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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 55 records · Page 3
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NASA NTRS2014

A Corrected Formulation of the Multilayer Model (MLM) for Inferring Gaseous Dry Deposition to Vegetated Surfaces

The Multilayer Model (MLM) has been used for many years to infer dry deposition fluxes from measured trace species concentrations and standard meteorological measurements for national networks in the U.S., including the U.S. Environmental Protection Agency's Clean Air Status and Trends Network (CASTNet). MLM utilizes a resistance analogy to calculate deposition velocities appropriate for whole vegetative canopies, while employing a multilayer integration to account for vertically varying meteorology, canopy morphology and radiative transfer within the canopy. However, the MLM formulation, as it was originally presented and as it has been subsequently employed, contains a non-physical representation related to the leaf-level quasi-laminar boundary layer resistance that affects the calculation of the total canopy resistance. In this note, the non-physical representation of the canopy resistance as originally formulated in MLM is discussed and a revised, physically consistent, formulation is suggested as a replacement. The revised canopy resistance formulation reduces estimates of HNO3 deposition velocities by as much as 38% during mid-day as compared to values generated by the original formulation. Inferred deposition velocities for SO2 and O3 are not significantly altered by the change in formulation (less than 3%). Inferred deposition loadings of oxidized and total nitrogen from CASTNet data may be reduced by 10-20% and 5-10%, respectively, for the Eastern U. S. when employing the revised formulation of MLM as compared to the original formulation.

vegetated surfaces↗
NASA NTRS2016

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Uncertainty↗
NASA NTRS2017

On Tidal Inference in the Diurnal Band

Standard methods of tidal inference should be revised to account for a known resonance that occurs mostly within the K(sub 1) tidal group in the diurnal band. The resonance arises from a free rotational mode of Earth caused by the fluid core. In a set of 110 bottom-pressure tide stations, the amplitude of the P(sub 1) tidal constituent is shown to be suppressed relative to K(sub 1), which is in good agreement with the resonance theory. Standard formulas for the K(sub 1) nodal modulation remain essentially unaffected. Two examples are given of applications of the refined inference methodology: one with monthly tide gauge data and one with satellite altimetry. For some altimeter-constrained tide models, an inferred P(sub 1) constituent is found to be more accurate than a directly determined one.

Ray, R. D.↗
NASA NTRS2017

A Framework for Inferring Taxonomic Class of Asteroids.

Introduction: Taxonomic classification of asteroids based on their visible / near-infrared spectra or multi band photometry has proven to be a useful tool to infer other properties about asteroids. Meteorite analogs have been identified for several taxonomic classes, permitting detailed inference about asteroid composition. Trends have been identified between taxonomy and measured asteroid density. Thanks to NEOWise (Near-Earth-Object Wide-field Infrared Survey Explorer) and Spitzer (Spitzer Space Telescope), approximately twice as many asteroids have measured albedos than the number with taxonomic classifications. (If one only considers spectroscopically determined classifications, the ratio is greater than 40.) We present a Bayesian framework that provides probabilistic estimates of the taxonomic class of an asteroid based on its albedo. Although probabilistic estimates of taxonomic classes are not a replacement for spectroscopic or photometric determinations, they can be a useful tool for identifying objects for further study or for asteroid threat assessment models. Inputs and Framework: The framework relies upon two inputs: the expected fraction of each taxonomic class in the population and the albedo distribution of each class. Luckily, numerous authors have addressed both of these questions. For example, the taxonomic distribution by number, surface area and mass of the main belt has been estimated and a diameter limited estimate of fractional abundances of the near earth asteroid population was made. Similarly, the albedo distributions for taxonomic classes have been estimated for the combined main belt and NEA (Near Earth Asteroid) populations in different taxonomic systems and for the NEA population specifically. The framework utilizes a Bayesian inference appropriate for categorical data. The population fractions provide the prior while the albedo distributions allow calculation of the likelihood an albedo measurement is consistent with a given taxonomic class. These inputs allows calculation of the probability an asteroid with a specified albedo belongs to any given taxonomic class.

Asteroids↗
NASA NTRS2017

BrO and Inferred Bry Profiles over the Western Pacific: Relevance of Inorganic Bromine Sources and a Bry Minimum in the Aged Tropical Tropopause Layer

We report measurements of bromine monoxide (BrO) and use an observationally constrained chemical box model to infer total gas-phase inorganic bromine (Br(sub y)) over the tropical western Pacific Ocean (tWPO) during the CONTRAST field campaign (January-February 2014). The observed BrO and inferred Bry profiles peak in the marine boundary layer (MBL), suggesting the need for a bromine source from sea-salt aerosol (SSA), in addition to organic bromine (CBry ). Both profiles are found to be C-shaped with local maxima in the upper free troposphere (FT). The median tropospheric BrO vertical column density (VCD) was measured as 1.6 x 10(exp 13) molec cm(exp -2), compared to model predictions of 0.9 x 10(exp 13) molec cm(exp -2) in GEOS-Chem (CBr(sub y) but no SSA source), 0.4 x 10(exp 13) molec cm(exp -2) in CAM-Chem (CBr(sub y) and SSA), and 2.1 x 10(exp 13) molec cm(exp -2) in GEOS-Chem (CBry and SSA). Neither global model fully captures the Cshape of the Br(sun y) profile. A local Br(sub y) maximum of 3.6 ppt (2.9-4.4 ppt; 95% confidence interval, CI) is inferred between 9.5 and 13.5 km in air masses influenced by recent convective outflow. Unlike BrO, which increases from the convective tropical tropopause layer (TTL) to the aged TTL, gas-phase Br(sub y) decreases from the convective TTL to the aged TTL. Analysis of gas-phase Br(sub y) against multiple tracers (CFC-11, H2O/O3 ratio, and potential temperature) reveals a Br(sub y) minimum of 2.7 ppt (2.3-3.1 ppt; 95% CI) in the aged TTL, which agrees closely with a stratospheric injection of 2.6 +/- 0.6 ppt of inorganic Br(sub y) (estimated from CFC-11 correlations), and is remarkably insensitive to assumptions about heterogeneous chemistry. Bry increases to 6.3 ppt (5.6-7.0 ppt; 95% CI) in the stratospheric "middleworld" and 6.9 ppt (6.5-7.3 ppt; 95% CI) in the stratospheric "overworld". The local Br(sub y) minimum in the aged TTL is qualitatively (but not quantitatively) captured by CAM-Chem, and suggests a more complex partitioning of gas-phase and aerosol Br(sub y) species than previously recognized. Our data provide corroborating evidence that inorganic bromine sources (e.g., SSA-derived gas-phase Br(sub y) ) are needed to explain the gas-phase Br(sub y) budget in the upper free troposphere and TTL. They are also consistent with observations of significant bromide in Upper Troposphere-Lower Stratosphere aerosols. The total Br(sub y) budget in the TTL is currently not closed, because of the lack of concurrent quantitative measurements of gas-phase Br(sub y) species (i.e., BrO, HOBr, HBr, etc.) and aerosol bromide. Such simultaneous measurements are needed to (1) quantify SSA-derived Br(sub y) in the upper FT, (2) test Br(sub y) partitioning, and possibly explain the gas-phase Br(sub y) minimum in the aged TTL, (3) constrain heterogeneous reaction rates of bromine, and (4) account for all of the sources of Br(sub y) to the lower stratosphere.

Atmospheric Chemistry; bromine; troposphere; CONTR↗
NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗
NASA NTRS2019

Exploiting OMI NO2 Satellite Observations to Infer Fossil-fuel CO2 Emissions from U.S. Megacities

Fossil-fuel CO2 emissions and their trends in eight U.S. megacities during 2006–2017 are inferred by combining satellite-derived NOX emissions with bottom-up city-specific NOX-to-CO2 emission ratios. A statistical model is fit to a collection NO2 plumes observed from the Ozone Monitoring Instrument (OMI), and is used to calculate top-down NOX emissions. Decreases in OMI-derived NOX emissions are observed across the eight cities from 2006 to 2017 (−17% in Miami to −58% in Los Angeles), and are generally consistent with long-term trends of bottom-up inventories (−25% in Miami to −49% in Los Angeles), but there are some interannual discrepancies. City-specific NOX-to-CO2 emission ratios, used to calculate inferred CO2, are estimated through annual bottom-up inventories of NOX and CO2 emissions disaggregated to 1 × 1 km2 resolution. Over the study period, NOX-to-CO2 emission ratios have decreased by ~40% nationwide (−24% to −51% for our studied cities), which is attributed to a faster reduction in NOX when compared to CO2 due to policy regulations and fuel type shifts. Combining top-down NOX emissions and bottom-up NOX-to-CO2 emission ratios, annual fossil-fuel CO2 emissions are derived. Inferred OMI-based top-down CO2 emissions trends vary between +7% in Dallas to −31% in Phoenix. For 2017, we report annual fossil-fuel CO2 emissions to be: Los Angeles 113 ± 49 Tg/yr; New York City 144 ± 62 Tg/yr; and Chicago 55 ± 24 Tg/yr. A study in the Los Angeles area, using independent methods, reported a 2013–2016 average CO2 emissions rate of 104 Tg/yr and 120 Tg/yr, which suggests that the CO2 emissions from our method are in good agreement with other studies' top-down estimates. We anticipate future remote sensing instruments – with better spatial and temporal resolution – will better constrain the NOX-to-CO2 ratio and reduce the uncertainty in our method.

Ozone Monitoring Instrument (OMI)↗
NASA NTRSDate not supplied

Mantle Source Mineralogy Inferred from Incompatible Element Ratios in Shergottites

The mineralogy of the martian mantle is a poorly known but vital compositional parameter. In the absence of samples of martian mantle peridotites, the mineralogy has been inferred from bulk composition constrained by a mix of cosmochemical and martian meteoritic constraints. Such a mineralogy reflects the primordial mantle composition, but mantle source regions that differentiated from a magma ocean are not likely to have the same mineralogy as bulk silicate Mars. An alternative approach is to infer source mineralogy of the martian mantle from the analysis of incompatible elements in shergottites. In a recent study, we showed that the general compatibility order of trace lithophile elements in martian mantle is similar to that of MORB mantle, with some exceptions. Compared to MORB mantle, in martian mantle: (1) highly incompatible elements (Th, Nb, Ta and U) are less incompatible relative to La; (2) K is significantly more compatible with respect to other elements; and (3) the HREEs are more compatible. In this study, we calculated effective partition coefficients of trace elements directly from martian igneous meteorites and used this data to infer the source mineralogy of the martian mantle.

Mars↗
NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗
NASA NTRS2022

Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical-Argo Floats and the Carbon-Based Productivity Model

Estimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite-based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon-based productivity model (CbPM) to compute vertically-resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float-based, depth-resolved, bio-optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float-based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth-resolved structure in NPP from bio-optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth-reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float-informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically- integrated NPP, where the mean bias between model implementations in depth-integrated productivity south of 30◦S is reduced by 62 % when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.

NPP↗
NASA NTRS2017

Modular Spectral Inference Framework Applied to Young Stars and Brown Dwarfs

In practice, synthetic spectral models are imperfect, causing inaccurate estimates of stellar parameters. Using forward modeling and statistical inference, we derive accurate stellar parameters for a given observed spectrum by emulating a grid of precomputed spectra to track uncertainties. Spectral inference as applied to brown dwarfs re: Synthetic spectral models (Marley et al 1996 and 2014) via the newest grid spans a massive multi-dimensional grid applied to IGRINS spectra, improving atmospheric models for JWST. When applied to young stars(10Myr) with large starpots, they can be measured spectroscopically, especially in the near-IR with IGRINS.

K↗
NASA NTRS1972

Inferring the interplanetary magnetic field by observing the polar geomagnetic field.

Svalgaard (1968, 1972) and Mansurov (1969) have shown that it is possible to infer the polarity of the interplanetary magnetic field quite reliably from observations of the diurnal variation of polar geomagnetic fields. The effect is most prominent in the vertical component of geomagnetic observatories near the geomagnetic poles during several hours near noon. The interplanetary magnetic field observed with spacecraft near the earth is very similar to the mean solar magnetic field (i.e., the sun observed as though it were a star); thus the fact that observations of the polar geomagnetic field have existed without interruption since 1926 at the Danish Meteorological Institute station at Godhavn, Greenland, means that in effect the inferred solar magnetic field during five sunspot cycles is available for analysis.-

Wilcox, J. M.↗
NASA NTRS1973

Inference of total ozone from photometric measurements of sky radiation.

Ground-based photometric measurements of spectral sky radiation have been made using a simple filter instrument. Sky radiation intensities measured in the solar vertical at 3200 and 3600 A are compared to infer total ozone. A model of a multiple scattering Rayleigh atmosphere serves as a primary reduction parameter. Spectral measurements of all-sky radiance distribution are used to study the effects of haze and clouds on the inference of total ozone. The brightness distribution of clear and overcast sky in ultraviolet is also described.

Ghazi, A.↗
NASA NTRS1976

Isotopic abundances of Hg in mercury stars inferred from the Hg II line at 3984 A

Wavelengths of the Hg II absorption feature at 3984 A in 30 Hg stars are distributed uniformly from the value for the terrestrial mix to a value that corresponds to nearly pure Hg-204. The wavelengths are correlated loosely with effective temperatures inferred from Q(UBV). Relative isotopic abundances derived from partially resolved profiles of the 3984-A line in iota CrB, chi Lup, and HR 4072 suggest that mass-dependent fractionation has occurred in all three stars. It is supposed that such fractionation occurs in all Hg stars, and a scheme whereby isotopic compositions can be inferred from a comparison of stellar wavelengths and equivalent widths with those calculated for a family of fractionated isotopic mixes. Theoretical profiles calculated for the derived isotopic composition agree well with high-resolution interferometric profiles obtained for three of the stars.

White, R. E.↗
NASA NTRS1977

Particle motion on Mars inferred from the Viking lander cameras

Data from Viking lander cameras indicate fine particle mobility on the surface of Mars such as: probable ventifacts, rock-associated raised streaks, and particulate drifts. Peak wind directions inferred from Chryse and Utopia are roughly equal, and are consistant with those inferred by orbiter photography. A 24 deg systematic offset between: (1) the direction of rock-associated streaks in the Viking I landing site, and (2) Mariner 9 and Viking observations of crater-associated streaks is consistant with a Coriolis acceleration of particles entrained by high-velocity winds during the production of crater-associated streaks. It is suggested that if a large fraction of collision impact energy goes into deformation, strain, and rupture, a preferential destruction of the most easily saltated grains and a depletion of 15-micron diameter grains will be observed. Investigations of particulates dumped on the landing grid suggest that major saltation events took place between sols 96 and 207, caused by winds of greater than 50 meters/sec normalized to the top of the boundary layer.

Sagan, C.↗
NASA NTRS1978

The EUV flux inferred from AE-C He/+/ abundances

The ionization rate of helium in the thermosphere is inferred from He(+) abundances determined by instrumentation on the Atmosphere Explorer-C satellite. The inferred ionization rate is independent of the particular geophysical parameters of the thermosphere for solar zenith angles less than 80 deg and altitudes between 280 and 360 km. The ionization rate is consistent with a value calculated from the EUV flux of Hinteregger (1976) below 504 A.

Oppenheimer, M.↗
NASA NTRS1979

Remote sensing of seasonal distribution of precipitable water vapor over the oceans and inference of boundary layer structure

Over the ocean satellite infrared spectral measurements in the 18 micrometer water vapor band and the 11 micrometer window region were used to derive precipitable water vapor, w, in the atmosphere and the sea surface temperature, SST. Seasonal maps of w on the oceans derived from these data reveal the dynamical influence of the large scale atmospheric circulation. With the help of a model for the vertical distribution of water vapor, the configuration of the atmospheric boundary layer over the oceans can be inferred from w when the information of SST is combined. The gross seasonal mean structure of the boundary layer inferred in this fashion reveals the broad areas of the trade wind inversion and the convectively active areas such as the intertropical convergence zones.

Prabhakara, C.↗
NASA NTRS1979

Plausible inference: A multi-valued logic for problem solving

A new logic is developed which permits continuously variable strength of belief in the truth of assertions. Four inference rules result, with formal logic as a limiting case. Quantification of belief is defined. Propagation of belief to linked assertions results from dependency-based techniques of truth maintenance so that local consistency is achieved or contradiction discovered in problem solving. Rules for combining, confirming, or disconfirming beliefs are given, and several heuristics are suggested that apply to revising already formed beliefs in the light of new evidence. The strength of belief that results in such revisions based on conflicting evidence are a highly subjective phenomenon. Certain quantification rules appear to reflect an orderliness in the subjectivity. Several examples of reasoning by plausible inference are given, including a legal example and one from robot learning. Propagation of belief takes place in directions forbidden in formal logic and this results in conclusions becoming possible for a given set of assertions that are not reachable by formal logic.

Friedman, L.↗