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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Constraining the High-Energy Emission from Gamma-Ray Bursts with Fermi

We examine 288 GRBs detected by the Fermi Gamma-ray Space Telescope's Gamma-ray Burst Monitor (GBM) that fell within the field-of-view of Fermi's Large Area Telescope (LAT) during the first 2.5 years of observations, which showed no evidence for emission above 100 MeV. We report the photon flux upper limits in the 0.1-10 GeV range during the prompt emission phase as well as for fixed 30 s and 100 s integrations starting from the trigger time for each burst. We compare these limits with the fluxes that would be expected from extrapolations of spectral fits presented in the first GBM spectral catalog and infer that roughly half of the GBM-detected bursts either require spectral breaks between the GBM and LAT energy bands or have intrinsically steeper spectra above the peak of the nuF(sub v) spectra (E(sub pk)). In order to distinguish between these two scenarios, we perform joint GBM and LAT spectral fits to the 30 brightest GBM-detected bursts and find that a majority of these bursts are indeed softer above E(sub pk) than would be inferred from fitting the GBM data alone. Approximately 20% of this spectroscopic subsample show statistically significant evidence for a cut-off in their high-energy spectra, which if assumed to be due to gamma gamma attenuation, places limits on the maximum Lorentz factor associated with the relativistic outflow producing this emission. All of these latter bursts have maximum Lorentz factor estimates that are well below the minimum Lorentz factors calculated for LAT-detected GRBs, revealing a wide distribution in the bulk Lorentz factor of GRB outflows and indicating that LAT-detected bursts may represent the high end of this distribution.

Gehrels, Neil↗
NASA NTRS2013

Low Velocity Difference Thermal Shear Layer Mixing Rate Measurements

Current CFD modeling techniques are known to do a poor job of predicting the mixing rate and persistence of slot film flow in co-annular flowing ducts with relatively small velocity differences but large thermal gradients. A co-annular test was devised to empirically determine the mixing rate of slot film flow in a constant area circular duct (D approx. 1ft, L approx. 10ft). The axial rate of wall heat-up is a sensitive measure of the mixing rate of the two flows. The inflow conditions were varied to simulate a variety of conditions characteristic of moderate by-pass ratio engines. A series of air temperature measurements near the duct wall provided a straightforward means to measure the axial temperature distribution and thus infer the mixing rate. This data provides a characterization of the slot film mixing rates encountered in typical jet engine environments. The experimental geometry and entrance conditions, along with the sensitivity of the results as the entrance conditions vary, make this a good test for turbulence models in a regime important to modern air-breathing propulsion research and development.

Bush, Robert H.↗
NASA NTRS2016

Holocene Accumulation and Ice Flow near the West Antarctic Ice Sheet Divide Ice Core Site

The West Antarctic Ice Sheet Divide Core (WDC) provided a high-resolution climate record from near the Ross-Amundsen Divide in Central West Antarctica. In addition, radar-detected internal layers in the vicinity of the WDC site have been dated directly from the ice core to provide spatial variations in the age structure of the region. Using these two data sets together, we first infer a high-resolution Holocene accumulation-rate history from 9.2 thousand years of the ice-core timescale and then confirm that this climate history is consistent with internal layers upstream of the core site. Even though the WDC was drilled only 24 kilometers from the modern ice divide, advection of ice from upstream must be taken into account. We evaluate histories of accumulation rate by using a flowband model to generate internal layers that we compare to observed layers. Results show that the centennially averaged accumulation rate was over 20 percent lower than modern at 9.2 thousand years before present (B.P.), increased by 40 percent from 9.2 to 2.3 thousand years B.P., and decreased by at least 10 percent over the past 2 thousand years B.P. to the modern values; these Holocene accumulation-rate changes in Central West Antarctica are larger than changes inferred from East Antarctic ice-core records. Despite significant changes in accumulation rate, throughout the Holocene the regional accumulation pattern has likely remained similar to today, and the ice-divide position has likely remained on average within 5 kilometers of its modern position. Continent-scale ice-sheet models used for reconstructions of West Antarctic ice volume should incorporate this accumulation history.

ice↗
NASA NTRS2019

Using Chemcam Derived Geochemistry to Identify the Paleonet Sediment Transport Direction and Source Region Characteristics of the Stimson Formation in Gale Crater, Mars

The NASA Curiosity rover has encountered both ancient and modern dune deposits within Gale crater. The modern dunes are actively migrating across the surface within the Bagnold Dune field of which Curiosity conducted analysis campaigns at two different localities. Variations in mafic-felsic mineral abundances between these two sites have been related to the aeolian mineral sorting regime for basaltic environments identified on the Earth which become preferentially enriched in olivine relative to plagioclase feldspar with increasing distance from the source. This aeolian mineral sorting regime for basaltic minerals has also been inferred for Mars from orbital data. The aim of this study is to investigate whether this aeolian mafic-felsic mineral sorting trend has left a geochemical signature in the ancient dune deposits preserved within the Stimson formation. The Stimson formation unconformably overlies the Murray formation and consists of thickly laminated, cross-bedded sandstone. Stimson outcrops have a variable thickness up to 5 meters covering a total area of 17 square kilometers. A dry, aeolian origin was determined for this sandstone due to the high sphericity and roundness of the grains, uniform bimodal grain size distribution (250-710 microns), and 1-meter-thick cross-beds. Identifying the geochemical signature of mineral sorting can provide insights about the paleo-net sediment transport direction of the dunes and prevailing wind direction at the time of deposition.

Bedford, C. C.↗
NASA NTRS2019

Parameters Inference and Model Reduction for the Single-Particle Model of Li Ion Cells

The Single-Particle Model (SPM) of Li ion cell is a computationally efficient model for simulating Li ion cell for weak to moderate currents. The model depends n a number of parameters describing the geometry and material properties of a cell components. In order to apply the model to simulating a cell, the best-fit parametric values have to be inferred from a constant discharge data. We report our efforts to determine the best-fit set for 18650 LP batteries. We found that rather than being best-fit by a particular point in the parametric space the data is fit equally well by an ensemble of points clustering about an effective multidimensional manifold in the parametric space. This property of the SPM is known to be shared by a multitude of the so-called "sloppy models" of complex systems, characterized by a few stiff directions in the parametric space, in which the predicted behavior varies significantly, and a number of sloppy directions in which the behavior doesn't change appreciably. Only the stiff parameters combinations are identifiable. Geometrical features of the BFM give insights to possible reduction of the SPM to a model having fewer sloppy parameters. We have constructed a hierarchy of such models. The fully reduced model depends on only stiff effective parameters which are identifiable and can be used for characterization of the battery's state of health.

Khasin, Michael↗
NASA NTRS2022

Trends in Europe Storm Surge Extremes Match the Rate of Sea-level Rise

Coastal communities across the world are already feeling the disastrous impacts of climate change through variations in extreme sea levels1. These variations reflect the combined effect of sea-level rise and changes in storm surge activity. Understanding the relative importance of these two factors in altering the likelihood of extreme events is crucial to the success of coastal adaptation measures. Existing analyses of tide gauge records agree that sea-level rise has been a considerable driver of trends in sea-level extremes since at least 1960. However, the contribution from changes in storminess remains unclear, owing to the difficulty of inferring this contribution from sparse data and the consequent inconclusive results that have accumulated in the literature. Here we analyse tide gauge observations using spatial Bayesian methods to show that, contrary to current thought, trends in surge extremes and sea-level rise both made comparable contributions to the overall change in extreme sea levels in Europe since 1960 . We determine that the trend pattern of surge extremes reflects the contributions from a dominant north–south dipole associated with internal climate variability and a single-sign positive pattern related to anthropogenic forcing. Our results demonstrate that both external and internal influences can considerably affect the likelihood of surge extremes over periods as long as 60 years, suggesting that the current coastal planning practice of assuming stationary surge extremes might be inadequate.

Francisco M Calafat↗
NASA NTRS2026

Characterizing the subglacial environment of lower Thwaites Glacier using radar modeling

Understanding the spatial heterogeneity beneath Thwaites Glacier, West Antarctica, is vital to projecting its impact on future sea levels. Radar-echo sounding (RES) is commonly used to infer subglacial conditions, but these data can be challenging to interpret. We assess basal heterogeneity across Thwaites Glacier by comparing RES returns to a radar backscattering simulator for over 400 km of RES data. The modeled variations in bed returned power exhibited a strong correlation with actual RES data in 40% of our simulated flight segments, which we consider evidence for a relatively homogeneous glacier bed. Other sites (40%) demonstrated improved fit quality when hydrology or substrate transitions were introduced in the bed material model. The remaining simulated segments (20%) were diagnosed as having more complex basal heterogeneity. The spatial distribution of complex heterogeneity appears to coincide with asymmetric patterns in the RES specularity content, which has been interpreted in previous studies as a signature for channelized hydrology. Conversely, the homogeneous substrate locations coincide with areas of fast-moving ice in western Thwaites. Our simulation method can isolate power variations induced by material heterogeneity vs topography, which is an important limitation of existing RES analysis methods.

Antarctic glaciology↗
DOE OSTI2021

A Comparison of Human Error Probabilities Collected from HuREX and SHEEP Frameworks

This paper discusses how different are the HEPs collected from HuREX and SHEEP frameworks. This study is a preceding research to infer full-scope HRA data based on the data collected from the SHEEP framework. For the comparison, we used HEPs in the HuREX database published in KAERI-TR-6649 [6]. The HEPs have been collected from actual licensed operators when manipulating MCR simulators of Westinghouse type and Optimized Power Reactor (OPR1000) type in South Korea. For the HEPs based on the SHEEP framework, these have been collected from actual licensed operators and students when using a simplified simulator, i.e., Rancor Microworld.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Benefits and Limits of Phasing Alleles for Network Inference of Allopolyploid Complexes

Abstract Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

Evolutionary Biology↗
DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1997

Gaseous abundances and methane supersaturation in Titan's troposphere

Various properties of Titan's troposphere are inferred from an analysis of Voyager I infrared spectrometer (IRIS) data between 200 and 600/ cm. Two homogeneous spectral averages acquired at widely separated emission angles are chosen for the analysis. Both data sets are associated with northern low latitudes very close to that of the radio science ingress occultation point. Solutions require simultaneous nonlinear least-squares fits to the two IRIS data sets, coupled with iteration of the radio occultation refractivity data. Values and associated 1-sigma uncertainties of several parameters are inferred from our analysis. These include mole fractions for molecular hydrogen (approx. 0.0011), argon (small), and methane near the surface ( approx. 0.057). Solutions are also obtained for the hydrogen parafraction (close to equilibrium, with considerable uncertainty), air temperature near the surface ( approx. 93 K), surface surface temperature discontinuity (approx. 1 K), and maximum degree of methane supersaturation in the upper troposphere (approx. 1.5). Actual values for the above-mentioned parameters depend on the amount of ethane cloud near the tropopause. There is no evidence for methane clouds in the upper troposphere, nor is their presence compatible with large degrees of supersaturation. A wave number dependence for the stratospheric haze opacity is inferred similar to that found for a polymeric residue created in laboratory discharge experiments. This haze appears to be uniformly distributed with latitude between altitudes of 40 and 160 km, provided those nighttime data at southern high latitudes that are discounted.

Samuelson, Robert E.↗
NASA NTRS1981

Inference of dust opacities for the 1977 Martian great dust storms from Viking Lander 1 pressure data

The tidal heating components for the dusty Martian atmosphere are computed based on dust optical parameters estimated from Viking Lander imaging data, and used to compute the variation of the tidal surface pressure components at the Viking Lander sites as a function of season and the total vertical extinction optical depth of the atmosphere. An atmospheric tidal model is used which is based on the inviscid, hydrostatic primitive equations linearized about a motionless basic state the temperature of which varies only with height, and the profiles of the tidal forcing components are computed using a delta-Eddington approximation to the radiative transfer equations. Comparison of the model results with the observed variations of surface pressure and overhead dust opacity at the Viking Lander 1 site reveal that the dust opacities and optical parameters derived from imaging data are roughly representative of the global dust haze necessary to reproduce the observed surface pressure amplitudes, with the exception of the model-inferred asymmetry parameter, which is smaller during the onset of a great storm. The observed preferential enhancement of the semidiurnal tide with respect to the diurnal tide during dust storm onset is shown to be due primarily to the elevation of the tidal heating source in a very dusty atmosphere.

Zurek, R. W.↗
NASA NTRS1959

Heat Transfer from Cylinders in Transition From Slip Flow to Free-Molecule Flow

Over 600 measured heat-transfer coefficients in the transition from slip to free-molecule flow have been correlated by using the Nusselt number Nu as a function of the Knudsen Kn and Reynolds Re (or Mach M) numbers. The experimental range for these heat-transfer data from transverse cylinders in air corresponds to the following dimensionless groups: M, 0.10 to 0.90; Re, 0.03 to 11.5; Kn, 0.10 to 5.0. The total air temperature T(sub t) was maintained constant at 80 F, but wire temperature was Varied from 150 to 580 F. At Kn=0.10, Nu extrapolates smoothly into slip-flow empirical curves that show Nu as a function of Re and M or Kn. The correlation gradually changes from the square root of Re(sub t) dependence characteristic of continuum flow to first-power Re dependence as Kn increases (decreasing Re). At the experimental limit Kn ft 5.0, the Nu data correlate with a mean fractional error of 413 percent by the prediction of free-molecule-flow theory. In comparing experimental results with theory, an accommodation coefficient of 0.57+/-0.07 was inferred from the heat-transfer data, which were obtained with etched tungsten wire in air. The wire recovery temperature T(sub e) was measured and compared with existing data and theory in terms of a ratio eta(equivalent to T(sub e)/T(sub t). The results can be divided into three groups by Kn criteria: For Kn less than 2.01, eta is independent of Kn, and eta decreases from 1.0 to 0.97 as M increases from 0 to 0.90; for 2.0 less than Kn less than 5.0, eta is a function of both Kn and M in this transition region to fully developed free-molecule flow; and for Kn greater than 5.0, eta predicted by free-molecule-flow theory is observed and increases from 1.0 to 1.08 as M increases from 0 to 0.90, again independent of Kn. Therefore, these T(sub e) data provide a guide to the boundary of fully developed free-molecule flow, which is.inferred from this research to exist for Kn greater than 5.0. This boundary criterion is substantiated by other published data on T(sub e) at supersonic speeds.

Cybulski, Ronald J.↗
NASA NTRS2001

Consistency Study of Enhanced Shortwave Cloud Absorption Using GEBA Data

Under the support of this project, we have obtained the following results:(1) Shortwave radiative fluxes in current atmospheric general circulation models (GCMs) cannot simultaneously match Earth Radiation Budget Experiment (ERBE) at the top of the atmosphere (TOA) and Global Energy Balance Archive (GEBA) at the surface. This inconsistency of model results with observation is a result of insufficient absorption of solar radiation in the model atmosphere; (2) Current state-of-the art global datasets describing the energy balance of the atmosphere cannot close the atmospheric energy budget if algorithm-derived surface shortwave radiative fluxes are used. The deficient amount of 20 W/sq m is similar to the recently reported enhanced absorption of solar radiation in the atmosphere; (3) We have clarified several sampling problems in the analysis of the collocated monthly GEBA/ERBE data sets which are germane to the interpretation of the clear-sky absorption of shortwave radiation in the atmosphere. As a result, the collocated monthly ERBE/GEBA data can be effectively used to infer enhanced absorption of atmospheric radiation in measurements relative to models, but it cannot be unambiguously used to answer whether the enhanced absorption is in clouds or in clear sky. Other field data are needed to resolve this issue; and (4) Analysis of aircraft measurements during Atmospheric Radiation Measurement Enhanced Shortwave Experiment (ARESE) field campaign supports the enhanced absorption of solar radiation in clouds.

Zhang, Ming-Hua↗
NASA NTRS2019

Uncertainty Quantification for JPL Retrievals

We define the notions of verification, validation, and uncertainty quantification (VVUQ), and place them in the context of retrievals of Earth science data. Uncertainty quantification (UQ) is the process of characterizing the uncertainty of a measurement system relative to the true, physical quantity it measures. UQ is needed when measurements are used for scientific inference, assimilated into models with other data, and used for decision-making and risk analysis. Many measurements come with measures of standard error and bias, but these uncertainty measures are often inaccurate, due to the operational need to assume crisp values for parameters which are in fact uncertain, as well as the failure of technical assumptions needed by operational error-estimation approaches. UQ provides a way to study which assumptions fail to hold, and a tool to improve the measurement. We describe a particular, broadly-applicable UQ setup that has been successful for JPL retrievals including OCO-2, and provide recommendations for furthering the UQ state of the practice at JPL.

Turmon, Michael↗
DOE OSTI2022

Statistical characterization of twin transmission across grain boundaries in magnesium

Here, a detailed statistical analysis of twin transmission (TT) across grain boundaries is performed in rolled, commercial purity magnesium compressed along the rolling direction. EBSD images are acquired from two different cuts: a section containing the rolling (RD) and normal (ND) directions; and a section at 35° to the ND which contains transverse direction (TD). An automated twinning analysis software, METIS, is used to obtain the statistical correlations between deformation twins and other microstructural features by analyzing EBSD microstructures comprising thousands of grains and twins. This detailed statistical analysis reveals that the TT propensity is sensitive to the grain boundary (GB) misorientation angle but not to the GB misorientation axis. Specifically, TT propensity decreases with increasing GB misorientation angle; however, the decreasing trend is not monotonic. Further, the detailed analysis of TT events combining macroscopic Schmid factor and the geometric measures (m’: accounts for the alignment of both twin plane normal and shear directions; m”: accounts for the alignment of only twin shear directions; and m”’: accounts for the alignment of only twin plane normal) helps in understanding and identifying the process of twin-pair formation, i.e., co-nucleation versus transmission, also to identify the role of local stresses at GBs induced by inter-grain interaction versus twinning shear transformation processes. In addition, Bayesian inference is used to draw statistically meaningful conclusions as to the likelihood of transmission given misorientation angles. Lastly, twin chain frequency as a function of chain length is predicted using the probability distribution obtained from the Bayesian inference and compared with the actual data from the EBSD microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The utility of Bayesian data reconciliation for separations

Data reconciliation methods for separation processes typically rely on classical statistical approaches to generate estimates of true mass flow rates from measurements. Knowledge regarding the uncertainty of these estimates has value in decision making, but is often not acquired. Bayesian approaches intrinsically quantify uncertainty; however, literature for Bayesian data reconciliation of separation processes is scarce. This publication outlines two Bayesian data reconciliation models and provides details for how the models were implemented for the BayesMassBal (V 1.0.0) software package written in R. To demonstrate the advantages of this approach for data reconciliation, the models were first applied to simulated data and then compared to a classical model through a Monte Carlo experiment. In this example, the Bayesian models were found to provide more accurate estimates of the simulated data, while also providing quantitative information on the estimate uncertainty. To demonstrate the use of the technique in a practical problem, the models were also applied to real data collected from a pilot-scale rare earth solvent extraction process. Here, this publication provides a small window into how Bayesian methods can be used for data reconciliation, but findings suggest Bayesian data reconciliation models for separation processes have distinct advantages over classical alternatives.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗