overlap as a function of cloud depth and of synoptic conditions. Finally, it is shown that our model is capable of accurately predicting overlap for a large number of cloud fields and under a variety of meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Improving Carbon Estimation of Large Tropical Trees by Linking Airborne Lidar Crown Size to Field Inventory

Quantifying tropical aboveground biomass (agb) is an outstanding challenge that requires knowledge on the 3D structure of forests. Recent studies suggest that the uncertainty in estimating agb of large trees is significantly reduced if tree height and crown size are accounted for in addition to the traditional trunk diameter and wood density. Due to the fact that field inventory techniques are not adapted to characterize the 3D forest structure, crown size metrics (e.g. height and radius) are commonly estimated as a function of trunk diameter using allometric models with limitations in explaining crown variability. Airborne lidar techniques have the potential for characterizing tree height and crown size but are not adapted to estimate trunk diameter, which is a strong predictor of agb. Here, we investigate the synergy of field inventory and airborne lidar techniques to characterize the forest structure by assessing the uncertainty introduced by the field-based allometric models in the estimation of agb at the tree-level. We focus in 1454 large individual trees (trunk diameter > 60 cm) located within the La Selva Biological Station for which we dispose of field observations (trunk diameter and wood density) and lidar derived metrics (tree height and crown radius). We show that the field-based allometric models overestimate tree height and underestimate crown radius. As a result, the allometric approach overestimates the tree-level agb in 0.8 Mg when considering the 1454 individuals and the errors can reach more than 50% of the agb of individual trees. These errors on the large trees agb highly impact on the plot-level results and then propagate to the estimation of carbon stocks at the regional and national-levels.

Clark, David↗
NASA NTRS2013

Analysis of a Split-Plot Experimental Design Applied to a Low-Speed Wind Tunnel Investigation

A procedure to analyze a split-plot experimental design featuring two input factors, two levels of randomization, and two error structures in a low-speed wind tunnel investigation of a small-scale model of a fighter airplane configuration is described in this report. Standard commercially-available statistical software was used to analyze the test results obtained in a randomization-restricted environment often encountered in wind tunnel testing. The input factors were differential horizontal stabilizer incidence and the angle of attack. The response variables were the aerodynamic coefficients of lift, drag, and pitching moment. Using split-plot terminology, the whole plot, or difficult-to-change, factor was the differential horizontal stabilizer incidence, and the subplot, or easy-to-change, factor was the angle of attack. The whole plot and subplot factors were both tested at three levels. Degrees of freedom for the whole plot error were provided by replication in the form of three blocks, or replicates, which were intended to simulate three consecutive days of wind tunnel facility operation. The analysis was conducted in three stages, which yielded the estimated mean squares, multiple regression function coefficients, and corresponding tests of significance for all individual terms at the whole plot and subplot levels for the three aerodynamic response variables. The estimated regression functions included main effects and two-factor interaction for the lift coefficient, main effects, two-factor interaction, and quadratic effects for the drag coefficient, and only main effects for the pitching moment coefficient.

Erickson, Gary E.↗
DOE OSTI2014

(Sr,Ba)(Si,Ge){sub 2} for thin-film solar-cell applications: First-principles study

In order to meet the increasing demand for electric power generation from solar energy conversion, the development of efficient light absorber materials has been awaited. To this end, the electronic and optical properties of advanced alkaline-earth-metals disilicides and digermanides (SrSi{sub 2}, BaSi{sub 2}, SrGe{sub 2}, and BaGe{sub 2}) are studied by means of the density functional theory using HSE06 exchange-correlation energy functional. Our calculations show that all these orthorhombic structured compounds have fundamental indirect band gaps in the range E{sub g} ≈ 0.89–1.25 eV, which is suitable for solar cell applications. The estimated lattice parameters and band gaps are in good agreement with experiments. Our calculations show that the electronic band structures of all four compounds are very similar except in the vicinity of the Γ-point. The valence band of these compounds is made up by Si(Ge)-p states, whereas the conduction band is composed of Sr(Ba)-d states. Their band alignments are carefully determined by estimating the work function of each compound using slab model. The optical properties are discussed in terms of the complex dielectric function ε(ω) = ε{sub 1}(ω) + iε{sub 2}(ω). The static and high-frequency dielectric constants are calculated, taking into account the ionic contribution. The absorption coefficient α(ω) demonstrates that a low energy dispersion of the conduction band, which results in a flat conduction band minimum, leads to large optical activity in these compounds. Therefore, alkaline-earth-metals disilicides and digermanides possess great potential as light absorbers for applications in thin-film solar cell technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1986

Robust detection, isolation and accommodation for sensor failures

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Emami-Naeini, A.↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
NASA NTRS2021

Relationship between Horizontal Wind Velocity and Normalized Surface Cross Section Using Data from the HIWRAP Dual-Frequency Airborne Radar

The High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dual-frequency conically scanning airborne radar provides estimates of the range-profiled mean Doppler and backscattered power from the precipitation and surface. A velocity–azimuth display analysis yields near-surface estimates of the mean horizontal wind vector υh in cases in which precipitation is present throughout the scan. From the surface return, the normalized radar cross section (NRCS) is obtained, which, by a method previously described, can be corrected for path attenuation. Comparisons between υh and the attenuation-corrected NRCS are used to derive transfer functions that provide estimates of the wind vector from the NRCS data under both rain and rain-free conditions. A reasonably robust transfer function is found by using the mean NRCS (⟨NRCS⟩) over the scan along with a filtering of the data based on a Fourier series analysis of υh and the NRCS. The approach gives good correlation coefficients between υh and ⟨NRCS⟩ at Ku band at incidence angles of 30° and 40°. The correlation degrades if the Ka-band data are used rather than the Ku band.

R Meneghini↗
NASA NTRS2000

Reconstruction algorithm for polychromatic CT imaging: application to beam hardening correction

This paper presents a new reconstruction algorithm for both single- and dual-energy computed tomography (CT) imaging. By incorporating the polychromatic characteristics of the X-ray beam into the reconstruction process, the algorithm is capable of eliminating beam hardening artifacts. The single energy version of the algorithm assumes that each voxel in the scan field can be expressed as a mixture of two known substances, for example, a mixture of trabecular bone and marrow, or a mixture of fat and flesh. These assumptions are easily satisfied in a quantitative computed tomography (QCT) setting. We have compared our algorithm to three commonly used single-energy correction techniques. Experimental results show that our algorithm is much more robust and accurate. We have also shown that QCT measurements obtained using our algorithm are five times more accurate than that from current QCT systems (using calibration). The dual-energy mode does not require any prior knowledge of the object in the scan field, and can be used to estimate the attenuation coefficient function of unknown materials. We have tested the dual-energy setup to obtain an accurate estimate for the attenuation coefficient function of K2 HPO4 solution.

NASA Center ARC↗